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With Internet access shifting from desktop-driven to mobile-driven, application-level mobile traffic identification has become a research hotspot. Although considerable progress has been made in this research field, two obstacles are hindering its further development. Firstly, there is a lack of sharable labeled mobile traffic datasets. Although it is easy to capture mobile traffic, labeling traffic at the application level is non-trivial. Besides, researchers usually hold a conservative attitude toward publishing their datasets for privacy concerns. Secondly, most of the datasets used by existing studies are inadequate to evaluate the proposed methods, since they usually have the problems of inaccurate labels, small scale and simple collection configurations. To tackle these two obstacles, a mobile traffic collection is carried out in this paper. The collected traffic has the advantages of large-scale data size, accurate application-level labels and diverse collection configurations. Then, the collected traffic is anonymized carefully to make it public. Several mobile traffic identification methods are compared based on our anonymized dataset, which proves the applicability of our dataset. [ABSTRACT FROM AUTHOR]

In truncated differential cryptanalysis of symmetric primitives, a generalized framework is to search a distinguisher concerning part of output differences, like truncated differential distribution (TDD) on certain bits (e.g. a nibble) first, and then append several rounds before and after it to recover the secret key. The logarithmic likelihood ratio statistic with respect to the TDD is usually used to distinguish guessed key bits. In this paper, we study how to improve the effect of truncated differential cryptanalysis by considering key schedules of the attacked ciphers. It turns out that for a cipher with a simple key schedule, certain guessed subkey bits may reveal information of the master key, which will help build a stronger TDD distinguisher and reduce the key recovery complexity or attack more rounds. As a result, we explore heuristic techniques to search key-recovery-friendly TDDs and construct automatic search models based on MILP. The refined methods are applied to two recent designs of symmetric primitives, WARP and Orthros, together with peculiarities of their structures as well. For WARP, after making two observations on relations between certain differences with key bits, we propose an algorithm that can find TDDs with low complexities and having potentialities to cover more rounds. Consequently, we launch key recovery attacks on 24 to 27 rounds of WARP. When it comes to Orthros, we present a two-step search algorithm to balance the number of guessed key bits and TDDs, obtaining a key recovery attack on a 7-round variant of it in the weak-key setting. Finally, we perform several verification experiments on round-reduced versions of WARP and Orthros, and the experimental results are consistent with the theoretical distributions and the analysis of generalized key recovery attack framework. [ABSTRACT FROM AUTHOR]

The high energy density of solar energy gives wireless sensor networks advantages in outdoor monitoring applications. However, long-term stable monitoring is challenging due to frequent weather changes, shading by buildings and trees, etc. The existing research usually uses two technologies to solve the above problems: (1) the energy prediction algorithm, and (2) the energy-aware routing strategy. However, in an actual deployment, frequent weather changes can significantly reduce the accuracy of the existing prediction algorithms. When using the algorithms as the support for energy-aware routing, the network lifetime is less than ideal. The existing routing strategies are in need of further improvement. Because of its lack of environmental adaptability, nodes consume energy quickly and have a high mortality rate. Therefore, aiming at the long-term stability of solar wireless sensor networks, this paper proposes a prediction algorithm based on classification and recurrent neural networks, and integrates the shadow judgement method from our previous research to correct the predicted values. Furthermore, we propose a routing optimization model that can flexibly adjust the target according to the solar intensity. The experimental results show that the prediction and routing scheduling algorithm can significantly improve the energy prediction accuracy (30–50%) and prolong the network lifetime (10–42%) in outdoor small sensor scenarios. [ABSTRACT FROM AUTHOR]

Fault-tolerant performance of a network is the prerequisite and guarantee for the normal operation of a network, which is often characterized by connectivity. Let |$H$| denote a connected subgraph of |$G$| and |$H^{*}$| denote the union of the set of all connected subgraphs of |$H$| and the set of the trivial graph. Super |$H$| -connectivity (resp. super |$H^{*}$| -connectivity) satisfies the conditions of both super connectivity and |$H$| -structure connectivity (resp. |$H$| -substructure connectivity). These two kinds of new connectivity provide a new metric to measure the fault-tolerance of the network, that is, the super structure fault-tolerance. The generalized hypercube |$G(m_{r}, m_{r-1},... m_{1})$| is a universal topology of interconnection networks that contains other commonly used topologies and it has been applied in many data center networks because of its excellent qualities. In this paper, we research the super structure fault-tolerance of |$G(m_{r}, m_{r-1},... m_{1})$| by studying super |$H$| -connectivity |$\kappa ^{\prime}(G|H)$| and super |$H^{*}$| -connectivity |$\kappa ^{\prime}(G|H^{*})$| for |$H\in \{K_{1,M},\ C_{3},\ C_{4},\ K_{4}\}$|⁠. [ABSTRACT FROM AUTHOR]

GIFT is a lightweight cipher proposed by Banik et al. at CHES'17, motivated by the design strategy of PRESENT. GIFT-64[2021] is a variant of GIFT proposed by Sun et al. at EUROCRYPT'22 to achieve better resistance against differential attack while maintaining a similar security level against linear attack. At EUROCRYPT'22, Dong et al. proposed a new rectangle framework considering the key guessing strategies for linear key-schedule ciphers, and established a uniform automatic search model for the whole rectangle attack. In this paper, we extend it to be applicable to bit-oriented ciphers, and construct an automatic search model involved in the distinguisher and key-recovery phase for GIFT. Moreover, we utilize the key relations of the linear key-schedule to the model, and find some new distinguishers both for GIFT-64 and GIFT-64[2021]. To evaluate the probability more accurately, we propose a method to calculate the probability of the 2-round middle part which connects the boomerang distinguisher for GIFT , and apply it with the SAT method to evaluate the probability of the whole distinguishers. As a result, we search out a new 20-round related-key boomerang distinguisher for GIFT-64 , and achieve a 26-round attack with better time complexity than the best previous attack. For GIFT-64[2021] , we find a 20-round boomerang distinguisher and give the first 26-round rectangle attack under related-key scenario. [ABSTRACT FROM AUTHOR]

It had always been believed that there was an inherent barrier to Differential Fault Attack (DFA) on the nonce-based authenticated encryption algorithm. At CHES 2016, Saha et al. proposed an Internal Differential Fault Attack on a parallelizable counter-mode algorithm. They induce the attack to classical DFA at the expense of one more fault injection in every encryption process. In this paper, we propose the DFA on HYENA, which is a nonce-based authenticated encryption mode for GIFT-128. Our work is the first pure classical DFA on a nonce-based authenticated encryption algorithm with only one fault injected in every decryption process. Firstly, we give the DFA on GIFT-128 with a fault injected into the 39th-round input. Based on this work, we inject a fault in the underlying GIFT-128 of a HYENA decryption process and make this decryption process still generate the correct tag and output plaintext. This makes the necessary conditions of DFA satisfied. Experiments show that at most 56 key bits of HYENA can be recovered with only a few faulty ciphertexts. In addition, our fault injection is easier to achieve than most other work about fault attack, because the injection location is relatively random and the fault type can be arbitrary. It should be noted that the left 72 key bits cannot be recovered in this way. [ABSTRACT FROM AUTHOR]

Vision Transformers for pre-trained models explore semantic context and spatial relationships for images, which heavily depend on how you select image patches. In this paper, we propose a novel Spatial-aware Multi-directional Patches Multi-cycle Autoencoder (SMPMA) for a pre-trained model that brings the following benefits: (1) Spatial-aware Multi-directional (SM) patches are created with multi-directional spatial locations, transforming a whole image autoencoder problem into a short-span image patches autoencoder problem; (2) SM patches admit a self-cycle autoencoder alignment learning for the first stage and a cross-cycle interaction learning for the second stage, which makes patches align and interact for optimization; (3) SM patches enable to explore local object features and correlation distribution of adjacent pixels by taking arbitrary sampled patches as inputs. Experimental results on four downstream tasks show that our model can achieve state-of-the-art performance over the tasks of image generation, image classification and semantic segmentation. [ABSTRACT FROM AUTHOR]

Recently, 5G has become an important mechanism to serve a growing amount of users in the network, which provides enhanced mobile broadband (eMBB) and ultra-reliable low latency communications (URLLC) services. Meanwhile, Carrier Aggregation (CA) is a promising 5G technology which lets users aggregate fragmented carrier components (CCs) to enhance transmission efficiency. This paper addresses 5G scheduling problem in the CA-enabled eMBB–URLLC coexistence network using genetic algorithm (GA). The key idea is first treating the serving time slot of each user equipment (UE) as a gene and combining all genes to form a chromosome, then generating a set of chromosomes as the initial population and letting the population evolve to iteratively select the fittest one as the resource scheduling decision. Up to now, this is the first work using GA for solving the scheduling problem in the CA-enabled 5G network. Simulation results show that our proposed method can serve the most UEs (⁠|$4\%$|⁠) with the highest throughput (⁠|$7.1\%$|⁠) and lower delay. [ABSTRACT FROM AUTHOR]

The low transaction capacity, high transaction cost and long-term privacy concerns of the current Ethereum platform are notorious. Developers are seeking alternative blockchain platforms to migrate their blockchain-based applications to reduce their applications' use-cost and improve their applications' user experience. The Hyperledger Fabric (HLF) platform with resiliency, flexibility, scalability and confidentiality is preferred for developers to migrate their Ethereum blockchain-based applications. However, it is laborious for developers to migrate blockchain-based applications from the Ethereum platform to the HLF platform. In this paper, we first propose a complete and secure migration solution to ease the migration process. The main idea of our solution is to design a toolbox to help developers automatically eliminate the adverse effects that the differences between Ethereum and HLF may bring to the migrated application. Developers with the toolbox can migrate the application with little time and minimal modification. It is theoretically proved that the migrated application with the toolbox is secure. Besides, a prototype of the toolbox is implemented. The extensive experiments demonstrate that the time for the migration process is acceptable, and the toolbox has little impact on the migrated application's performance. [ABSTRACT FROM AUTHOR]

Dragonfly networks have significant advantages in data exchange due to the small network diameter, low cost and modularization. A graph |$G$| is |$vertex$| - |$pancyclic$| if for any vertex |$u\in V(G)$|⁠ , there exist cycles through |$u$| of every length |$\ell $| with |$3\leq \ell \leq |V(G)|$|⁠. A graph |$G$| is |$Hamiltonian$| - |$connected$| if there exists a Hamiltonian path between any two distinct vertices |$u,v\in V(G)$|⁠. In this paper, we mainly research the pancyclic and Hamiltonian properties of the dragonfly network |$D(n,h)$|⁠ , and find that it is Hamiltonian with |$n\geq 1,\,\,h\geq 2$|⁠ , pancyclic, vertex-pancyclic and Hamiltonian-connected with |$n\geq 4,\,\,h\geq 2$|⁠. [ABSTRACT FROM AUTHOR]

As a key issue of network virtualisation, virtual network embedding (VNE) aims to embed multiple virtual network requests (VNRs) from different applications onto the substrate network effectively. In real networks, about 90% of traffic is generated by different quality of service (QoS) sensitive applications. However, most existing VNE algorithms do not account for the difference. Although several VNE algorithms considered the delay metric of applications, they usually provide strict delay guarantees for all VNRs, leading to a low VNR acceptance ratio. In this paper, we focus on the VNE problem involving multiple QoS metrics and propose a multiple QoS metrics-aware VNE algorithm based on reinforcement learning (RLQ-VNE). We first classify VNRs according to their different requirements for multiple QoS metrics including delay, jitter and packet loss rate, and then introduce reinforcement learning to implement differentiated VNE. Specifically, RLQ-VNE provides strict QoS guarantees for the VNRs with high-level QoS requirements and provides lower QoS guarantees for the VNRs with low-level QoS requirements, thus balancing the QoS guarantee and request acceptance ratio. Simulation results from multiple experimental scenarios show that RLQ-VNE improves the request acceptance ratio and network resource utilisation by sacrificing less QoS. [ABSTRACT FROM AUTHOR]

Commitment scheme is a fundamental cryptographic primitive that serve as building blocks for many other two-party protocols. In this paper, we propose a novel quantum bit commitment scheme, which is secure and does not require quantum memory. Our scheme processes the quantum information using coherent states and unambiguous state discrimination (USD) measurements, which can be experimentally realized by linear optics and photon detectors. We ensure the unconditionally hiding and binding property by preventing both Alice and Bob from obtaining complete information about the commitment in committing stage, and eliminate the requirement for quantum memory by performing USD measurements and phase shifts immediately after receiving the coherent states. [ABSTRACT FROM AUTHOR]

Byzantine ordered consensus , introduced by Zhang et al. (OSDI 2020), is a new consensus primitive that additionally guarantees a correctness specification of transaction order, allowing nodes to assign fairly an ordering indicator to the committed transaction. Zhang et al. also presented a concrete Byzantine ordered consensus protocol called Pompē in the partially synchronous network model. However, Pompē cannot prevent an adversary from manipulating message delivery time. In this paper, we present Chronos, the first Byzantine ordered consensus protocol in the asynchronous network model, where an adversary can arbitrarily manipulate message delivery time. To construct Chronos, we propose a variant of asynchronous common subset called signal asynchronous common subset protocol, which guarantees the liveness of Chronos. We implement both Chronos and its baseline HoneyBadgerBFT using Go language and deploy them on 100 Amazon t3.medium instances distributed throughout 10 regions across the world. The experimental results show that Chronos is more efficient than HoneyBadgerBFT for small network, achieving peak throughput of 59 368 tx/s when the batch size is 100 000 and the number of nodes is 4, while the peak of HoneyBadgerBFT is 57 077 tx/s. [ABSTRACT FROM AUTHOR]

The MinRank problem is an NP-complete problem that is prevalent in multivariate cryptography and its goal is to find a non-zero linear combination of given a series of matrices over a ring such that the obtained matrix has a small rank. At Asiacrypt 2001, two Zero-Knowledge Proofs of Knowledge (ZKPoK) for the MinRank problem are proposed, and we call them MRZK and MRZK |$^{\dagger }$|⁠ , respectively. The latter is an improved version of the proof size of the former. However, the efficiency of MRZK |$^{\dagger }$| has been open and not analyzed. While the MRZK protocol is secure, it must be repeated many times due to the soundness error |$2/3$|⁠ , which leads to the large proof size. For 128-bit security, the MRZK protocol is executed at least 219 iterations and the proof size is about 32 KB. In this paper, we first show that the efficiency of MRZK |$^{\dagger }$| is impractical due to unreasonable parameter size. However, when the parameter size is tuned and the efficiency is improved, an imposter can be efficiently constructed. Then, to alleviate the large proof size of MRZK, inspired by the technique designing ZKPoK (Eurocrypt 2020), we propose a sigma protocol with helper to prove the solution to the MinRank problem. Finally, we transform the sigma protocol with helper into a standard ZKPoK (MRZK |$^{\sharp }$|⁠) by removing the helper. The MRZK |$^{\sharp }$| protocol can achieve any small soundness error and enjoy the proof size of about 15 KB (53% improvement over MRZK). [ABSTRACT FROM AUTHOR]

Fair exchange is a challenging problem for two mutually distrusting players. It is widely known that fair exchange is impossible without a trusted third party (TTP). However, relying on a single TTP can cause a single-point failure. An intuitive idea is to adopt multiple TTPs to distribute trust. This paper constructs a two-party optimistic fair exchange (OFE) protocol using decentralized ciphertext-policy attribute-based encryption (CP-ABE), achieving decentralized TTPs. This is achievable because decentralized CP-ABE ciphertext supports a nested access control policy. A nested access control policy fits perfectly in a fair exchange protocol which contains multiple roles (i.e. players and TTPs). Further, we apply non-interactive zero knowledge proofs to prove the well-formedness of ciphertexts, so as to enforce players to follow the protocol specification honestly. Consequently, we construct an OFE protocol in which each player's operations are publicly verifiable without revealing secret information. Also, we obtain decentralized TTPs with optimism (i.e. the TTPs are involved only when arbitration is required), autonomy (i.e. the TTPs do not need to interact with each other), statelessness (i.e. the TTPs do not need to store data for the exchange protocol) and verifiability (i.e. the TTPs are publicly verifiable). Compared with previous work, our protocol assumes only a public communication channel and each party's operations are publicly verifiable. Besides, it achieves a favorable |$O(n)$| verification complexity in the normal case, where |$n$| is the number of TTPs. Finally, we present a proof-of-concept implementation to demonstrate the feasibility. [ABSTRACT FROM AUTHOR]

Bootle et al. in CRYPTO 2019 proposed a zero knowledge proof for an |$\mathrm{ISIS}_{m,n,q,\beta }$| instance |$A\vec{s} = \vec{u} \bmod q$| with |$\|\vec{s}\|_{\infty }\leq \beta $| (BLS scheme). It was implemented by transforming the instance into the form |$A^{\prime }\vec{s}^{\prime } =\vec{u}\bmod q$|⁠ , where the coefficients of |$\vec{s}^{\prime}$| are in |$\{0,1,2\}$|⁠ , and proved the latter in an exact way. With the concrete parameters |$m=1024,n=2048,\beta =1,q\approx 2^{32}$|⁠ , their proof is of length 384.03KB. In this paper, we decrease the proof size of BLS scheme by two techniques. The first one takes effect on some special parameters. For these parameters, using the binary basic set instead of the ternary one results in a shorter proof. The second one deals with the repetition of the lower half in BLS scheme. Observing that what the lower half proves is of form |$\mathbf{B}\vec{\mathbf{r}}=\vec{\mathbf{t}}$| with a short vector |$\vec{\mathbf{r}}$| of polynomials, a variant of parallel repetition can be used to shorten the proof size. Combining these two techniques together, the proof size of the above-mentioned instance can be reduced to 220.01KB, only 57.3 |$\%$| of BLS scheme. [ABSTRACT FROM AUTHOR]

White-box implementation can ensure the security of cryptographic algorithm in white-box attack environment without changing the inputs and outputs of the original algorithm. Most existing white-box implementations construct a series of lookup tables to protect the key. However, with the development of white-box attack techniques, many white-box implementations have been proved to be insecure. In this paper, a new white-box implementation of SM4 is proposed, which is based on an equivalent partial SPN structure of the SM4 algorithm. Our implementation includes three types of table lookup operations and XOR operations. The round keys are obfuscated with the self-equivalences of the S-box and random affine encodings. Security analysis shows that our implementation can resist BGE-type attack, the attack based on affine equivalence algorithm, the structure attack, the collision attack and differential computational analysis. Furthermore, our scheme requires 8.125 MB of memory. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of resilient vector consensus for a group of dynamic agents against mobile malicious attacks. As real networks often operate under random environment and noises, we approach this problem by considering general random dynamic networks with weighted directed topologies. We propose three types of mobile attack models, which differ in the timing of moving of attackers and the capability of detecting such moving. By employing distributed discrete-time algorithms, the Lyapunov theory and martingale convergence theorem, resilient vector consensus is shown to be reached for all three models when the underlying network satisfies certain stochastic robustness conditions. [ABSTRACT FROM AUTHOR]

A trusted platform module is identified by its endorsement key, while it uses an attestation key to provide attestation services, for example, signing a set of platform configuration registers, providing a timestamp or certifying another of its keys. This paper addresses the problem of how a certificate authority binds the endorsement and attestation keys together. This is necessary for the authority to be able to reliably certify the attestation key. This key binding also enables the authority to revoke the attestation key should the endorsement key be compromised. We study all of the existing solutions and show that they either do not solve the problem or cannot be implemented with a real trusted platform module (or both). We propose a new solution which addresses this problem. We develop a security model for our solution and provide a rigorous security proof under this model. We have also implemented the solution using a real trusted platform module, and our implementation results show that this solution is feasible and efficient. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) systems employing wearable sensors are a promising area of research for tracking human activity. Recently, wearable devices such as smartwatches and sensors have been developed for activity recognition and monitoring. These systems aim to obtain the subject's state within his or her environment by exploiting heterogeneous sensors attached to the body. With the development of deep learning, new strategies have emerged to facilitate and solve the HAR problems. In this work, a deep multilayer bidirectional long-short memory (Bi-LSTM) architecture has been implemented to detect human activities. Instead of training a single model as in traditional LSTM methods, two models are presented in the Bi-LSTM scheme, one for learning the input data sequence and the other for learning the reverse sequence. Finally, a new novel postprocessing approach has been proposed based on windowing and voting in the last step to improve the average F1 score. Comprehensive investigations on the three publicly available datasets consisting of a different set of activities were used to evaluate the performance of the proposed framework. The empirical results of this paper on AReM, Mhealth and PAMAP2 datasets attained 95.46, 95.79 and 93.41% average F1 score, respectively. The results also revealed that selecting the window size and implementing the appropriate voting method has a significant effect on improving the average percentage of the F1 score. [ABSTRACT FROM AUTHOR]

Named Data Networking (NDN) has gained importance in today's era due to a paradigm shift in the Internet usage pattern which revolves around the content rather than the respective host addresses. Three important data structures in NDN are Content Store (CS), Pending Interest Table (PIT) and Forwarding Information Base (FIB). The search time of PIT is quite high since its size grows with the addition of new content names, and the Interest packets which are not served by CS are searched in millions of existing entries in the PIT. Hence, lookup time can be improved if, instead of checking all the available entries, initial scanning is done to determine whether the required content name exists in the PIT or not. In this paper, we propose a Stable Bloom Filter (SBF) based PIT called S-PIT, to minimize the PIT search time by identifying the existence of query content through SBF. The various experiments performed show that S-PIT outperforms existing data structures in terms of memory consumption, content insertion time, average search time and false positive rate. [ABSTRACT FROM AUTHOR]

A '1-out-of-n' oblivious transfer (OT) protocol involves two participants: a sender, who provides a set of items as input, and a receiver. The protocol guarantees that the receiver gets exactly one of the items of the input set, while the sender is unable to determine which. Priced OT (POT) protocols further allow each item of the input set to be assigned a price in such a way that, after a proper execution of the protocol, the receiver gets the requested item if and only if the corresponding sum of money has been paid. In this paper, we present a construction which takes a '1-out-of-n' OT protocol and transform it into a POT one. Moreover, the resulting system is unlinkable in the sense that the sender is unable to determine whether two executions of the protocol were run by the same receiver or not. When compared to existing unlinkable POT protocols, our construction offers a lower conceptual complexity as it does not involve the use of zero-knowledge proofs. [ABSTRACT FROM AUTHOR]

This paper proposes a general construction method for structural distinguishers, based on the recently proposed 'multiple-of- n property' of a subspace trail. The multiple-of- n property refers to the fact that by appropriate choices of difference for a number of input pairs, it is possible to make sure that the number of times that the difference of the resulting output pairs lie in a particular subspace is always a multiple of n. We weaken the condition for constructing the structural distinguisher from the exact subspace trail to the general subspace trail and this general condition is applied to the SPN cryptographic algorithm, allowing a six-round structural distinguisher to be obtained for the first time. In this way, the best six-round Midori-64 distinguisher is constructed, with its data complexity being identical to Midori's five-round structural distinguisher. We also applied this new condition to the SKINNY algorithm, extending its structural distinguisher from five to six rounds. [ABSTRACT FROM AUTHOR]

Off-chain payment channel network is an effective solution to deal with low throughput and high load on the blockchain. However, the quality of the routing scheme will directly affect the performance of the off-chain network system. Existing routing schemes cannot select an appropriate transaction path according to the actual needs of user nodes. And they may also lead to network congestion, channel imbalance and node centralization. This paper proposes a new balanced routing selection scheme based on weight calculation called BRBW, which builds a weight model by Analytic Hierarchy Process. BRBW comprehensively considers the channel capacity, handling fee and path length to improve transaction success rate, reduce channel congestion and maintain the long-term sustainability of the payment channel. It uses a modified maximum flow algorithm to find paths with sufficient capacity and selects the transaction path for a large payment through linear programming. Finally, we do some experiments and compare with state-of-the-art approaches in the test environment. Simulation results show that under the same network environment, the payment success rate of the BRBW is above 75%, and the channel utilization rate is about 10% higher than other schemes. [ABSTRACT FROM AUTHOR]

SipHash is a family of ARX-based MAC algorithms optimized for short inputs. So far, a lot of implementations and applications for SipHash have been proposed, whereas the cryptanalysis of SipHash still lags behind. In this paper, we study the property of truncated differential in reduced-round SipHash. By exhaustively testing all kinds of 1-bit input differences, we find out the greatest differential biases from corresponding output bits through 3 or 4 SipRounds. Making use of these results, we construct distinguishers for SipHash-2-1 and SipHash-2-2 with practical complexities of |$2^{12}$| and |$2^{36}$|⁠ , respectively. However, one limitation of the latter is that it begins with 1-bit input differences on the most significant message bit, which means it can only work when neglecting the padding rules of SipHash. Furthermore, we reveal the relations between the value of output bias and the difference after the first modular addition step, which is directly determined by corresponding key bits. Based on these relations, we propose a key recovery method for SipHash-2-1 that can obtain a significantly nonuniform distribution of the 128-bit secret key. It is summarized that about |$97\%$| of random keys can be fully recovered under this method within a complexity of |$2^{83}$|⁠. [ABSTRACT FROM AUTHOR]

With the development of information networks, the entities from different network domains interact with each other more and more frequently. Therefore, identity management and authentication are essential in cross-domain setting. The traditional Public Key Infrastructure (PKI) architecture has some problems, including single point of failure, inefficient certificate revocation status management and also lack of privacy protection, which cannot meet the demand of cross-domain identity authentication. Blockchain is suitable for multi-participant collaboration in multi-trust domain scenarios. In this paper, a cross-domain certificate management scheme CD-BCM based on the consortium blockchain is proposed. For the issue of Certificate Authority's single point of failure, we design a multi-signature algorithm. In addition, we propose a unified structure for batch certificates verification and conversion, which improve the efficiency of erroneous certificate identification. Finally, by comparing with current related schemes, our scheme achieves good functionality and scalability in the scenario of cross-domain certificate management. [ABSTRACT FROM AUTHOR]

Cyber Range is an experimental platform based on virtualization technology to construct a controlled simulation environment, providing a real-world simulation environment for cybersecurity personnel to conduct various practical exercises. The problem is that generating a virtual environment satisfying the requirements is labor-intensive and time-consuming. To resolve the above problem, this paper proposes a system to automate the deployment of a cyber range. In our method, the first step is to collect virtual machines (VMs) related to cybersecurity and extract relevant features. Then, machine learning is used to classify VMs to reduce the cost of manual VMs selection. Lastly, leveraging the popular OpenStack cloud platform as the deployment platform enhances the applicability of the cyber range. When it comes time to deploy a virtual environment, the instructor only needs to provide some brief description information of a virtual environment. Then, the system will automatically parse the description file to complete the automated deployment of the virtual environment. This system has been successfully applied to the cyber range of Sichuan University for daily teaching tasks. [ABSTRACT FROM AUTHOR]

With rapid development of next-generation mobile networks and communications (5G networks), group-oriented applications in resource-constrained smart environments (RSEs), such as smart homes and smart classrooms, have attracted great attentions. Due to the insecure communications between resource-constrained devices, secure group communications in RSE toward 5G face many challenges. In RSE toward 5G, lightweight communications and low computational overheads are crucial. Besides, the private tokens used to generate the group key are expected to be reused multiple times. However, the conventional frameworks for secure group communications cannot meet these requirements. A practical construction of extremely lightweight constant-round membership authenticated group key establishment framework is proposed in this paper for RSE toward 5G, which not only implements identity authentication among the members and group key establishment but also ensures extremely lightweight computation and communication costs by each group member. In our proposed scheme, the increase in the number of group members will not lead to a linear or logarithmic increase in the communication and calculation costs at the member side. Our framework also resists external and internal attacks and meets all the desirable security features. In this framework, the privacy of tokens can be well protected, so that they can be reused for multiple times. Therefore, our scheme significantly reduces the costs of communication and calculation, and it is more efficient compared with the related schemes in the literature. This proposal is fairly suitable for lightweight membership authentication and group key establishment in RSE toward 5G. [ABSTRACT FROM AUTHOR]

This paper completes analysis of the worst-case running time of |$ {\tt{Heapsort}}$| measured by the number of comparisons of keys performed while sorting. A derivation of an exact formula for the maximum number of comparisons of keys performed by |$ {\tt{Heapsort}} $| on any array of size |$ N \geq 2 $| is presented. It is equal to $$ \begin{align}& \nonumber 2(N-1)\lceil \lg N \rceil - 2 ^{\lceil \lg N \rceil +1} - 2 s_2(N) - e_2(N) + \min (\lceil \lg N \rceil, 3) + 5 + c, \end{align} $$ where |$s_2(N)$| is the sum of digits of the binary representation of |$N$|⁠ , |$e_2(N)$| is the exponent of |$2$| in the |$N$| 's prime factorization and |$ c $| is a binary function on the set of positive integers defined by $$ \begin{align}& \nonumber c = \left\{ \begin{array}{@{}ll} 1 \text{ if} \; N \leq 2 ^{\lceil \lg N \rceil} - 4 \\[4pt] 0 \text{ otherwise}. \end{array} \right. \end{align} $$ The above formula allows for deciding, in |$ O(N \log N) $| time, if any given |$ N $| -element array is a worst-case array for |$ {\tt{Heapsort}} $|⁠. Its proof yields an algorithm for construction, in |$ O(N \log N) $| time, of worst-case arrays of arbitrary sizes |$ N \geq 2 $| for |$ {\tt{Heapsort}} $|⁠. [ABSTRACT FROM AUTHOR]

In this study, we construct a smoothing term structure, which is an essential part of the energy function in binocular matching. However, the existing energy models are mainly deterministic, which cannot adapt to processing low-quality images, especially when there exists a large proportion of vague areas. In order to perform better in processing these low-quality images, in this paper, we construct the smoothing term based on a fuzzy model, which includes fuzzy segmentation, the fuzzy network between the superpixels and the fuzzy relationship between the pixels. These can be compatible with the uncertainty in the image. In addition, to explain the rationality of the calculation of the degree of correlation between superpixels and further elaborate on the property of these degrees between each superpixel, we propose five corresponding theorems with proofs. After we solve the energy model combined with our proposed smoothing term, we compare our disparity results with the corresponding deterministic model and several state-of-the-art algorithms in the experiment. The results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper aims to improve the overall task processing time of mobile real-time Internet of Things (IoT) applications in fog-cloud computing, considering the various resource and security requirements along with the time constraints of the task. Fog computing extends the cloud resources to serve the IoT devices at the network edge. In such a scenario, deciding whether the tasks should be processed at the fog layer or submitted to the cloud is critical. Moreover, for real-time applications, the mobility of IoT devices and the limited bandwidth available at the edge devices endanger the low processing time of the task. Besides, the security demands of some IoT applications (i.e. healthcare) require processing the tasks by specific fog or cloud servers to assure confidentiality of information, which may also delay the task processing time. Therefore, we first address the mobility issue by proposing three different algorithms that work on allocating the mobile IoT device to the appropriate edge device (i.e. fog gateway), considering the distance and bandwidth load factors. Then, we offer a novel task scheduling algorithm that uses fuzzy logic to optimize the distribution of tasks between the fog and cloud layers, considering the task security requirements. The algorithm selects the proper processing unit to execute the task in the fog layer by exploiting the task demands (i.e. computation, storage, bandwidth, security) and deadline. Results demonstrate that considering the factors of distance and available bandwidth load while allocating the IoT device to the fog gateway improves the task processing time better than adopting one aspect. Results also show that our proposed scheduling algorithm outperforms other existing algorithms regarding makespan, turnaround time, success ratio and processing time metrics. [ABSTRACT FROM AUTHOR]

Maximizing influences in complex networks is a practically important but computationally challenging task for social network analysis, due to its nondeterministic polynomial time (NP)-hard nature. Most current approximation or heuristic methods either require tremendous human design efforts or achieve unsatisfying balances between effectiveness and efficiency. Recent machine learning attempts only focus on speed but lack performance enhancement. In this paper, different from previous attempts, we propose an effective deep reinforcement learning model that achieves superior performances over traditional best influence maximization algorithms. Specifically, we design an end-to-end learning framework that combines graph neural network as the encoder and reinforcement learning as the decoder , named DREIM. Through extensive training on small synthetic graphs, DREIM outperforms the state-of-the-art baseline methods on very large synthetic and real-world networks on solution quality, and we also empirically show its linear scalability with regard to the network size, which demonstrates its superiority in solving this problem. [ABSTRACT FROM AUTHOR]

The lattice-based cryptographic accumulators, which enable short zero-knowledge arguments of membership, have numerous applications in post-quantum privacy-preserving protocols. However, most efficient quantum-safe zero-knowledge arguments are PCP-based systems and rely on non-falsifiable assumptions. For non-PCP-based constructions using the state-of-the-art techniques on compressing lattice-based zero-knowledge proofs, the concrete size of the resulting proof for accumulators with |$2^{32}$| members is at least 500 KB. In this paper, we propose a compact non-PCP zero-knowledge proof for the lattice-based Merkle-tree, which leads to an efficient post-quantum cryptographic accumulator. The complexity of our construction is logarithmic in |$l\cdot n_{s}$|⁠ , where |$l$| and |$n_{s}$| denote the depth of the underlying Merkle-tree and the size of a node, respectively, and the concrete size is only |$143.7\ $| KB when |$l=32$|⁠. In particular, we provide an improved lattice-based Bulletproof with efficient knowledge extraction, which allows large challenge space but small soundness slack. Furthermore, the amortized technique can be applied to the Bulletproof without breaking the knowledge soundness due to our improved knowledge extraction. As a direct application, we present a practical lattice-based ring signature, which can achieve logarithmical signing/verifying computational complexity with the number of the ring, while the state-of-the-art constructions (CRYPTO 21) have linear computational complexity. [ABSTRACT FROM AUTHOR]

Reliability evaluation of multiprocessor systems is of significant importance in the design and maintenance of multiprocessor systems. Based on edge-connectivity, more refined quantitative indicators for the reliability of multiprocessor systems have been introduced. The extra edge-connectivity and the component edge-connectivity, as two important parameters to evaluate the robustness of multiprocessor systems, are explored extensively. In this paper, we determine the |$h$| -extra edge-connectivity and the |$(g+1)$| -component edge-connectivity of the balanced complete |$t$| -partite graph |$K_{r}^{t}$| for |$t, r\geq 2$|⁠ , where |$1\leq h \leq \lfloor tr/2 \rfloor$| and |$2\leq g \leq tr-1$|⁠. [ABSTRACT FROM AUTHOR]

Whether a block cipher can resist impossible differential attack is an important basis to evaluate the security of a block cipher. However, the length of impossible differentials is important for the security evaluation of block ciphers. Most of the previous studies are based on structural cryptanalysis to find the impossible differential, and the structural cryptanalysis covers a lot of specific cryptanalytic vectors which are independent of the nonlinear S-boxes. In this paper, we study the maximum length of the impossible differential of an Advanced Encryption Standard-like cipher in the setting with the details of S-boxes. Inspired by the 'Divide-and-Conquer' technique, we propose a new technique called Reduced Block , which combines the details of the S-box. With this tool, the maximum length of impossible differentials can be proven under reasonable assumptions. As applications, we use this tool on uBlock and Midori. Consequently, we prove that for uBlock-128, uBlock-256 and Midori-64, there are no impossible five-round, six-round and seven-round differentials with one active input nibble and one active output nibble, even when considering the details of S-boxes. Furthermore, we reveal some properties of the uBlock S-box and linear layer and demonstrate theoretically that there are no impossible differentials longer than four rounds for uBlock-128 under the assumption that the round keys are independent and uniformly random. This study might provide some insight into the bounds of the length of impossible differentials. [ABSTRACT FROM AUTHOR]

Falling is one of the major health concerns, and its early detection is very important. The goal of this study is an early prediction of impending falls using wearable sensors data. The SisFall data set has been used along with two deep learning models (CNN and a combination model named Conv_Lstm). Also, a dynamic sampling method is offered to improve the accuracy of the models by increasing the equilibrium rate between the samples of the majority and minority classes. To fulfill the main idea of this paper, we present a future prediction strategy. Then, by defining a time variable 'T', the system replaces and labels the state of the next T s instead of considering the current state only. This leads to predicting falling states at the beginning moments of balance disturbance. The results of the experiments show that the Conv_Lstm model was able to predict the fall in 78% of cases and an average of 340 ms before the accident. Also, for the Sensitivity criterion, a value of 95.18% has been obtained. A post-processing module based on the median filter was implemented, which could increase the accuracy of predictions to 95%. [ABSTRACT FROM AUTHOR]

Cloud medical treatment provides real-time data sharing in a cost-effective method, making it more practical to create, collect and manage vast amounts of personal health records (PHR) of patients. However, health information is considered highly sensitive. How to securely store and dynamically process massive patients' PHR data in a public cloud environment has become one of the most important challenges. Therefore, we introduce a novel solution to the problems of privacy exposure, data security and flexible access of storage modules in medical systems. In this paper, we present a privacy-preserving certificateless broadcast encryption with authorization for the PHR system, which is the best approach to effectively solve the above problems and avoid key escrow. In our work, users (patients) outsource their encrypted data to the cloud server and reallocate data accessing rights of recipients through an authorization set, sharing with a group of authorized receivers (doctors) in a secure and efficient manner. In addition, it is shown to be capable of achieving both plaintext confidentiality and receiver anonymity under the random oracle model. Moreover, the experimental evaluation shows that the proposed scheme enjoys low computational and communication overhead, indicating the feasibility and practicality of the scheme. [ABSTRACT FROM AUTHOR]

Graph products have been extensively applied to model complex networks with striking properties observed in real-world complex systems. In this paper, we study the hitting times for random walks on a class of graphs generated iteratively by edge corona product. We first derive recursive solutions to the eigenvalues and eigenvectors of the normalized adjacency matrix associated with the graphs. Based on these results, we further obtain interesting quantities about hitting times of random walks, providing iterative formulas for two-node hitting time, as well as closed-form expressions for the Kemeny's constant defined as a weighted average of hitting times over all node pairs, as well as the arithmetic mean of hitting times of all pairs of nodes. [ABSTRACT FROM AUTHOR]

In CRYPTO 2019, Gohr made a pioneering attempt and successfully applied deep learning to the differential cryptanalysis against NSA block cipher Speck 32/64, achieving higher accuracy than the pure differential distinguishers. By its very nature, mining effective features in data plays a crucial role in data-driven deep learning. In this paper, in addition to considering the integrity of the information from the training data of the ciphertext pair, domain knowledge about the structure of differential cryptanalysis is also considered into the training process of deep learning to improve the performance. Meanwhile, taking the performance of the differential-neural distinguisher of Simon 32/64 as an entry point, we investigate the impact of input difference on the performance of the hybrid distinguishers to choose the proper input difference. Eventually, we improve the accuracy of the neural distinguishers of Simon 32/64, Simon 64/128, Simeck 32/64 and Simeck 64/128. We also obtain related-key differential-based neural distinguishers on round-reduced versions of Simon 32/64, Simon 64/128, Simeck 32/64 and Simeck 64/128 for the first time. [ABSTRACT FROM AUTHOR]

The undirected graph, exchanged hypercube |$EH(s,t)$|⁠ , is a variant of hypercube proposed by Loh et al. It is obtained by removing some links from |$(s+t+1)$| -dimensional hypercube. It retains many excellent properties, so many people have studied its reliability and fault tolerance. In this paper, combining the structure connectivity and substructure connectivity of graphs proposed not long ago, we obtain its |$P_k$| -path, |$C_{2l}$| -cycle and |$K_{1,r}$| -star structure connectivity and substructure connectivity where |$2\le k,r\le s-1\le t-1$| and |$6\le 2l\le s-1\le t-1$|⁠ ; we also establish |$\kappa ^s(EH(s,t);C_4)$| for |$5\le s\le t$| and the upper bound of |$\kappa (EH(s,t);C_4)$| for |$4\le s\le t$|⁠. [ABSTRACT FROM AUTHOR]

Deep neural network model extraction attack is the process of retraining a surrogate model based on the outputs of a target model with a given set of inputs. Such attacks are hard to defend for the sake of model owners' interest. Recently, some work propose model watermarking scheme for image processing networks, which is able to prove the intellectual property of deep models even after the model extraction attack. This scheme makes sure that, once the target model (an image processing network) is watermarked, we can extract the watermark from the output of the surrogate model. In this paper, we propose a new model extraction attack scheme to fight against the latest method. Instead of directly using the output images of a target model, we propose to use their reconstructed versions for model retraining, where an asymmetrical UNet is proposed for image reconstruction. To thoroughly remove the watermarking traces, we propose and incorporate a referenced subspace attention module in the asymmetrical UNet, which removes the watermark by projecting the outputs of the target model into the subspaces of the reference image. Various experiments demonstrate the effectiveness of our attack. [ABSTRACT FROM AUTHOR]

Pattern detection for revealing the patterns of users' behavior is an important analysis-assisting tool toward the understanding and prediction of their attitudes, manners, activities and habits. In this paper, two novel query operators applied to transactional data are introduced to ease the query processing, strengthening query capabilities and revealing valuable patterns for data analysis and mining. The operators are named as PeriodicTransactions and SimilarTransactions, and as their names imply, they measure periodicity and similarity, respectively, in a set of transactions. The operators are formally defined and the corresponding algorithms are also provided. To show the expediency of the operators, the proposed algorithms are implemented and a set of experiments were conducted with real data from the Ethereum blockchain. The results show the feasibility and usefulness of the proposal for identifying these patterns that help to understand user behavior and reveal a rich interaction between senders and recipients, where periodic and similar transactions occur. [ABSTRACT FROM AUTHOR]

Continuous data scale growth increases energy consumption and operating cost that cannot be ignored in cloud storage systems. Previous studies have shown that analyzing the characteristics of I/O access and mining data features is effective for reasonable data distribution in storage systems. The granularity and criterion of classification are the key factors in determining the data distribution. To decrease energy consumption and operating cost, this paper puts forward a fine-grained framework of the climatic-season-based energy-aware in cloud storage system called CSEA. The framework concludes the following three aspects: (i) data feature mining. CSEA discovers potential data features by analyzing data access to provide help with data classification. (ii) K-means clustering algorithm. CSEA uses an unsupervised data classification algorithm in machine learning to divide data into categories based on seasonal characteristics by gathering real I/O access. (iii) data distribution of fine-grained. On the basis of seasonal features, CSEA fuses regional features to further refine the data distribution granularity to save on energy consumption and operating cost. Simulation experiments using extended CloudSimDisk and the constructed mathematical models indicate that CSEA reduces the energy consumption and operating cost compared with the single data classification standard and coarse-grained data distribution. [ABSTRACT FROM AUTHOR]

Copy-move is a common image forgery operation, which copies and moves a block of an image from one position to another place. Image hashing refers to extracting a unique number sequence from the image by using various image features. In practical application, image hashing is used to replace the image itself, which effectively reduces the cost of image storage and computational complexity. In this paper, we propose a novel image hash extraction scheme: constructing image hashing by combining local feature based on Canny operator and global feature based on tensor. In addition, instead of using the traditional correlation coefficient or Hamming distance, a novel method is proposed to calculate the hash distances. A large number of experiments have proved that our image hashing can achieve a better balance between robustness and discrimination with a shorter hash length. What's more, we can directly locate the forgery areas from the hashing for copy-move forged images. [ABSTRACT FROM AUTHOR]

The emergent technology has been increasingly incorporated with the tourism industry. In the digital world, social media plays an important role in identifying the most visited tourist places. More than 80% of people commonly used the social media platform called 'Twitter'. By using tweet reviews, tourists around the world know about the feelings, suggestions and opinions of most visited tourist places in the selected region. This paper recommends an approach for the development of trip planning in South India based on tourist's preferences. For the research, we use Twitter Application Programming Interface to collect information about the most visited tourist places in South India by using tweets reviews and store it as South India Tourism Tweet reviews database. This paper mainly focuses on analyzing Twitter reviews, which are very helpful to locate the most visited tourist place. In the tweets where reviews are mostly unstructured and heterogeneous, which are then classified into positive tweets, negative tweets and neutral tweets by using the preprocessing technique and machine learning algorithm called support vector machine. Performance measures are calculated by using the classification results. Finally, the results are plotted by using the classification results and performance measures as a wordcloud. This can be very useful for tourists to select the most visited tourist locations. The proposed system can satisfy individual tourist needs. [ABSTRACT FROM AUTHOR]

Parameter servers (PSs) placement is one of the most important factors for global model training on distributed deep learning. This paper formulates a novel problem for placement strategy of PSs in the dynamic available storage capacity, with the objective of minimizing the training time of the distributed deep learning under the constraints of storage capacity and the number of local PSs. Then, we provide the proof for the NP-hardness of the proposed problem. The whole training epochs are divided into two parts, i.e. the first epoch and the other epochs. For the first epoch, an approximation algorithm and a rounding algorithm are proposed in this paper, to solve the proposed problem. For the other epochs, an adjustment algorithm is proposed, by continuously adjusting the decisions for placement strategy of PSs to decrease the training time of the global model. Simulation results show that the proposed approximation algorithm and rounding algorithm perform better than existing works for all cases, in terms of the training time of global model. Meanwhile, the training time of global model for the proposed approximation algorithm is very close to that for optimal solution generated by the brute-force approach for all cases. Besides, the integrated algorithm outperforms the existing works when the available storage capacity varies during the training. [ABSTRACT FROM AUTHOR]

A number of text compression algorithms have been proposed in the past decades, which have been very effective and usually operate on conventional character/word-based approaches. A novel data compression perspective of data mining is explored in this research and the paper focuses on novel frequent sequence/pattern mining approach to text compression. This work attempts to make use of longer-range correlations between words in languages for achieving better text compression. We propose a novel and efficient method by making the compression of any word-level text in a universal manner for corpora across domains referred as Universal Huffman Tree-based encoding. The major contribution of this work is in terms of avoidance of code table communication to the decoder. Simulation results over benchmark datasets indicate that Universal Huffman encoding employing frequent sequence mining (achieves [20%, 89%] improvement in compression in reduced time. The paper also contributes a usable interface for Data compression that employs the proposed frequent sequent mining-based data compression algorithm. The interface supports features such as feedback, consistency, usability, navigation, visual appeal, performance and accessibility on par with existing compression softwares. The work results in an intelligent data compression software employing knowledge engineering perspective. [ABSTRACT FROM AUTHOR]

The article focuses on the specifications and constraints of minimum message length (MML) and Kolmogorov complexity. The research conducted by C. S. Wallace and D. L. Dowe focused on the principles of MML within the theory of algorithmic complexity. They also compared MML with minimum description length (MDL) principle.

The Block Tree is a data structure for representing repetitive sequences in compressed space, which reaches space comparable with that of Lempel–Ziv compression while retaining fast direct access to any position in the sequence. In this paper, we generalize Block Trees to two dimensions, in order to exploit repetitive patterns in the representation of images, matrices and other kinds of bidimensional data. We demonstrate the practicality of the two-dimensional Block Trees (2D-BTs) in representing the adjacency matrices of Web graphs, and raster images in GIS applications. For this purpose, we integrate our 2D-BT with the |$k^2$| -tree—an efficient structure that exploits clustering and sparseness to compress adjacency matrices—so that it also exploits repetitive patterns. Our experiments show that this structure uses 60–80% of the space of the original |$k^2$| -tree, while being 30% faster to three times slower when accessing cells. [ABSTRACT FROM AUTHOR]

In this paper, initially, message intensities are used to create two encoding tables: an index and a code sequence. The table is then updated with |$n$| secret data encoded into it. If the code sequence of the second encoding table matches the code sequence of the preceding or succeeding half of the two encoding tables, the second encoding table is folded. The folding is done when maximum intensity occurs in the utmost succeeding part of the two encoding tables. Finally, using the combination orientation approach, the encoded indices are embedded in the stego images. In the extraction phase, using the two stego images and the encoding with the combination orientation method, the encoded indices are obtained. The decoding is performed to extract the secret message as well as the cover image and there is no occurrence of overflow and underflow problems. It has a high visual quality and a high embedding capacity. The proposed technique achieved a maximum |$\mathrm{PSNR}$| of |$49.49$| dB and a |$648\ 242$| -bit embedding capacity. When compared to traditional techniques, the experimental results show that the proposed method outperforms them. [ABSTRACT FROM AUTHOR]