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Robust face clustering enjoys a wide range of applications for gate passes, surveillance systems and security analysis in embedded sensors. Nevertheless, existing algorithms have limitations in finding accurate clusters when data contain noise (e.g., occluded face clustering and recognition). It is known that in subspace clustering, the ℓ 1 - and ℓ 2 -norm regularizers can improve subspace preservation and connectivity, respectively, and the elastic net regularizer (i.e., the mixture of the ℓ 1 - and ℓ 2 -norms) provides a balance between the two properties. However, existing deterministic methods have high per iteration computational complexities, making them inapplicable to large-scale problems. To address this issue, this paper proposes the first accelerated stochastic variance reduction gradient (RASVRG) algorithm for robust subspace clustering. We also introduce a new momentum acceleration technique for the RASVRG algorithm. As a result of the involvement of this momentum, the RASVRG algorithm achieves both the best oracle complexity and the fastest convergence rate, and it reaches higher efficiency in practice for both strongly convex and not strongly convex models. Various experimental results show that the RASVRG algorithm outperformed existing state-of-the-art methods with elastic net and ℓ 1 -norm regularizers in terms of accuracy in most cases. As demonstrated on real-world face datasets with different manually added levels of pixel corruption and occlusion situations, the RASVRG algorithm achieved much better performance in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet technology and its application, the existence of network vulnerabilities is very common. Attackers may use the defects of software, hardware, or system security policy in the network system to access or destroy the system without authorization. How to nip in the bud and carry out a safety risk assessment and early warning is an urgent problem to be solved. Based on the overall assessment of the risk factors in the whole network, the more dangerous nodes are found and priority measures are taken. The method proposed in this paper can reflect and predict the actions of attackers, repair, and adjust the previously predicted probability. It is compared with the method that evaluates the uncertainty in the network solely by calculating the static probability. The proposed new ideas and methods better reflect the real-time changes in the actual environment of the Internet, thereby better responding to the actual situation. This method can be well applied to threat detection, threat analysis, and risk assessment of monitoring system networks, enabling monitoring network managers to evaluate and protect the security of real-time power grids. It is of great significance to effectively defend against network attacks, ensure system security, and study the resistance of control systems under network attacks. [ABSTRACT FROM AUTHOR]

The immutability of traditional blockchain limits the development of blockchain technology, research on modifiable blockchain is still in its infancy, many schemes still have many deficiencies in system efficiency and security. To deal with these problems, firstly, this paper proposes an improved blockchain data structure to make it suitable for modifiable functions; Secondly, a master private key synthesis algorithm is designed using homomorphic encryption technology to ensure the security of the private key used for data modification on the chain during the synthesis process; At the same time, a matching mechanism is designed to reduce the communication amount in the private key synthesis process; Finally, an auditing method for modification requests is designed to ensure the rationality and security of auditing modification requests. The experimental results show that the scheme in this paper not only has the characteristics of decentralization, but also improves the system security and communication efficiency compared with other schemes. [ABSTRACT FROM AUTHOR]

Aiming at the problems in the practical Byzantine fault tolerance（PBFT） consensus algorithm, such as large communication overhead, lack of reward and punishment mechanism and lack of enthusiasm of nodes, this paper proposed an improved PBFT consensus algorithm based on the node grouping reputation model（GR-PBFT）. Firstly, GR-PBFT algorithm introduced a reputation reward and punishment mechanism to ensure the security of the system, and then grouped by nodes reputation to select consensus nodes so that it could solve the reputation accumulation problem of nodes generated by consensus algorithms such as reputation mechanisms. Besides, it could also reduce the degree of system centralization and improve the enthusiasm of nodes to become consensus nodes. Then, this algorithm improved the election method of the master node to ensure the reliability of the master node. Meanwhile, it optimized the execution process of the consensus protocol and reduced the communication complexity in the preparation, confirmation and response stage to improve the consensus efficiency. Finally, the simulation experiments show that the GR-PBFT consensus algorithm has better performance than the PBFT consensus algorithm in terms of consensus delay, communication overhead, throughput and security and so on. [ABSTRACT FROM AUTHOR]

To improve the blockchain consensus algorithm practical Byzantine fault tolerance (PBFT) with random master node selection, which has high communication overhead and a small supported network size, this paper proposes a Byzantine fault tolerant consensus algorithm based on credit (CBFT) enhanced with a grouping and credit model. The CBFT algorithm divides the network nodes according to the speed of their response to the management nodes, resulting in different consensus sets, and achieves consensus within and outside the group separately to reduce communication overhead and increase system security. Second, the nodes are divided into different types according to the credit model, each with different responsibilities to reduce the probability that the master node is a malicious node. Experimental results show that the throughput of the CBFT algorithm is 3.1 times that of PBFT and 1.5 times that of GPBFT when the number of nodes is 52. Our scheme has latency that is 7.4% that of PBFT and 38.8% that of GPBFT; CBFT has communication overhead that is 6.4% that of PBFT and 87.3% that of GPBFT. The number of nodes is 300, and the Byzantine fault tolerance is improved by 59.3%. These improvements are clearer with the increase in the number of nodes. [ABSTRACT FROM AUTHOR]

The continuous progress of society has created conditions for the widespread use of information technology. People rely more and more on information technology and the Internet. People can use the Internet to retrieve relevant information to meet their work needs, but the widespread use of the Internet is also accompanied by network security issues. The existence of network security problems will affect people's work to varying degrees. Network security management departments use intrusion detection technology and set firewalls to improve the security of people's personal information on the Internet and prevent viruses and some criminals from stealing people's important information through the Internet, causing adverse effects on the Internet environment. In the daily management of the school, the extensive use of the campus network can not only facilitate students' daily study and life, but also improve the efficiency of school management. Ensuring the network security of campus network is an important work to ensure the campus management and students' study and life. The four parts of network security work include setting firewall, encrypting cloud data, using intrusion detection technology, and recovering data. Intrusion detection technology is extremely important for the development of network security work, which can help people actively find vulnerabilities in networks and systems, identify possible behaviors that may invade systems and networks, and give early warning or automatically close intrusion channels. There are various types of cloud data, so cloud database plays an extremely important role in the development of various fields. This paper uses intrusion detection algorithms to design the campus network security protection system, which provides security for the use of the campus network. [ABSTRACT FROM AUTHOR]

With the rapid expansion of new energy in China, the large-scale grid connection of new energy is increasing, and the operating safety of the new energy power system is being put to the test. The static security and stability region (SSSR) with hyper-plane expression is an effective instrument for situational awareness and the stability-constrained operation of power systems. This paper proposes a hybrid improved particle swarm optimization (IPSO) and recursive least square (RLS) approach for rapidly approximating the SSSR boundary. Initially, the operating point data in the high-dimensional nodal injection space is examined using the IPSO algorithm to find the key generators, equivalent search space, and crucial points, which have a relatively large impact on static stability. The RLS method is ultimately utilized to fit the SSSR border that best suits the crucial spots. Consequently, the adopted algorithm technique was used to rapidly approximate the SSSR border in power injection spaces. Finally, the suggested algorithm is confirmed by simulating three kinds of generators of the new energy 118 bus system using the DIgSILENT/Power Factory. As a result, this method accurately characterized the stability border of the new energy power system and created the visualization space of the SSSR. Using the SSSR, a rapid state analysis could be undertaken on a variety of parameters, such as security evaluation with diverse energy supply capacities. This study's findings confirmed the accuracy and efficacy of the suggested modeling for the considered system and may thus give technical support for the new energy power system's stability. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) allows the integration of the physical world with network devices for proper privacy and security in a healthcare system. IoT in a healthcare system is vulnerable to spoofing attacks that can easily represent themselves as a legal entity of the network. It is a passive attack and can access the Medium Access Control address of some valid users in the network to continue malicious activities. In this paper, an algorithm is proposed for detecting spoofing attacks in IoT using Received Signal Strength (RSS) and Number of Connected Neighbors (NCN). Firstly, the spoofing attack is detected, located and eliminated through Received Signal Strength (RSS) in an inter-cluster network. However, the RSS is not useful against intra-cluster spoofing attacks and therefore the NCN is introduced to detect, identify and eliminate the intra-cluster spoofing attack. The proposed model is implemented in Network Simulator 2 (NS-2) to compare the performance of the proposed algorithm in the presence and absence of spoofing attacks. The result is that the proposed model increases the detection and prevention of spoofing. [ABSTRACT FROM AUTHOR]

With the continuous development of social economy, mobile wireless network as an important network communication mode is more and more widely used, but its security is worth paying attention to. According to different environment possible switch mobile wireless network security problem, the paper introduced secure localization algorithm, through combing the existing security vertical switching process, the corresponding vertical safety switch security index system was constructed, and the simulation experiment, from safety factor to switch from the mobile wireless network security monitoring the status of the transformation, and use the safety parameter to carry on the quantified computation expression. The simulation results prove that the visualization of the 5G network clearly shows the status of WLAN1. The simulation results show that the secure location algorithm is effective and can support secure vertical handoff in mobile wireless network. [ABSTRACT FROM AUTHOR]

A bit‐level image encryption algorithm based on Fully‐Connected‐Like network(FCLN) and random modification of edge pixels is proposed. In the paper, in order to enhance the security of the cryptographic system, random noise is first used to modify the least significant bits of the edge pixels of the image, and the modified image is used as the input image. Later,the chaotic sequence is used to perform cyclic shift transformation on the image. In the subsequent steps, the FCLN is generated based on a fully connected neural network, which can perform scrambling and diffusion operations on the input image. Finally, the bidirectional diffusion method is used to diffuse the image forward and backward. In addition, the image after the edge pixel modification is convolved with the chaotic sequence, and the initial value of the chaotic system is set by the result to establish the correlation between the plain image and the algorithm, which makes the algorithm resistant to known/chosen plaintext attack. Experimental results show that although the image is modified by random noise, the decrypted image is visually the same as the original image. At the same time, through the analysis of common attacks such as differential attacks, noise attacks, and data loss attacks, our algorithm shows high security. [ABSTRACT FROM AUTHOR]

The consensus mechanism is the core of the blockchain system, which plays an important role in the performance and security of the blockchain system. The Practical Byzantine Fault Tolerance (PBFT) algorithm is a widely used consensus algorithm, but the PBFT algorithm also suffers from high consensus latency, low throughput and performance. In this paper, we propose a grouped PBFT consensus algorithm (GPBFT) based on feature trust. First, the algorithm evaluates the trust degree of nodes in the transaction process through the EigenTrust trust model, and uses the trust degree of nodes as the basis for electing master nodes and proxy nodes. Then, the algorithm divides the nodes in the blockchain system into multiple groups, and the consensus within each independent group does not affect the other groups, which greatly reduces the communication overhead of the consensus process when the number of nodes in the system is large. Finally, we demonstrate through theoretical and experimental analysis that the GPBFT algorithm has a significant improvement in security and performance. [ABSTRACT FROM AUTHOR]

This paper provides an in-depth analysis of the history of DES and its successor Triple DES in the security market and secure data encryption. The different variations and improvements that have been proposed in research related to the uses of Triple DES since before it was approved as a standard for data encryption worldwide are studied. It is also important to mention that the proposals collected and analyzed are from different parts of the world, which solidifies the level of research carried out, due to the variety of sources found. In this way, the history of this algorithm is traced in order to provide the reader with a broad perspective on how it works and the different uses that have been made of it. Finally, it's determined that DES and 3-DES have been algorithms widely used in security information systems, but currently they are vulnerable to different attacks such as brute force and differential cryptanalysis, because of this it is suggested the use of other algorithms, for instance AES, or to include improvements in the DES and 3-DES implementation. [ABSTRACT FROM AUTHOR]

In order to alleviate the homogeneity problem, this paper proposed a diversity-based microservice resilience deployment mechanism. It designed the strategy to deploy a wide variety of instances according to resource constraints. Combined with centralized deployment and load balancing, the proposed strategy could effectively alleviate the homogeneity problem and enhance the resilience of the system. Thus, it implemented a minimum load deployment algorithm (Load-Min). Simulation experiments show that compared with other six classical algorithms, Load-Min algorithm has great improvement in system security, resource utilization, and load balancing performance. [ABSTRACT FROM AUTHOR]

Ensuring reliable operation of large power systems subjected to multiple outages is a challenging task because of the combinatorial nature of the problem. Traditional approaches for security assessment are often limited by their scope and/or speed, resulting in missing of critical contingencies that could lead to cascading failures. This paper proposes a two-component methodology to enhance power system security. The first component combines an efficient algorithm to detect cut-set saturation (called the feasibility test (FT) algorithm) with real-time contingency analysis (RTCA) to create an integrated corrective action (iCA), whose goal is to secure the system against cut-set saturation as well as critical branch overloads. The second component only employs the results of the FT to create a relaxed corrective action (rCA) to secure the system against post-contingency cut-set saturation. The first component is more comprehensive, but the latter is computationally more efficient. The effectiveness of the two components is evaluated based upon the number of cascade triggering contingencies alleviated, and the computation time. The results obtained by analyzing different case-studies on the IEEE 118-bus and 2000-bus synthetic Texas systems indicate that the proposed two-component methodology successfully enhances the scope and speed of power system security assessment during multiple outages. [ABSTRACT FROM AUTHOR]

As known, security system administrators need to be aware of the security risks and abnormal behaviors in a network system. Given the exploitation probability value of each vulnerability, the cumulative probability of an attack path from an attacker to a target node can be quantified and calculated, namely as the K maximum probability attack paths for a target node. It is proposed in this paper a design to compute the K maximum probability attack paths for a given set of target nodes, where available vulnerability sets for each node in the system are built and assigned to different access flags during the computation process of attack paths, aimed at reducing the computation costs. Experimental results show that the proposed design can improve the performance on the computation of the K maximum probability attack paths for a given set of target nodes, promising and more efficient than existing algorithms to generate the attack paths. [ABSTRACT FROM AUTHOR]

In this paper, we study an information flow security property for systems specified as terms of a quantitative Markovian process algebra, namely the Performance Evaluation Process Algebra (PEPA). We propose a quantitative extension of the Non-Interference property used to secure systems from the functional point view by assuming that the observers are able to measure also the timing properties of the system, e.g., the response time of certain actions or its throughput. We introduce the notion of Persistent Stochastic Non-Interference (PSNI) based on the idea that every state reachable by a process satisfies a basic Stochastic Non-Interference (SNI) property. The structural operational semantics of PEPA allows us to give two characterizations of PSNI: one based on a bisimulation-like equivalence relation inducing a lumping on the underlying Markov chain, and another one based on unwinding conditions which demand properties of individual actions. These two different characterizations naturally lead to efficient methods for the verification and construction of secure systems. A decision algorithm for PSNI is presented and an application of PSNI to a queueing system is discussed. [ABSTRACT FROM AUTHOR]

In this paper, we present a very simple and efficient hybrid encryption algorithm based on block and stream ciphers using chaotic systems. Due to the specific characteristics of chaotic systems which are described by a set of nonlinear deterministic dynamic equations, chaos-based encryption achieves a very high level of security. The chaotic system used in this algorithm is Chirikov Standard Map, which is chosen in order to further minimize the encryption time. The proposed scheme adopts two main operations one to generate pseudorandom data block that will be used for stream cipher, and the second to create substitution and permutation tables in initial step and perform rounds for confusion and diffusion processes in block cipher. Some cryptographic tests and metrics are applied to measure the degree of security and analyze the performance of the encryption scheme. The evaluation and simulation analysis indicate that our proposal possesses excellent cryptographic properties; it is extremely sensitive to the small change in secret key, resists against common cryptanalytic attacks, has a high speed and easy to implement. [ABSTRACT FROM AUTHOR]

This paper is concerned with the distributed secure estimation problem for a cyber-physical system (CPS) with fading measurements and false data injection (FDI) attacks, where the fading probability of measurements is assumed to be individual for each sensor, and the FDI attacks occur at the physical system layer to intensively modify the system's state. For improving security of the system, an attack detection mechanism is proposed based on an FDI attack observer constructed by minimising the upper bound of observation error covariance. Then a complete distributed state estimation algorithm is given to provide locally reliable estimation. A sufficient condition is provided to guarantee boundedness of the estimation error covariance. Finally, the effectiveness of the proposed method is illustrated by numerical simulation and practical example simulation. [ABSTRACT FROM AUTHOR]

A network security situation assessment system based on the extended hidden Markov model is designed in this paper. Firstly, the standard hidden Markov model is expanded from five-tuple to seven-tuple, and two parameters of network defense efficiency and risk loss vector are added so that the model can describe network security situation more completely. Then, an initial algorithm of state transition matrix was defined, observation vectors were extracted from the fusion of various system security detection data, the network state transition matrix was created and modified by the observation vectors, and a solution procedure of the hidden state probability distribution sequence based on extended hidden Markov model was derived. Finally, a method of calculating risk loss vector according to the international definition was designed and the current network risk value was calculated by the hidden state probability distribution; then the global security situation was assessed. The experiment showed that the model satisfied practical applications and the assessment result is accurate and effective. [ABSTRACT FROM AUTHOR]

Among the existing chaotic image encryption algorithms, there are problems such as low system security, inability to resist plaintext attacks, independent of encrypted images, and low efficiency. This paper proposed a double-graph association encryption algorithm based on a hyper-chaotic system. At first, it used a hyper-chaotic system together with the secret key to generate the key chaotic random matrix for image encryption. Then it masked plain images by the diffusion process. After that, it selected the first image to perform operations of plaintext-related scrambling and fast bit diffusion to generate cipher image. Finally, it subjected the remaining images to an adaptive correlation bit operation to generate a cipher image. Simulate results and comparative analysis show that the proposed image cryptosystem has the characteristics of large key space, the faster encryption/ decryption speed, the higher sensitivity, the better statistical properties of cipher image, and etc. And the corresponding plain text image cannot be obtained by only one cipher image. Therefore, the application prospect is greater. [ABSTRACT FROM AUTHOR]

In this paper, we consider an energy-constrained unmanned aerial vehicle (UAV)-enabled mobile relay assisted secure communication system in the presence of a legitimate source-destination pair and multiple eavesdroppers with imperfect locations. The energy-constrained UAV employs the power splitting (PS) scheme to simultaneously receive information and harvest energy from the source, and then exploits the time switching (TS) protocol to perform information relaying. Furthermore, we consider a full-duplex destination node which can simultaneously receive confidential signals from the UAV and cooperatively transmit artificial noise (AN) signals to confuse malicious eavesdroppers. To further enhance the reliability and security of this system, we formulate a worst case secrecy rate maximization problem, which jointly optimizes the position of the UAV, the AN transmit power, as well as the PS and TS ratios. The formulated problem is non-convex and generally intractable. In order to circumvent the non-convexity, we decouple the original optimization problem into three subproblems; this facilitates the design of a suboptimal iterative algorithm. In each iteration, we propose a multi-dimensional search and numerical method to handle the subproblem. Numerical simulation results are provided to demonstrate the effectiveness and superior performance of the proposed joint design versus the conventional schemes in the literature. [ABSTRACT FROM AUTHOR]

The abnormalities like (N-1) line contingencies are unavoidable and consequently can cause for either transmission system security margin decrement or power loss increment. Both the issues are primary objectives of the modern power system operation due to their significant effect on generation capacity and economics. This paper proposes Generalized Unified Power Flow Controller (GUPFC) for maintaining adequate security margin and performance improvement under (n-1) contingencies. At first stage, the optimal location of GUPFC device is proposed to determine based on (N-1) line contingency severity index. In second stage, the rating of the GUPFC device is optimized towards minimization of multiobjective function, which formulated using real power loss and security margin index. The parameters involved in the power injection modeling (PIM) of GUPFC and various system operational parameters are optimized using Particle Swarm Optimization (PSO) and Improved Particle Swarm Optimization (IPSO) algorithms towards minimization of multi-objective function. The case studies on IEEE 14-bus and IEEE 30-bus test system are validating the capability GUPFC device to minimize transmission loss and improve security margin significantly even under (n-1) line contingency and need of FACTS devices for real-time. [ABSTRACT FROM AUTHOR]

An electronic health (e-health) system, such as a medical cyber-physical system, offers a number of benefits (e.g. inform medical diagnosis). There are, however, a number of considerations in the implementation of the medical cyber-physical system, such as the integrity of medical / healthcare data (e.g. manipulated data can result in misdiagnosis). A number of digital signature schemes have been proposed in recent years to mitigate some of these challenges. However, the security of existing signatures is mostly based on conventional difficult mathematical problems, which are known to be insecure against quantum attacks. In this paper, we propose a certificateless signature scheme, based on NTRU lattice. The latter is based on the difficulty of small integer solutions on the NTRU lattice, and is known to be quantum attack resilience. Security analysis and performance evaluations demonstrate that our proposed scheme achieves significantly reduced communication and computation costs in comparison to two other competing quantum resilience schemes, while being quantum attack resilience. [ABSTRACT FROM AUTHOR]

Highlights • The wavelet transform and Yang-Gu mixture amplitude-phase retrieval algorithm based technique is proposed. • The decryption key is produced by performing binary modulation to chaotic spiral phase function. • The parameters of chaotic spiral phase function are used as remarkably sensitive decryption keys. • The proposed method can be implemented by using optoelectronic setup. • Numerical simulation results prove the security and feasibility of the strategy. Abstract In this paper, a novel multiple information security system based on wavelet transform and Yang-Gu mixture amplitude-phase retrieval algorithm is proposed. In this method, four color images are independently single-level 2-D discrete wavelet transformed. A single color image constructed by fusing LL sub-bands of four color images is separated into R, G , and B channels. The amplitude of each channel and corresponding chaotic spiral phase function (CSPF) are, respectively, exploited as an input function and a phase function in Yang-Gu mixture amplitude-phase retrieval algorithm in gyrator transform domain. The detectable retrieved amplitude is obtained by one-way binary phase modulation, which is more convenient for key management, storage, and transmission. The decryption keys are generated by executing binary phase modulation to CSPF. The parameters (initial value, bifurcation parameter, chosen integer, order, wavelength, focal length, and radius) of CSPF are used as decryption keys. Consequently, the proposed asymmetric system is protected by multiple-decryption keys and it can avoid various types of attacks. The proposed method can be implemented by using optoelectronic setup. Numerical simulation results prove the security and feasibility of the strategy. [ABSTRACT FROM AUTHOR]

Because the financial crisis early warning and monitoring is too complex, most of the current implementation can only alternately choose early warning in accordance with the criteria of monitoring the financial crisis. As a result, the security measures are not in accordance with the actual situation of the system, and cannot be adjusted quickly according to the changes of the system. This paper presents a financial crisis early warning detection algorithm based on fuzzy cognitive map. The relationship between financial data is obtained by fuzzy cognitive map, and the system crisis value is calculated by fuzzy cognitive map reasoning process. Finally, through the financial data of listed companies, the early warning and monitoring effect of the algorithm is verified. The experimental results show that the method is efficient, cost-effective, and can timely and reasonably reflect the crisis state of financial data. [ABSTRACT FROM AUTHOR]

Accurate detection of flames is an important prerequisite for the implementation of many security measures. To address the problems of low detection accuracy and difficulty in detecting small target flame in the current mainstream flame detection algorithms, we propose a small target flame detection algorithm incorporating an attention mechanism. First, we optimize the self-acquired dataset with data enhancement and structural similarity algorithms to improve the quality of the dataset and enhance the training efficiency of the model; then, a coordinate attention mechanism is introduced in the feature extraction stage of the original you-only-look-once (YOLOv7) network to improve the ability of the model to extract useful feature information and help the model to more accurately; and then, a small target flame detection layer is introduced at the output of the YOLOv7 network to improve the model's ability to detect small flames. Finally, the parameters of each layer of the improved model are initialized through model pre-training and migration learning to reduce the risk of model overfitting and accelerate the convergence speed of model training. The experimental results show that the average detection accuracy of the flame detection algorithm designed in this paper reaches up to 89.5%, which is 8.7% better than the detection accuracy of the original YOLOv7 algorithm and is innovative and practical. [ABSTRACT FROM AUTHOR]

Security plays a crucial role in payment systems; however, some implementations of payment card security rely on weak cardholder verification methods, such as card and a signature, or use the card without having any cardholder verification process at all. Other vulnerable implementations of cardholder verification methods suffer from many security attacks, such as relay attacks and cloning attacks. In addition, the impact of these security attacks is high since they cause monetary losses for banks and consumers. In this paper, we introduce a new cardholder verification method using a multi-possession factor authentication with a distance bounding technique. It adds an extra level of security to the verification process and utilizes the idea of distance bounding which prevents many different security attacks. The proposed method gives the user the flexibility to add one or more extra devices and select the appropriate security level. This paper argues that the proposed method mitigates or removes many popular security attacks that are claimed to be effective in current card based payment systems, and that it can help to reduce fraud on payment cards. Furthermore, the proposed method provides an alternative verification technique and enables cardholders with special needs to use the payment cards and make the payment system more accessible. [ABSTRACT FROM AUTHOR]

Security is considered a major challenge for self-driving and semi self-driving vehicles. These vehicles depend heavily on communications to predict and sense their external environment used in their motion. They use a type of ad hoc network termed Vehicular ad hoc networks (VANETs). Unfortunately, VANETs are potentially exposed to many attacks on network and application level. This paper, proposes a new intrusion detection system to protect the communication system of self-driving cars; utilising a combination of hierarchical models based on clusters and log parameters. This security system is designed to detect Sybil and Wormhole attacks in highway usage scenarios. It is based on clusters, utilising Time Division Multiple Access (TDMA) to overcome some of the obstacles of VANETs such as high density, high mobility and bandwidth limitations in exchanging messages. This makes the security system more efficient, accurate and capable of real time detection and quick in identification of malicious behaviour in VANETs. In this scheme, each vehicle log calculates and stores different parameter values after receiving the cooperative awareness messages from nearby vehicles. The vehicles exchange their log data and determine the difference between the parameters, which is utilised to detect Sybil attacks and Wormhole attacks. In order to realize efficient and effective intrusion detection system, we use the well-known network simulator (ns-2) to verify the performance of the security system. Simulation results indicate that the security system can achieve high detection rates and effectively detect anomalies with low rate of false alarms. [ABSTRACT FROM AUTHOR]

• A real-time application for secure data transmission using a different approach. • The receiver system requires a confirmation signal to perform necessary action. • Utilizing chaos dynamics for generating secret codes used in the algorithm. • The transmitted data can be a simple bit or any multimedia data of major systems. In today's technology, remote control or access is widely used in many platforms. However, the fact that remotely controlled platforms are open to external attacks or not secure enough which can have serious negative consequences. In this paper, a real-time LoRa modulation based data transmission application is achieved. The security of remotely transmitted data is not only provided with a basic encryption structure, but also a confirmation signal based transceiver system is designed in which the required process is performed only with a certain time interval. Firstly, the data to be transmitted is mixed with a secret key using XOR operation then transmitter creates a random period of time that specifies how long after the confirmation signal will be sent relative to the data signal. Hence, the entire system security is enhanced by the timing algorithm running with millisecond precision between the transceiver circuits. Encryption of the confirmation signal in a similar structure as the data signal improves security of the entire system. In this application, a PWM signal containing voltage information has been successfully transmitted in ciphered form using LoRa modulation. The designed algorithm for the receiver and transmitter circuits has been tested and verified in real time application. Experimental results show that the proposed scheme can be applied to any application-based communication system. [ABSTRACT FROM AUTHOR]

SUMMARY This paper investigates bidding strategy problems of generation companies in electricity markets. A new bidding model is set up, and a solution approach is presented. Compared with existing models, the model proposed in this paper has some different characteristics. First, the model adopts a bilevel optimization framework where the upper-level optimization is to maximize the profit of generation companies, and the aim of the lower-level optimization is the maximal social welfare and the security of the operation system considered by the Independent System Operator. This model combines practical situations faced by market agents in real-life operations and the requirement of both the system security and the social welfare. Second, the popular risk measure, called conditional value at risk, is used in the bidding model, which can quantify the market risk and describe the competitive mechanism effectively. For solving the complicated bilevel optimization model, a hybrid particle swarm optimization algorithm is presented. Numerical examples are used to validate the model and the algorithm. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

When securing complex infrastructures or large environments, constant surveillance of every area is not affordable. To cope with this issue, a common countermeasure is the usage of cheap but wide-ranged sensors, able to detect suspicious events that occur in large areas, supporting patrollers to improve the effectiveness of their strategies. However, such sensors are commonly affected by uncertainty. In the present paper, we focus on spatially uncertain alarm signals. That is, the alarm system is able to detect an attack but it is uncertain on the exact position where the attack is taking place. This is common when the area to be secured is wide, such as in border patrolling and fair site surveillance. We propose, to the best of our knowledge, the first Patrolling Security Game where a Defender is supported by a spatially uncertain alarm system , which non-deterministically generates signals once a target is under attack. We show that finding the optimal strategy is FNP -hard even in tree graphs and APX -hard in arbitrary graphs. We provide two (exponential time) exact algorithms and two (polynomial time) approximation algorithms. Finally, we show that, without false positives and missed detections, the best patrolling strategy reduces to stay in a place, wait for a signal, and respond to it at best. This strategy is optimal even with non-negligible missed detection rates, which, unfortunately, affect every commercial alarm system. We evaluate our methods in simulation, assessing both quantitative and qualitative aspects. [ABSTRACT FROM AUTHOR]

Nowadays personalized influence maximization has become a new branch of influence maximization in social network. The existed methods mainly focus on independent cascade model and linear threshold model, in those two models, nodes' influence forecast depends on the Monte-Carlo simulation. In order to simulate the propagation of influence more efficiently, we introduce heat diffusion model to the study of personalized influence maximization in this paper. The diffusion process of heat is adopted to simulate the influence of information among users. Furthermore, we propose a target's heat greedy algorithm based on the analysis of the laws of the heat propagation. Experimental results on real datasets show that our proposed algorithm is effective. [ABSTRACT FROM AUTHOR]

This paper describes an efficient new algorithm being developed for the Wide Area Augmentation System (WAAS) that significantly improves its ability to detect threats arising from multiple simultaneous one to two sigma errors. This new algorithm is based on the sum of the squares of the normalized errors at each reference station. It has significantly improved the ability to detect error conditions that are not obviously faulted individually, but that could be harmful to users when combined. Currently, other monitors are inflating the confidence bounds to protect against such conditions. This new User Position Monitor (UPM) is able to identify such threats, and as a consequence, excess conservatism may be removed from the system. We expect this innovation to lead to better availability while maintaining equal or better levels of integrity. Copyright © 2017 Institute of Navigation [ABSTRACT FROM AUTHOR]

Approaches to access control (AC) policy languages, such as eXtensible access control markup language, do not provide a formal representation for specifying rule- and policy-combining algorithms or for verifying properties of AC policies. Some authors propose formal representations for these combining algorithms. However, the proposed models are not expressive enough to represent formally history-based classes of these algorithms, such as ordered-permit-overrides. In addition, some other authors propose a formal representation but do not present automated support for formal verification of properties of AC policies that use these algorithms. This paper demonstrates a new representation that can express all existing AC rule and policy combinations of which the authors are aware. This representation can also be used to automate the formal verification of properties of AC policies related to these algorithms. A new modeling representation for rule- and policy-combining algorithms based on state machines is used to specify rule- and policy-combining algorithms. Examples of these algorithms are programmed in the language of the SPIN model checker, and the programs are then used to support the automated formal verification of properties of AC policies. We present our approach and then use the AC policies and properties of CONTINUE, a conference management system, to compare it with prior work. Our first contribution is a new modeling representation for combining algorithms based on state machines. The second contribution is the formal verification of AC properties under certain combining algorithms that are beyond the capability of other approaches. [ABSTRACT FROM AUTHOR]

Saliency text is that characters are ordered with visibility and expressivity. It also contains important clues for video analysis, indexing, and retrieval. Thus, in order to localize the saliency text, a critical stage is to collect key points from real text pixels. In this paper, we propose an evidence-based model of saliency feature extraction (SFE) to probe saliency text points (STPs), which have strong text signal structure in multi-observations simultaneously and always appear between text and its background. Through the multi-observations, each signal structure with rhythms of signal segments is extracted at every location in the visual field. It supports source of evidences for our evidence-based model, where evidences are measured to effectively estimate the degrees of plausibility for obtaining the STP. The evaluation results on benchmark datasets also demonstrate that our proposed approach achieves the state-of-the-art performance on exploring real text pixels and significantly outperforms the existing algorithms for detecting text candidates. The STPs can be the extremely reliable text candidates for future text detectors. [ABSTRACT FROM AUTHOR]

The paper deals with developing algorithms for stall diagnostics and airplane protection from inadvertently encountering post stall gyration and entering spin modes. It summarizes the results of the linear theory of stall developing as theoretical foundation of nonlinear approach for diagnostics of flight conditions relating to the loss of aircraft control which is understood as a moment when the angular rates exceed some critical (threshold) values, which in general depend on the angle of attack and airspeed and they correspond to control surfaces deflection. [ABSTRACT FROM AUTHOR]

As a promising biometric system, finger vein identification has been studied widely and many relevant researches have been proposed. However, it is hard to extract a satisfied finger vein pattern due to the various vein thickness, illumination, low contrast region, and noise existing. And most of the feature extraction algorithms rely on high-quality finger vein database and take a long time for a large dimensional feature vector. In this paper, we proposed two block selection methods which are based on the estimate of the amount of information in each block and the contribution of block location by looking at recognition rate of each block position to reduce feature extraction time and matching time. The specific approach is to find out some local finger vein areas with low-quality and noise, which will be useless for feature description. Local binary pattern (LBP) descriptors are proposed to extract the finger vein pattern feature. Two finger vein databases are taken to test our algorithm performance. Experimental results show that proposed block selection algorithms can reduce the feature vector dimensionality in a large extent. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) are created according to the principles of ad hoc mobile networks (MANETs), i.e., spontaneous creation of a wireless network for vehicle-to-vehicle (V2V) communication. Each vehicle in this network is treated as a node that is part of the mobile network. VANET turns all cooperating vehicles into a wireless router or node. This makes it possible to connect all cars within range to a stationary unit and create a wide network with a huge range. VANET is widely used for better traffic management, vehicle-to-vehicle communication, and road information provision. The VANET network is exposed to identity and information attacks, concealing or delaying data transmission, or information theft. Therefore, there are multiple types of attack, such as Sybil or bogus, that might harm the whole network infrastructure. The consequences of the mentioned two attacks could lead not only to the given infrastructure but could cause hammering people's lives. In this paper, we analyze the ongoing methods for preserving Sybil and bogus attacks in a VANET network together with the authors' methods: the Bogus & Sybil Trust Level & Timestamp (B&STL&T) algorithm and the Bogus & Sybil Enhanced Behavior Processing & Footprint (B&SEBP&F) algorithm. The first algorithm, the Bogus & Sybil Trust Level & Timestamp (B&STL&T) algorithm was improved into the Bogus & Sybil Enhanced Behavior Processing & Footprint (B&SEBP&F), presented in the paper. The proposed methods were tested with multiple scenarios using different variations of bogus and Sybil attack and various attacker–victim node number ratios. During analysis, it was observed that detection of all attackers in the network was reduced by approximately 30% in comparison to previous work and that of other cited authors. [ABSTRACT FROM AUTHOR]

The number of vulnerabilities discovered and reported during the recent decades is enormous, making an improved ranking and prioritization of vulnerabilities’ severity a major issue for information technology (IT) management. Although several methodologies for ranking and scoring vulnerabilities have been proposed, the Common Vulnerability Scoring System (CVSS) is the open standard with wide acceptance from the information security community. Recently, the Weighted Impact Vulnerability Scoring System (WIVSS) has been proposed as an alternative scoring methodology, which assigns different weights to impact factors of vulnerability in order to achieve higher diversity of values and thus improvement in flexibility of ranking in comparison to CVSS. The purpose of this paper is to expand the idea of WIVSS by defining the sets of weights which provide higher diversity of values. For this reason, an algorithm that finds all the possible combinations of optimal weights within a specified range and under certain constrains is presented. The algorithm results in 14 different combinations of impact weights that are applied to a sample of 20,496 vulnerabilities and statistically analyzed for associations among impact factors. The results suggest that one specific combination of impact weights can achieve highest diversity of values. [ABSTRACT FROM PUBLISHER]

This paper presents a flexible deployment of ultrasonic position sensors and a novel positioning algorithm suitable for the navigation of mobile robots (MRs) in extensive indoor environments. Our proposal uses several independently-referenced local positioning systems (LPS), which means that each one of them operates within its own local reference system. In a typical layout, an indoor extensive area can be covered using just a reduced set of globally-referenced LPS (GRLPS), whose beacon positions are known to the global reference system, while the rest of the space can be covered using locally-referenced LPSs (LRLPS) that can be distributed arbitrarily. The number of LRLPS and their position can be also changed at any moment. The algorithm is composed of several Bayesian filters running in parallel, so that when an MR is under the GRLPS coverage area, its position is updated by a global filter, whereas when the MR is inside the LRLPS area, its position is updated using position increments within a local filter. The navigation algorithm has been tested by simulation and with actual data obtained using a real set of ultrasonic LPSs. [ABSTRACT FROM AUTHOR]

As a hotspot of machine learning research, deep learning is applied in many fields. Embedded systems are becoming more and more complex and networked, so the real-time performance of embedded systems and the security of network embedded devices face severe challenges. Based on this, this paper studies the real-time task scheduling problem for complex embedded systems and the security of embedded network devices. For real-time, this paper proposes a comprehensive task scheduling algorithm. Based on the task classification in the embedded system, different scheduling methods are adopted for different tasks, and the scheduling mode is flexibly changed as the system load changes. A dynamic integrity measurement model is established based on the star trust chain structure, and the hardware implementation mechanism of constructing dynamic trust chain in embedded system is studied. The dynamic reconfigurable hardware design method based on FPGA is applied to the construction of dynamic trust chain, and a verification system is designed to verify the dynamic measurement mechanism. This can solve the security problem of deep network embedded devices to a certain extent. [ABSTRACT FROM AUTHOR]

Critical Infrastructures (CIs) are sensible targets. They could be physically damaged by natural or human actions, causing service disruptions, economic losses, and, in some extreme cases, harm to people. They, therefore, need a high level of protection against possible unintentional and intentional events. In this paper, we show a logical architecture that exploits information from both physical and cybersecurity systems to improve the overall security in a power plant scenario. We propose a Machine Learning (ML)-based anomaly detection approach to detect possible anomaly events by jointly correlating data related to both the physical and cyber domains. The performance evaluation showed encouraging results—obtained by different ML algorithms—which highlights how our proposed approach is able to detect possible abnormal situations that could not have been detected by using only information from either the physical or cyber domain. [ABSTRACT FROM AUTHOR]

As an essential part of an encryption system, the performance of a chaotic map is critical for system security. However, there are many defects for the existing chaotic maps. The low-dimension (LD) ones are easily predicted and are vulnerable to be attacked, while high-dimension (HD) ones have a low iteration speed. In this paper, a 2D multiple collapse chaotic map (2D-MCCM) was designed, which had a wide chaos interval, a high complexity, and a high iteration speed. Then, a new chaotic S-box was constructed based on 2D-MCCM, and a diffusion method was designed based on the S-box, which improved security and efficiency. Based on these, a new image encryption algorithm was proposed. Performance analysis showed that the encryption algorithm had high security to resist all kinds of attacks easily. [ABSTRACT FROM AUTHOR]

A variant of adaptive arithmetic coding is proposed, adding cryptographic features to this classical compression method. The idea is to perform the updates of the frequency tables for characters of the underlying alphabet selectively, according to some randomly chosen secret key K. We give empirical evidence that with reasonably chosen parameters, the compression performance is not hurt, and discuss also aspects of how to improve the security of the system being used as an encryption method. To keep the paper self-contained, we add a short description of the arithmetic coding algorithm that is necessary to understand the details of the new suggested method. [ABSTRACT FROM AUTHOR]

Robots can be very different, from humanoids to intelligent self-driving cars or just IoT systems that collect and process local sensors' information. This paper presents a way to increase dependability for information exchange and processing in systems with Cloud-Fog-Edge architectures. In an ideal interconnected world, the recognized and registered robots must be able to communicate with each other if they are close enough, or through the Fog access points without overloading the Cloud. In essence, the presented work addresses the Edge area and how the devices can communicate in a safe and secure environment using cryptographic methods for structured systems. The presented work emphasizes the importance of security in a system's dependability and offers a communication mechanism for several robots without overburdening the Cloud. This solution is ideal to be used where various monitoring and control aspects demand extra degrees of safety. The extra private keys employed by this procedure further enhance algorithm complexity, limiting the probability that the method may be broken by brute force or systemic attacks. [ABSTRACT FROM AUTHOR]

• Protection devices independent assessment of grid security. • Applicable to multivariable grid structures. • New evaluation algorithm based on realistic simulation data and flexible fuzzy sets. • Full adaptive and automatic protection security assessment. • Specifically designed for the use with an optimization algorithm to generate best settings. System Protection Security Assessment is an important task in modern energy grids to ensure system security at all. The assessment system is particularly challenged by multivariate grid structures caused by volatile renewable infeeds. This paper presents an innovative strategy to evaluate the protection relay coordination of system-wide power grids. A way to calculate the quality of all protection relays based on realistic simulation data and independent of the protection method was sought. The hybrid algorithm consists of two major steps. First, a systematic analysis with various fault simulations is performed and the measurement results of all relays of all simulations are used as database. Subsequently, the use of fuzzy sets allows to express the quality of each relay setting regardless of its type. Specifically developed for the use of an optimization algorithm, finally, a new protection coordination is determined for an adapted version of the IEEE 9 bus grid. The results are validated, discussed and the effectiveness of the methodology compared to conventional setting rules. [ABSTRACT FROM AUTHOR]

The boom of Internet of Things devices promotes huge volumes of eHealthcare data will be collected and aggregated at eHealthcare provider. With the help of these health data, eHealthcare provider can offer reliable data service (e.g., k-NN query) to doctors for better diagnosis. However, the IT facility in the eHealthcare provider is incompetent with the huge volumes of eHealthcare data, so one popular solution is to deploy a powerful cloud and appoint the cloud to execute the k-NN query service. In this case, since the eHealthcare data are very sensitive yet cloud servers are not fully trusted, directly executing the k-NN query service in the cloud inevitably incurs privacy challenges. Apart from the privacy issues, efficiency issues also need to be taken into consideration because achieving privacy requirement will incur additional computational cost. However, existing focuses on k-NN query do not (fully) consider the data privacy or are inefficient. For instance, the best computational complexity of k-NN query over encrypted eHealthcare data in the cloud is as large as O (k log 3 N) , where N is the total number of data. In this paper, aiming at addressing the privacy and efficiency challenges, we design an efficient and privacy-preserving k-NN query scheme for encrypted outsourced eHealthcare data. Our proposed scheme is characterized by integrating the k d-tree with the homomorphic encryption technique for efficient storing encrypted data in the cloud and processing privacy-preserving k-NN query over encrypted data. Compared with existing works, our proposed scheme is more efficient in terms of privacy-preserving k-NN query. Specifically, our proposed scheme can achieve k-NN computation over encrypted data with O (lk log N) computational complexity, where l and N respectively denote the data dimension and the total number of data. In addition, detailed security analysis shows that our proposed scheme is really privacy-preserving under our security model and performance evaluation also indicates that our proposed scheme is indeed efficient in terms of computational cost. [ABSTRACT FROM AUTHOR]