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The rapid advancement of deep learning has significantly heightened the threats posed by Side-Channel Attacks (SCAs) to information security, transforming their effectiveness to a degree several orders of magnitude superior to conventional signal processing techniques. However, the majority of existing Deep-Learning Side-Channel Attacks (DLSCAs) primarily focus on the classification accuracy of the trained model at the attack stage, often assuming that adversaries have unlimited computational and time resources during the profiling stage. This might result in an inflated assessment of the trained model's fitting capability in a real attack scenario. In this paper, we present a novel DLSCA model, called a Multi-Dimensional Fusion Convolutional Residual Dendrite (MD_CResDD) network, to enhance and speed up the feature extraction process by incorporating a multi-scale feature fusion mechanism. By testing the proposed model on two software implementations of AES-128, we show that it is feasible to improve the profiling speed by at least 34% compared to other existing deep-learning models for DLSCAs and meanwhile achieved a certain level of improvement (8.4% and 0.8% for two implementations) in the attack accuracy. Furthermore, we also investigate how different fusion approaches, fusion times, and residual blocks can affect the attack efficiency on the same two datasets. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is an innovative technology that has the potential to enhance the scalability, flexibility, and security of telecommunications networks. The emergence and development of SDNs have introduced new opportunities and challenges in the telecommunications industry. One of the major challenges encountered by SDNs is the timing side-channel attacks. These attacks exploit timing information to expose sensitive data, including flow tables, routes, controller types, and ports, which pose a significant threat to communication networks. Existing techniques for mitigating timing side-channel attacks primarily focus on limiting them via network architectural changes. This significantly increases the overhead of SDNs and makes it difficult to identify the origin of the attack. To secure resilient integration of SDN in telecommunications networks, it is necessary to conduct comprehensive research that not only identifies the attack activity, but also formulates an adequate response. In this paper, we propose a detection and response solution for timing side-channel attacks in SDN. We used a machine learning-based approach to detect the probing activity and identify the source. To address the identified timing side-channel attack queries, we propose a response mechanism. This entails devising a feedback-oriented response to counter the identified source, such as blocking or diverting it, while minimising any adverse effects on legitimate network traffic. This methodology is characterised by an automated data-driven approach that enables prompt and effective responses. The architecture of this security solution ensures that it has a minimal impact on network traffic and resource usage as it is designed to be used in conjunction with SDN. The overall design findings show that our detection approach is 94% precise in identifying timing side-channel attacks in SDN when compared with traditional mitigation strategies. Additionally, the response mechanism employed by this approach yielded highly customised and precise responses, resulting in an impressive accuracy score of 97.6%. [ABSTRACT FROM AUTHOR]

Side-channel attacks represent a realistic and serious threat to the security of embedded devices for already almost three decades. A variety of attacks and targets they can be applied to have been introduced, and while the area of side-channel attacks and their mitigation is very well-researched, it is yet to be consolidated. Deep learning-based side-channel attacks entered the field in recent years with the promise of more competitive performance and enlarged attackers’ capabilities compared to other techniques. At the same time, the new attacks bring new challenges and complexities to the domain, making the systematization of knowledge (SoK) even more critical. We first dissect deep learning-based side-channel attacks according to the different phases they can be used in and map those phases to the efforts conducted so far in the domain. For each phase, we identify the weaknesses and challenges that triggered the known open problems.We also connect the attacks to the threat models and evaluate their advantages and drawbacks. Finally, we provide a number of recommendations to be followed in deep learning-based side-channel attacks. [ABSTRACT FROM AUTHOR]

This paper reviews hardware-based protection methods for electronic devices, encompassing scientific publications and published patents. This review covers insights from the scientific community and innovative solutions patented in the industry. By combining these two sources, this paper offers a comprehensive and holistic review of electronic device security. Electronic devices are integral to modern life, but their widespread use invites security threats, both digital and physical. This paper reviews hardware-based protection methods against invasive and non-invasive attacks, emphasizing the importance of a dual approach through hardware design. Invasive attacks involve physical tampering, and we explore anti-tampering techniques such as conductive meshes, sensors and physically unclonable functions (PUFs). Non-invasive, side-channel attacks encompass various attack vectors, focusing on electromagnetic analysis. To counter these attacks, we analyze techniques like reducing and masking electromagnetic radiation. This paper bridges the gap between invasive and non-invasive attack mitigation. It underscores the necessity of a multifaceted approach to safeguard electronic devices in an interconnected world, preserving their reliability and functionality. [ABSTRACT FROM AUTHOR]

Side-channel attacks exploit a physical observable originating from a cryptographic device in order to extract its secrets. Many practically relevant advances in the field of side-channel analysis relate to security evaluations of cryptographic functions and devices. Accordingly, many metrics have been adopted or defined to express and quantify side-channel security. These metrics can relate to one another, but also conflict in terms of effectiveness, assumptions, and security goals. In this work, we review the most commonly used metrics in the field of side-channel analysis. We provide a self-contained presentation of each metric, along with a discussion of its limitations. We practically demonstrate the metrics on examples of relevant implementations of the Advanced Encryption Standard (AES), and make the software implementation of the presented metrics available to the community as open source. This work, being beyond a survey of the current status of metrics, will allow researchers and practitioners to produce a well-informed security evaluation through a better understanding of its supporting and summarizing metrics. [ABSTRACT FROM AUTHOR]

The Advanced Encryption Standard (AES) is widely recognized as a robust cryptographic algorithm utilized to protect data integrity and confidentiality. When it comes to lightweight implementations of the algorithm, the literature mainly emphasizes area and power optimization, often overlooking considerations related to performance and security. This paper evaluates two of our previously proposed lightweight AES implementations using both profiled and non-profiled attacks. One is an unprotected implementation, and the other one is a protected version using Domain-Oriented Masking (DOM). The findings of this study indicate that the inclusion of DOM in the design enhances its resistance to attacks at the cost of doubling the area.

Significant advancements have been achieved in the field of quantum computing in recent years. If somebody ever creates a sufficiently strong quantum computer, many of the public-key cryptosystems in use today might be compromised. Kyber is a post-quantum encryption technique that depends on lattice problem hardness, and it was recently standardized. Despite extensive testing by the National Institute of Standards and Technology (NIST), new investigations have demonstrated the effectiveness of CRYSTALS-Kyber attacks and their applicability in non-controlled environments. We investigated CRYSTALS-Kyber’s susceptibility to side-channel attacks. In the reference implementation of Kyber512, additional functions can be compromised by employing the selected ciphertext. The implementation of the selected ciphertext allows the attacks to succeed. Real-time recovery of the entire secret key is possible for all assaults.

The masking countermeasure is very effective against side-channel attacks such as differential power analysis. However, the design of masked circuits is a challenging problem since one has to ensure security while minimizing performance overheads. The security of masking is often studied in the t-probing model, and multiple formal verification tools can verify this notion. However, these tools generally cannot verify large masked computations due to computational complexity. We introduce a new verification tool named Quantile, which performs randomized simulations of the masked circuit in order to bound the mutual information between the leakage and the secret variables. Our approach ensures good scalability with the circuit size and results in proven statistical security bounds. Further, our bounds are quantitative and, therefore, more nuanced than t-probing security claims: by bounding the amount of information contained in the lower-order leakage, Quantile can evaluate the security provided by masking even when they are not 1-probing secure, i.e., when they are classically considered as insecure. As an example, we apply Quantile to masked circuits of Prince and AES, where randomness is aggressively reused.

Quantum key distribution (QKD) is an effective technique to share unconditional secure keys among a pair of legal distant users with the existence of eavesdroppers. It depends upon quantum mechanics that exhibits the probability for an eavesdropper to acquire full information on the key is forbidden by the laws of physics. Though quantum mechanics assure the security of the QKD system, it is critical to inspect whether the imperfections can result in the revelation of sensitive data. The presence of imperfections can result in side-channel attacks and the ones relevant to single-photon detectors are crucial as it is adopted in several QKD approaches. For resolving these issues, this study develops a new Security in Quantum side Channel (SQSC) framework to accomplish security. The proposed SQSC approach involves the design of Shifting and Binary Conversions (SBC) algorithm with two sub-processes namely shifting process and binary conversion. Since none of the works have been developed quantum channel in the current system to secure data, the proposed model added complexity to the secret data (shifting process and binary conversion using SBC) before q-bit conversion. The design of SBC algorithm to resolve side-channel attacks and accomplish improved security shows the novelty of the work. In order to demonstrate the enhanced performance of the SQSC approach, a comparison study with existing model is performed. The experimental results reported the promising performance of the SQSC approach over the other existing methods. [ABSTRACT FROM AUTHOR]

Differential deep learning analysis (DDLA) is the first deep-learning-based nonprofiled side-channel attack (SCA) on embedded systems. However, DDLA requires many training processes to distinguish the correct key. In this letter, we introduce a nonprofiled SCA technique using multi-output classification to mitigate the aforementioned issue. Specifically, a multi-output multilayer perceptron and a multi-output convolutional neural network are introduced against various SCA protected schemes, such as masking, noise generation, and trace de-synchronization countermeasures. The experimental results on different power side channel datasets have clarified that our model performs the attack up to 9–30 times faster than DDLA in the case of masking and de-synchronization countermeasures, respectively. In addition, regarding combined masking and noise generation countermeasure, our proposed model achieves a higher success rate of at least 20% in the cases of the standard deviation equal to 1.0 and 1.5. [ABSTRACT FROM AUTHOR]

Because of its strong leakage characterization ability and high-key-recovery efficiency, template attack (TA) is the strongest side-channel attack from the information-theoretic point-of-view. In light of this, TA can be used as a powerful tool to evaluate the security of a crypto device in real scenarios. In fact, by evaluating the key-recovery efficiency of TA, the security of a crypto device can be quantified. Therefore, it is very meaningful to optimize the key-recovery efficiency of TA in real scenarios. In this work, we show that the leakage exploitation style can significantly influence the key-recovery efficiency of TA. In detail, rather than exploiting the leakage of the noise part, the leakage of the squared noise part can be used in TA. Theoretically, by exploiting the leakage of the squared noise part, the key-recovery efficiency of TA can be optimized under the normalized difference metric. Empirically, with the evaluation data provided by DPA Contest and ASCAD, the key-recovery efficiency of TA can be optimized under the success rate metric. Overall, this contribution may help one to accurately evaluate the security of a crypto device in real scenarios. [ABSTRACT FROM AUTHOR]

In the last 15 years, we have witnessed a never-ending arm’s race between the attacker and the defender with respect to cache-based side-channel attacks. We have seen a slew of attacks, countermeasures (CMs), counterattacks, counter-CMs, and so on. We analyze the evolution of this area, propose three necessary conditions for designing a successful CM, and then analyze timing and address-based CMs for popular algorithms, such as AES and PRESENT. We show that an optimal yet trivial solution for timing-based CMs is possible. Furthermore, address-based CMs are inferior to timing-based CMs, and they can be broken in $n^{O(\log (\log (n)))}$ time. [ABSTRACT FROM AUTHOR]

The post-quantum cryptography (PQC) standardization process of the National Institute of Standards and Technology (NIST) has entered the final round. Kyber is one of the four key encapsulation mechanism (KEM) finalists. The coefficient multiplication is vulnerable to side-channel attacks. In this paper, we analyzed the multiplication part of Kyber and proposed the methods for constructing ciphertext. By using the chosen ciphertext, more functions can be attacked in the reference implementation of Kyber512. The attacks succeed on the pqm4 implementation with chosen ciphertext while failing with random ciphertext in our experiment environment. For all the attacks, the full secret key recovery can be achieved in a few minutes. • We used chosen ciphertext to attack more position in the decryption of Kyber. • The number of traces used for some functions was reduced by using chosen ciphertext. • In all the experiments, the proposed attack taked only several minutes. [ABSTRACT FROM AUTHOR]

Adversaries are often able to penetrate networks and compromise systems by exploiting vulnerabilities in people and systems. The key to the success of these attacks is information that adversaries collect throughout the phases of the cyber kill chain.We summarize and analyze the methods, tactics, and tools that adversaries use to conduct reconnaissance activities throughout the attack process. First, we discuss what types of information adversaries seek and how and when they can obtain this information. Then, we provide a taxonomy and detailed overview of adversarial reconnaissance techniques. The taxonomy introduces a categorization of reconnaissance techniques based on the source as third-party and human-, and system-based information gathering. This article provides a comprehensive view of adversarial reconnaissance that can help in understanding and modeling this complex but vital aspect of cyber attacks as well as insights that can improve defensive strategies, such as cyber deception. [ABSTRACT FROM AUTHOR]

Correlation power analysis (CPA) is a classical method in side-channel attacks. Based on the power consumption model, the correlation between the power consumption of cryptographic devices and the assumed intermediate value is analyzed to recover the key. Theoretically, only a few power traces are required to recover the key when the noise hypothesis is known. However, in the high-frequency and high-noise environment, the completion of CPA requires more power traces, and the computational complexity also increases. Therefore, this paper proposes a fault probability correlation analysis method based on secondary filtering (2F-FPCA), which selects the fault probability traces according to the Hamming Weight of the intermediate value and reduces the number of sampling points by selecting points of interest. This method does not need to access ciphertext and is little affected by noise. Moreover, it can recover the key with fewer fault probability traces and lower computational complexity, improving the attack efficiency of CPA. In this paper, 2F-FPCAs are carried out based on the AES-128 algorithm of the Micro Controller Unit (MCU). The key can be recovered successfully using 10 fault probability traces, and the computational complexity is reduced by 104 times.

Signature can be used to verify the integrity of both a message and the identity of a signer, whereas encryption can be used to ensure the confidentiality of a message. In the past, cryptography researchers have studied and proposed numerous certificateless signcryption (CLSC) schemes to combine the benefits of both signature and encryption. However, these schemes may not be robust enough to withstand side-channel attacks. Through such attacks, an attacker can constantly retrieve a portion of a private key of the system, and could eventually recover the entire private key. Leakage-resilient certificateless signcryption (LR-CLSC) can ensure its security when the attacker launches such attacks. As far as we know, the existing LR-CLSC schemes can only guarantee the security under a bounded leakage model, where the portion of the private key that an attacker can obtain through side-channel attacks is limited. In this paper, we propose the $first$ LR-CLSC scheme under a continual leakage model. Also, we demonstrate the proposed scheme is secure for the existential unforgeability and the ciphertexts indistinguishability against attackers with side-channel attacking abilities.

Key encapsulation schemes in public key system (PKS) can be used to protect sensitive or private data. Unlike traditional PKS and identity-based PKS, certificate-based PKS (CB-PKS) not only avoids the establishment of complex public key infrastructures, but also does not encounter the key escrow problem. Anonymous multi-receiver certificate-based encryption (AMR-CBE) or anonymous multi-receiver certificate-based key encapsulation (AMR-CB-KE) scheme enables a sender to use multiple receivers’ public keys to perform one-time encryption process for a message and send the encrypted ciphertext to these receivers, while these receivers do not know the other receiver’s identity. However, the existing AMR-CBE and AMR-CB-KE schemes cannot resist side-channel attacks. Attackers with the ability of such attacks can continuously obtain part (several bits) of the secret keys and then calculate the complete secret keys. In such a case, such attacks make a cryptographic scheme (including AMR-CBE and AMR-CB-KE schemes) insecure. Leakage-resilient cryptography is an important research topic to resist side-channel attacks. In this paper, we propose the first leakage-resilient anonymous multi-receiver certificate-based key encapsulation (LR-AMR-CB-KE) scheme. Based on the discrete logarithm and hash function assumptions, we demonstrate the scheme has the indistinguishability of two ciphertexts against chosen ciphertext attacks (IND-CCA) and the anonymous indistinguishability of two identities against chosen ciphertext attacks (ANON-IND-CCA) for two types of attackers in CB-PKS settings.

Cloud providers are increasingly exposed to malicious actors through transient attacks, such as Spectre and Meltdown. The cache hierarchy is the main target to build the required side-channels to leak data. Randomized caches can be employed to provide security but often rely on cryptographic primitives to deter side-channel attacks. These increase the access latency and deteriorates the system performance. This paper shows that randomized caches do not have to increase the cache access latency, and that their security does not have to rely on a cryptographic hash function or block-cipher. Herein, CoDi $\$ $ is proposed, a randomized last level cache that achieves security by tying the local and global states. Security is achieved through a higher miss energy consumption and occupied area, instead of penalizing performance. CoDi $\$ $ is able to evict any cached address by allowing addresses to be displaced in two levels of freedom, through hopscotch (local state) and cuckoo hashing (global state), without increasing the hit latency. Through these displacements, paths can be built to the eviction address. The security of CoDi $\$ $ relies on the hardness to control all possible displacement paths when a miss occurs, which requires control over the local and global states of the cache simultaneously. Confusion is generated when evicting an address, as there are many possible cache states that could result from this eviction. Also, for each executed eviction, multiple addresses in the cache are non-deterministically displaced, providing diffusion. Moreover, experimental analysis for a 48-bit secure CoDi $\$ $ , using SPEC, NPB, and Polybench benchmarks, shows improvements in the number of instructions per cycle and up to 5% in misses, when compared to two state-of-the-art randomized caches.