Attack Countermeasures Research Articles

A 128-Gbps Pipelined SM4 Circuit With Dual DPA Attack Countermeasures

A 128-Gbps Pipelined SM4 Circuit With Dual DPA Attack Countermeasures

Locking-Enabled Security Analysis of Cryptographic Circuits

Hardware implementations of cryptographic primitives require protection against physical attacks and supply chain threats. This raises the question of secure composability of different attack countermeasures, i.e., whether protecting a circuit against one threat can make it more vulnerable against a different threat. In this article, we study the consequences of applying logic locking, a popular design-for-trust solution against intellectual property piracy and overproduction, to cryptographic circuits. We show that the ability to unlock the circuit incorrectly gives the adversary new powerful attack options. We introduce LEDFA (locking-enabled differential fault analysis) and demonstrate for several ciphers and families of locking schemes that fault attacks become possible (or consistently easier) for incorrectly unlocked circuits. In several cases, logic locking has made circuit implementations prone to classical algebraic attacks with no fault injection needed altogether. We refer to this “zero-fault” version of LEDFA by the term LEDA, investigate its success factors in-depth and propose a countermeasure to protect the logic-locked implementations against LEDA. We also perform test vector leakage assessment (TVLA) of incorrectly unlocked AES implementations to show the effects of logic locking regarding side-channel leakage. Our results indicate that logic locking is not safe to use in cryptographic circuits, making them less rather than more secure.

Empirical analysis of power side-channel leakage of high-level synthesis designed AES circuits

<span>Many internet of things (IoT) devices and integrated circuit (IC) cards have been compromised by side-channel attacks. Power-analysis attacks, which identify the secret key of a cryptographic circuit by analyzing the power traces, are among the most dangerous side-channel attacks. Gen-erally, there is a trade-off between execution time and circuit area. However, the correlation between security and performance has yet to be determined. In this study, we investigate the cor-relation between side-channel attack resistance and performance (execution time and circuit area) of advanced encryption standard (AES) circuits. Eleven AES circuits with different performances are designed by high-level synthesis and logic synthesis. Of the eleven AES circuits, six are circuits with no side-channel attack countermeasures and five are circuits with masking countermeasures. We employ four metrics based on a T-test to evaluate the side-channel attack resistance. The results based on the correlation coefficient show the correlation between side-channel attack resistance and performance. The correlation varies according to four metrics or masking countermeasure. We argue that designers should change their attitudes towards circuit design when considering security.</span>

Real-time detection of stealthy IoT-based cyber-attacks on power distribution systems: A novel anomaly prediction approach

IoT-enabled appliances are essential components in the development of modern smart homes and cities as they offer energy-efficient solutions that bring comfort to customers, resulting in a better quality of life. Despite the advantages, these smart appliances also present new privacy and security challenges that create vulnerabilities in both smart homes and Power Distribution Systems (PDSs), ultimately affecting their efficiency and reliability. Hence, identifying the security risks linked with behind-the-meter (BTM) IoT-enabled appliances is crucial to develop appropriate countermeasures for safeguarding the PDSs. Previous studies have primarily focused on developing countermeasures for attacks on utility-scale domains, while the problem of attack detection has been largely neglected in the literature. This paper fills the gap by presenting a stealthy attack model applied to a group of compromised IoT-enabled appliances manipulating their operation to deteriorate the power quality of a PDS. The presented attack minimizes the chance of getting detected by maintaining the operating conditions of such appliances within the consumers’ comfort levels. Then, a novel real-time anomaly detection and prediction method is proposed based on demand power spectrum analysis and Long-short-term memory (LSTM) neural networks. Further, a Feeder Loading Abnormal Power Spectrum (FLAPS) index is introduced and utilized to estimate the attack onset time, which is then reported to the PDS’s operator for further investigation and action. The evaluation of the stealthy attack model and the anomaly prediction approach is carried out on a residential community modeled using the IEEE-13 bus power distribution feeder, which exhibits various power consumption patterns and appliance operation schedules. The effects of the proposed stealthy attack on the PDS are analyzed quantitatively by examining the occurrences of voltage violations and the wear and tear of the line voltage regulator (LVR) of the system. Numerical results show that compromising only 5% of the PDS’s load, the LVR operates 1.8 more times than during normal conditions. This impact increases to 8 times at 50% compromised load with many voltage violation cases. Also, the simulation results demonstrate the superiority of the proposed prediction technique to detect stealthy attacks 50% faster than threshold-based approaches, thereby enabling the system’s operator to take faster actions.

A comprehensive evaluation approach for efficient countermeasure techniques against timing side-channel attack on MPSoC-based IoT using multi-criteria decision-making methods

ContextTiming side-channel attack countermeasure techniques (TSCA-CTs) evaluation is a multi-criteria decision-making (MCDM) problem based on different MPSoCs of IoT platform architectures, performance, and design overhead. Therefore, the Fermatean by fuzzy decision opinion score method (F-FDOSM) for prioritizing the powerful countermeasure technique against timing side-channel attack is the best approach because it employs the most efficient MCDM ranking technique. Nonetheless, the FDOSM method needs to weigh the criteria before being submitted for the ranking process. In order to address this theoretical challenge, the Criteria-importance through inter-criteria correlation (CRITIC) technique can be applied as an effective MCDM weighting technique to offer an explicit weight for a set of criteria with no inconsistency based on the standard deviation, which uses correlation analysis to determine the relevance of each criterion. ObjectivesThis research proposes a Fermatean-FDOSM framework for evaluating TSCA-CTs in the context of MPSoCs-based IoT and CRITIC techniques to weight the criteria. MethodsThe methodology is presented in three phases. Firstly, a proposed countermeasure techniques dataset was collected that included seven defense approaches (e.g., Gossip and SER router) based on ten criteria (e.g., number of malicious IP cores, number of routers, power, latency… etc.). Then, the decision matrix was built based on an intersection of the countermeasure techniques as an alternative and MPSoC design and performance criteria. Then, the multi-criteria decision-making methods were integrated. The CRITIC method for criteria weighting was followed by the development of the Fermatean-FDOSM method for ranking. Results(1) CRITIC weighting shows that MPSoC NoC throughput (packet/clock) is the highest weight criterion, whereas latency (clock/cycle) is the less weight criterion. (2) The Fermatean-FDOSM-based group ranking shows that the Adaptive routing countermeasure technique is the highest score alternative compared to the Combined with separate interface hybrid approach. (3) The TSCA-CTs priority ranks were subjected to a systematic ranking that was confirmed by strong correlation results throughout ten criterion weight values. A comparison with recent studies confirmed the feasibility of the proposed framework. ConclusionThe outcomes of this study are anticipated to give particular knowledge and direction to those who want to do decision theory-based MPSoCs-based IoT and NoC communication security research.

Practical aspects on non-profiled deep-learning side-channel attacks against AES software implementation with two types of masking countermeasures including RSM

Deep-learning side-channel attacks, applying deep neural networks to side-channel attacks, are known that can easily attack some existing side-channel attack countermeasures such as masking and random jitter. While there have been many studies on profiled deep-learning side-channel attacks, a new approach that involves applying deep learning to non-profiled attacks was proposed in 2018. In our study, we investigate the structure of multi-layer perceptrons and points of interest for non-profiled deep-learning side-channel attacks using the ANSSI database with a masking countermeasure. The results of investigations indicate that it is better to use a simple network model, apply regularization to prevent over-fitting, and select a wide range of power traces that contain side-channel information as the points of interest. We also implemented AES-128 software implementation protected with the Rotating Sboxes Masking countermeasure, which has never been attacked by non-profiled deep-learning side-channel attacks, on the Xmega128 microcontroller and carried out non-profiled deep-learning side-channel attacks against it. Non-profiled deep-learning side-channel attacks successfully recovered all partial keys while the conventional power analysis could not. The attack results also showed that the least significant bit is the adequate selection for successful non-profiled deep-learning side-channel attacks, but the best labeling method may vary depending on the implementation of the countermeasure algorithm. We conducted two experimental analyses to clarify that deep-learning side-channel attacks learn mask values used in the masking countermeasure. One is the gradient visualization used in previous studies, and the other is a new analysis method using partial removal of power traces.

A machine learning low‐dropout regulator‐assisted differential power analysis attack countermeasure with voltage scaling

SummaryFalse key‐controlled aggressive voltage scaling (FKCAVS) technique is a lightweight and effective leakage power analysis (LPA) attack countermeasure. However, the regular FKCAVS technique may not be utilized as a countermeasure against differential power analysis (DPA) attacks unconditionally. The primary reason is that the working frequency of DPA attacks is significantly higher than the corresponding frequency of LPA attacks. Thus, it is difficult to make the speed of voltage scaling keep pace with the speed of DPA attacks by employing the regular FKCAVS technique. In this paper, a fast FKCAVS technique is proposed to maximize the security of a cryptographic circuit (CC) against DPA attacks while minimizing the corresponding overhead by embedding a machine learning low‐dropout (LDO) regulator (MLLR). As shown in the result, by deploying the proposed FKCAVS technique, the measurement‐to‐disclose (MTD) value against DPA attacks is maintained over 1 million with less than 17.4% power/area overhead.

Antispoofing in face biometrics: a comprehensive study on software-based techniques

The vulnerability of the face recognition system to spoofing attacks has piqued the biometric community's interest, motivating them to develop anti-spoofing techniques to secure it. Photo, video, or mask attacks can compromise face biometric systems (types of presentation attacks). Spoofing attacks are detected using liveness detection techniques, which determine whether the facial image presented at a biometric system is a live face or a fake version of it. We discuss the classification of face anti-spoofing techniques in this paper. Anti-spoofing techniques are divided into two categories: hardware and software methods. Hardware-based techniques are summarized briefly. A comprehensive study on software-based countermeasures for presentation attacks is discussed, which are further divided into static and dynamic methods. We cited a few publicly available presentation attack datasets and calculated a few metrics to demonstrate the value of anti-spoofing techniques.

Antispoofing in face biometrics: a comprehensive study on software-based techniques

The vulnerability of the face recognition system to spoofing attacks has piqued the biometric community's interest, motivating them to develop antispoofing techniques to secure it. Photo, video, or mask attacks can compromise face biometric systems (types of presentation attacks). Spoofing attacks are detected using liveness detection techniques, which determine whether the facial image presented at a biometric system is a live face or a fake version of it. We discuss the classification of face anti-spoofing techniques in this paper. Anti-spoofing techniques are divided into two categories: hardware and software methods. Hardware-based techniques are summarized briefly. A comprehensive study on software-based countermeasures for presentation attacks is discussed, which are further divided into static and dynamic methods. We cited a few publicly available presentation attack datasets and calculated a few metrics to demonstrate the value of anti-spoofing techniques.

A Proposal of Shoulder-surfing Attack Countermeasure Method with Improved Usability

A Proposal of Shoulder-surfing Attack Countermeasure Method with Improved Usability

Metaverse intrusion detection of wormhole attacks based on a novel statistical mechanism

The Metaverse shows great potential to facilitate the development of new technologies. Because the security of the Metaverse has attracted considerable attention, the automatic detection of malware in different scenarios related to the Metaverse has become significant as well. A Metaverse-based wireless system comprises various physical and virtual sensing models, and the security between these sensors and nodes should be further considered. A wormhole link is created by two malicious radio transceivers connected by high capacity out-of-band wireless or wired links. Wormhole attacks can easily subvert many network protocols and maliciously collect a large amount of traffic. As many new mobile applications in the Internet of Things (IoT) have emerged recently, the threat posed by wormhole attacks has increased. Accordingly, research efforts have been made to develop countermeasures for wormhole attacks. However, most of them have considered a static network, assuming that a wormhole does not move and that wormhole nodes cannot adaptively turn their radio on or off to avoid being detected. These assumptions limit the use of existing detection methods. Hence, we first study the security impact and characteristics of wormhole attacks in mobile cloud and Metaverse environments and find the possibility of matching statistical methods such as the sequential probability ratio test (SPRT) to detect wormholes. Moreover, in our assumed topology, nodes in the network have mobility. From our investigation results, we attempt to derive a novel defense mechanism design against wormhole attacks.

Deep-Learning-Aided RF Fingerprinting for NFC Relay Attack Detection

Near field communication (NFC) has been a widely used radiofrequency identification (RFID) technology, credited to its convenience and security features. However, the transmitted signals can be easily eavesdropped or relayed in an open wireless channel. One of the challenges is relay attack, where an attacker simply relays the signal and bypasses encryption or other means in the application layer. Prior works on relay attack countermeasures have focused on distance-bounding protocols or ambient-based solutions. This paper focuses on ISO/IEC 14443-A and proposes an NFC relay detection method based on RF fingerprinting of transmitted wireless signals in the physical layer. To this end, we first designed and implemented two realizations of NFC relay attacks, wired and wireless relays, and built an SDR-based testbed. We collected the normal and relayed signals of four NFC tags, and the answer to request type A (ATQA) segments were selected for RF fingerprinting. The created dataset comprised 66,366 samples, with four tags’ normal and wired relayed signals and the wireless relayed signals. The dataset was then fed into a deep CNN for training. Finally, our experiment results showed that the method effectively distinguished normal and relayed signals with a high accuracy of 99%, confirming that RF fingerprinting can be a promising countermeasure to NFC relay attacks.

On Side-Channel Analysis of Memristive Cryptographic Circuits

Memristive technologies offer fascinating opportunities for unconventional computing architectures and emerging applications. While memristive devices have received substantial attention as sources of entropy for security applications, security vulnerabilities of memristive technologies for implementing cryptographic circuits have been largely neglected so far. In this article, we provide the first in-depth analysis of power side-channel analysis against memristive cryptographic implementations based on both: physical experiments and simulations. We show that power consumption models developed for CMOS are not fully adequate for memristive circuits. In particular, the memory effect makes even input-independent initialization cycles vulnerable to attacks that would be fundamentally impossible in CMOS technologies. We propose a memristive-oriented Power Estimation Model (mPEM) integrated into the Stochastic Approach (StA) framework and demonstrate its effectiveness against larger-scale circuits. Finally, we demonstrate that attack countermeasures that were effective for CMOS fail for fundamental reasons in the memristive case.

A Comprehensive Survey of V2X Cybersecurity Mechanisms and Future Research Paths

Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.

Evaluation Approach for Efficient Countermeasure Techniques Against Denial-of-Service Attack on MPSoC-Based IoT Using Multi-Criteria Decision-Making

Context: Denial-of-Service Attack countermeasure techniques (DoS A-CTs) evaluation is a Multi-criteria decision-making (MCDM) problem based on different MPSoCs of IoT platform design, performance, and design overhead. Therefore, the Fermatean by fuzzy decision opinion score method (F-FDOSM) for prioritizing the powerful countermeasure technique against Denial-of-Service (DoS) attack is the best approach because it employs the most efficient MCDM ranking technique. Nonetheless, the FDOSM method needs to weight the criteria before being submitted for the ranking process. In order to address this theoretical challenge, the Criteria-importance through inter-criteria correlation (CRITIC) technique can be applied as an effective MCDM weighting technique to offer an explicit weight for a set of criteria with no inconsistency based on the standard deviation, which uses correlation analysis to determine the relevance of each criterion. Objectives: This research proposes a Fermatean-FDOSM framework for evaluating DoS A-CTs in the context of MPSoCs-based IoT and CRITIC techniques to weight the criteria. Methods: The methodology is presented in three phases. Firstly, a proposed countermeasure techniques dataset was collected that included eighteen defense approaches (e.g., Sniffer, SeRA, and RLAN) based on thirteen criteria (e.g., size, power, latency, and effectiveness…etc.). Then, the Decision matrix (DM) was built based on an intersection of the countermeasure techniques as an alternative and MPSoC design and performance criteria. Then, the multi-criteria decision-making methods were integrated. The CRITIC method for criteria weighting was followed by the development of the Fermatean-FDOSM method for ranking. Results: (1) CRITIC weighting shows that MPSoC NoC Routing Algorithm (XY and YX) is the highest weight criterion, whereas latency (clock/cycle) is the less weight criterion. (2) The Fermatean-FDOSM-based group ranking shows that the Collision Point Router Detection (CPRD) countermeasure technique is the first-ranked alternative compared to the Secure Model Checkers (SMCs) approach. (3) The DoS A-CTs priority ranks were subjected to a systematic ranking that was confirmed by solid correlation results throughout thirteen criterion weight values. A comparison with recent studies confirmed the feasibility of the proposed framework. Conclusion: The results of this research are expected to provide a specific understanding and guide for those who want to engage in MPSoCs-based IoT and NoC communication security research with decision theory.

A Survey of Sybil Attack Countermeasures in Underwater Sensor and Acoustic Networks

A Survey of Sybil Attack Countermeasures in Underwater Sensor and Acoustic Networks

Algebraic Persistent Fault Analysis of SKINNY_64 Based on S_Box Decomposition

Algebraic persistent fault analysis (APFA), which combines algebraic analysis with persistent fault attacks, brings new challenges to the security of lightweight block ciphers and has received widespread attention since its introduction. Threshold Implementation (TI) is one of the most widely used countermeasures for side channel attacks. Inspired by this method, the SKINNY block cipher adopts the S_box decomposition to reduce the number of variables in the set of algebraic equations and the number of Conjunctive Normal Form (CNF) equations in this paper, thus speeding up the algebraic persistent fault analysis and reducing the number of fault ciphertexts. In our study, we firstly establish algebraic equations for full-round faulty encryption, and then analyze the relationship between the number of fault ciphertexts required and the solving time in different scenarios (decomposed S_boxes and original S_box). By comparing the two sets of experimental results, the success rate and the efficiency of the attack are greatly improved by using S_box decomposition. In this paper, We can recover the master key in a minimum of 2000s using 11 pairs of plaintext and fault ciphertext, while the key recovery cannot be done in effective time using the original S_box expression equations. At the same time, we apply S_box decomposition to another kind of algebraic persistent fault analysis, and the experimental results show that using S_box decomposition can effectively reduce the solving time and solving success rate under the same conditions.

Retraction Note to: Design and implementation of hybrid integration of cognitive learning and chaotic countermeasures for side channel attacks

Retraction Note to: Design and implementation of hybrid integration of cognitive learning and chaotic countermeasures for side channel attacks

IoT Security on Smart Grid: Threats and Mitigation Frameworks

The number of smart grid IoTs is expected to grow exponentially in the near future, as predicted by the analyst. The improvement of a smart grid IoT security system increases due to communication technologies used in traditional power systems. Smart grid IoTs include critical devices due to its complex architectures. Smart grid IoTs that are connected and controlled remotely through the internet are becoming more universal. As a result, homes and businesses have ever increasing attack surfaces on their networks. It can lead to security arrears, and large-scale economic damage when the confidentiality and integrity of the communication is broken down. These huge systems may be endangered to threats. Consequently, there is a lot of research effort to enhance smart grid security in government, industry, and academia. We present a broad survey supported by a thorough review of earlier work. Moreover, recent advances and corrective measures are presented on smart grid IoT security. In this paper, the threats and mitigation framework of the smart grid IoT are analyzed. This paper reviews the existing literature on the smart grid IoTs in energy systems. We focus on threat types and provide an in-depth discussion of the threat state of the smart grid. Explicitly, we focus on the discussion and scrutiny of network vulnerabilities, attack countermeasures, and security requirements. We focus to supply a deep understanding of threat vulnerabilities and mitigation framework and give a path on future research directions for threat in smart grid IoTs.

Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning

Deep reinforcement learning (DRL) has numerous applications in the real world, thanks to its ability to achieve high performance in a range of environments with little manual oversight. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life critical systems and applications (e.g., smart grids, traffic controls, and autonomous vehicles) unless its vulnerabilities are addressed and mitigated. To address this problem, we provide a comprehensive survey that discusses emerging attacks on DRL-based systems and the potential countermeasures to defend against these attacks. We first review the fundamental background on DRL and present emerging adversarial attacks on machine learning techniques. We then investigate the vulnerabilities that an adversary can exploit to attack DRL along with state-of-the-art countermeasures to prevent such attacks. Finally, we highlight open issues and research challenges for developing solutions to deal with attacks on DRL-based intelligent systems.