Side-channel analysis of the modular inversion step in the RSA key generation algorithm

This paper studies the security of the RSA key generation algorithm with regard to side-channel analysis and presents a novel approach that targets the simple power analysis (SPA) vulnerabilities that may exist in an implementation of the binary extended Euclidean algorithm (BEEA). The SPA vulnerabilities described, together with the properties of the values processed by the BEEA in the context of RSA key generation, represent a serious threat for an implementation of this algorithm. It is shown that an adversary can disclose the private key employing only one power trace with a success rate of 100 % – an improvement on the 25% success rate achieved by the best side-channel analysis carried out on this algorithm. Two very different BEEA implementations are analyzed, showing how the algorithm’s SPA leakages could be exploited. Also, two countermeasures are discussed that could be used to reduce those SPA leakages and prevent the recovery of the RSA private keyPeer reviewe

Side-channel analysis of the modular inversion step in the RSA key generation algorithm

This paper studies the security of the RSA key generation algorithm with regard to side-channel analysis and presents a novel approach that targets the simple power analysis (SPA) vulnerabilities that may exist in an implementation of the binary extended Euclidean algorithm (BEEA). The SPA vulnerabilities described, together with the properties of the values processed by the BEEA in the context of RSA key generation, represent a serious threat for an implementation of this algorithm. It is shown that an adversary can disclose the private key employing only one power trace with a success rate of 100 % – an improvement on the 25% success rate achieved by the best side-channel analysis carried out on this algorithm. Two very different BEEA implementations are analyzed, showing how the algorithm’s SPA leakages could be exploited. Also, two countermeasures are discussed that could be used to reduce those SPA leakages and prevent the recovery of the RSA private keyGobierno de España TEC2014-57971-R, RTC-2014-2932-

FPGA IMPLEMENTATION FOR ELLIPTIC CURVE CRYPTOGRAPHY OVER BINARY EXTENSION FIELD

Elliptic curve cryptography plays a crucial role in network and communication security. However, implementation of elliptic curve cryptography, especially the implementation of scalar multiplication on an elliptic curve, faces multiple challenges. One of the main challenges is side channel attacks (SCAs). SCAs pose a real threat to the conventional implementations of scalar multiplication such as binary methods (also called doubling-and-add methods). Several scalar multiplication algorithms with countermeasures against side channel attacks have been proposed. Among them, Montgomery Powering Ladder (MPL) has been shown an effective countermeasure against simple power analysis. However, MPL is still vulnerable to certain more sophisticated side channel attacks. A recently proposed modified MPL utilizes a combination of sequence masking (SM), exponent splitting (ES) and point randomization (PR). And it has shown to be one of the best countermeasure algorithms that are immune to many sophisticated side channel attacks [11]. In this thesis, an efficient hardware architecture for this algorithm is proposed and its FPGA implementation is also presented. To our best knowledge, this is the first time that this modified MPL with SM, ES, and PR has been implemented in hardware

Private and Public-Key Side-Channel Threats Against Hardware Accelerated Cryptosystems

Modern side-channel attacks (SCA) have the ability to reveal sensitive data from non-protected hardware implementations of cryptographic accelerators whether they be private or public-key systems. These protocols include but are not limited to symmetric, private-key encryption using AES-128, 192, 256, or public-key cryptosystems using elliptic curve cryptography (ECC). Traditionally, scalar point (SP) operations are compelled to be high-speed at any cost to reduce point multiplication latency. The majority of high-speed architectures of contemporary elliptic curve protocols rely on non-secure SP algorithms. This thesis delivers a novel design, analysis, and successful results from a custom differential power analysis attack on AES-128. The resulting SCA can break any 16-byte master key the sophisticated cipher uses and it\u27s direct applications towards public-key cryptosystems will become clear. Further, the architecture of a SCA resistant scalar point algorithm accompanied by an implementation of an optimized serial multiplier will be constructed. The optimized hardware design of the multiplier is highly modular and can use either NIST approved 233 & 283-bit Kobliz curves utilizing a polynomial basis. The proposed architecture will be implemented on Kintex-7 FPGA to later be integrated with the ARM Cortex-A9 processor on the Zynq-7000 AP SoC (XC7Z045) for seamless data transfer and analysis of the vulnerabilities SCAs can exploit

Similar operation template attack on RSA-CRT as a case study

A template attack, the most powerful side-channel attack methods, usually first builds the leakage profiles from a controlled profiling device, and then uses these profiles to recover the secret of the target device. It is based on the fact that the profiling device shares similar leakage characteristics with the target device. In this study, we focus on the similar operations in a single device and propose a new variant of the template attack, called the similar operation template attack (SOTA). SOTA builds the models on public variables (e.g., input/output) and recovers the values of the secret variables that leak similar to the public variables. SOTA’s advantage is that it can avoid the requirement of an additional profiling device. In this study, the proposed SOTA method is applied to a straightforward RSA-CRT implementation. Because the leakage is (almost) the same in similar operations, we reduce the security of RSA-CRT to a hidden multiplier problem (HMP) over GF(q), which can be solved byte-wise using our proposed heuristic algorithm. The effectiveness of our proposed method is verified as an entire prime recovery procedure in a practical leakage scenario

Deep Learning -Powered Computational Intelligence for Cyber-Attacks Detection and Mitigation in 5G-Enabled Electric Vehicle Charging Station

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has various cyber-attack vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. Therefore, proactively monitoring, detecting, and defending against these attacks is very important. The state-of-the-art approaches are not agile and intelligent enough to detect, mitigate, and defend against various cyber-physical attacks in the EVCS system. To overcome these limitations, this dissertation primarily designs, develops, implements, and tests the data-driven deep learning-powered computational intelligence to detect and mitigate cyber-physical attacks at the network and physical layers of 5G-enabled EVCS infrastructure. Also, the 5G slicing application to ensure the security and service level agreement (SLA) in the EVCS ecosystem has been studied. Various cyber-attacks such as distributed denial of services (DDoS), False data injection (FDI), advanced persistent threats (APT), and ransomware attacks on the network in a standalone 5G-enabled EVCS environment have been considered. Mathematical models for the mentioned cyber-attacks have been developed. The impact of cyber-attacks on the EVCS operation has been analyzed. Various deep learning-powered intrusion detection systems have been proposed to detect attacks using local electrical and network fingerprints. Furthermore, a novel detection framework has been designed and developed to deal with ransomware threats in high-speed, high-dimensional, multimodal data and assets from eccentric stakeholders of the connected automated vehicle (CAV) ecosystem. To mitigate the adverse effects of cyber-attacks on EVCS controllers, novel data-driven digital clones based on Twin Delayed Deep Deterministic Policy Gradient (TD3) Deep Reinforcement Learning (DRL) has been developed. Also, various Bruteforce, Controller clones-based methods have been devised and tested to aid the defense and mitigation of the impact of the attacks of the EVCS operation. The performance of the proposed mitigation method has been compared with that of a benchmark Deep Deterministic Policy Gradient (DDPG)-based digital clones approach. Simulation results obtained from the Python, Matlab/Simulink, and NetSim software demonstrate that the cyber-attacks are disruptive and detrimental to the operation of EVCS. The proposed detection and mitigation methods are effective and perform better than the conventional and benchmark techniques for the 5G-enabled EVCS

GNSS Vulnerabilities and Existing Solutions:A Review of the Literature

This literature review paper focuses on existing vulnerabilities associated with global navigation satellite systems (GNSSs). With respect to the civilian/non encrypted GNSSs, they are employed for proving positioning, navigation and timing (PNT) solutions across a wide range of industries. Some of these include electric power grids, stock exchange systems, cellular communications, agriculture, unmanned aerial systems and intelligent transportation systems. In this survey paper, physical degradations, existing threats and solutions adopted in academia and industry are presented. In regards to GNSS threats, jamming and spoofing attacks as well as detection techniques adopted in the literature are surveyed and summarized. Also discussed are multipath propagation in GNSS and non line-of-sight (NLoS) detection techniques. The review also identifies and discusses open research areas and techniques which can be investigated for the purpose of enhancing the robustness of GNSS