Advances in Information Security and Privacy

With the recent pandemic emergency, many people are spending their days in smart working and have increased their use of digital resources for both work and entertainment. The result is that the amount of digital information handled online is dramatically increased, and we can observe a significant increase in the number of attacks, breaches, and hacks. This Special Issue aims to establish the state of the art in protecting information by mitigating information risks. This objective is reached by presenting both surveys on specific topics and original approaches and solutions to specific problems. In total, 16 papers have been published in this Special Issue

Side-Channel Watchdog: Run-Time Evaluation of Side-Channel Vulnerability in FPGA-Based Crypto-systems

Besides security against classical cryptanalysis, its important for cryptographic implementations to have sufficient robustness against side-channel attacks. Many countermeasures have been proposed to thwart side channel attacks, especially power trace measurement based side channel attacks. Additionally, researchers have proposed several evaluation metrics to evaluate side channel security of crypto-system. However, evaluation of any crypto-system is done during the testing phase and is not part of the actual hardware. In our approach, we propose to implement such evaluation metrics on-chip for run-time side channel vulnerability estimation of a cryptosystem. The objective is to create a watchdog on the hardware which will monitor the side channel leakage of the device, and will alert the user if that leakage crosses a pre-determined threshold, beyond which the system might be considered vulnerable. Once such alert signal is activated, proactive countermeasures can be activated either at the device level or at the protocol level, to prevent the impending side channel attack. A FPGA based prototype designed by us show low hardware overhead, and is an effective option that avoids the use of bulky and inconvenient on-field measurement setup

A Key to Success -- Success Exponents for Side-Channel Distinguishers

The success rate is the classical metric for evaluating the performance of side-channel attacks. It is generally computed empirically from measurements for a particular device or using simulations. Closed-form expressions of success rate are desirable because they provide an explicit functional dependence on relevant parameters such as number of measurements and signal-to-noise ratio which help to understand the effectiveness of a given attack and how one can mitigate its threat by countermeasures. However, such closed-form expressions involve high-dimensional complex statistical functions that are hard to estimate. In this paper, we define the success exponent (SE) of an arbitrary side-channel distinguisher as the first-order exponent of the success rate as the number of measurements increases. Under fairly general assumptions such as soundness, we give a general simple formula for any arbitrary distinguisher and derive closed-form expressions of it for DoM, CPA, MIA and the optimal distinguisher when the model is known (template attack). For DoM and CPA our results are in line with the literature. Experiments confirm that the theoretical closed-form expression of the SE coincides with the empirically computed one, even for reasonably small numbers of measurements. Finally, we highlight that our study raises many new perspectives for comparing and evaluating side-channel attacks, countermeasures and implementations

On Evaluating Fault Resilient Encoding Schemes in Software

Cryptographic implementations are often vulnerable against physical attacks, fault injection analysis being among the most popular techniques. On par with development of attacks, the area of countermeasures is advancing rapidly, utilizing both hardware- and software-based approaches. When it comes to software encoding countermeasures for fault protection and their evaluation, there are very few proposals so far, mostly focusing on single operations rather than cipher as a whole. In this paper we propose an evaluation framework that can be used for analyzing the effectivity of software encoding countermeasures against fault attacks. We first formalize the encoding schemes in software, helping us to define what properties are required when designing a fault protection. Based on these findings, we develop an evaluation metric that can be used universally to determine the robustness of a software encoding scheme against bit flip faults and instruction skips. We provide a way to select a code according to user criteria and also a dynamic code analysis method to estimate the level of protection of assembly implementations using encoding schemes. Finally, we verify our findings by implementing a block cipher PRESENT, protected by encoding scheme based on anticodes, and provide a detailed evaluation of this implementation using different codes

Functionality-preserving adversarial machine learning for robust classification in cybersecurity and intrusion detection domains: A survey

Machine learning has become widely adopted as a strategy for dealing with a variety of cybersecurity issues, ranging from insider threat detection to intrusion and malware detection. However, by their very nature, machine learning systems can introduce vulnerabilities to a security defence whereby a learnt model is unaware of so-called adversarial examples that may intentionally result in mis-classification and therefore bypass a system. Adversarial machine learning has been a research topic for over a decade and is now an accepted but open problem. Much of the early research on adversarial examples has addressed issues related to computer vision, yet as machine learning continues to be adopted in other domains, then likewise it is important to assess the potential vulnerabilities that may occur. A key part of transferring to new domains relates to functionality-preservation, such that any crafted attack can still execute the original intended functionality when inspected by a human and/or a machine. In this literature survey, our main objective is to address the domain of adversarial machine learning attacks and examine the robustness of machine learning models in the cybersecurity and intrusion detection domains. We identify the key trends in current work observed in the literature, and explore how these relate to the research challenges that remain open for future works. Inclusion criteria were: articles related to functionality-preservation in adversarial machine learning for cybersecurity or intrusion detection with insight into robust classification. Generally, we excluded works that are not yet peer-reviewed; however, we included some significant papers that make a clear contribution to the domain. There is a risk of subjective bias in the selection of non-peer reviewed articles; however, this was mitigated by co-author review. We selected the following databases with a sizeable computer science element to search and retrieve literature: IEEE Xplore, ACM Digital Library, ScienceDirect, Scopus, SpringerLink, and Google Scholar. The literature search was conducted up to January 2022. We have striven to ensure a comprehensive coverage of the domain to the best of our knowledge. We have performed systematic searches of the literature, noting our search terms and results, and following up on all materials that appear relevant and fit within the topic domains of this review. This research was funded by the Partnership PhD scheme at the University of the West of England in collaboration with Techmodal Ltd

Side-Channel Analysis and Cryptography Engineering : Getting OpenSSL Closer to Constant-Time

As side-channel attacks reached general purpose PCs and started to be more practical for attackers to exploit, OpenSSL adopted in 2005 a flagging mechanism to protect against SCA. The opt-in mechanism allows to flag secret values, such as keys, with the BN_FLG_CONSTTIME flag. Whenever a flag is checked and detected, the library changes its execution flow to SCA-secure functions that are slower but safer, protecting these secret values from being leaked. This mechanism favors performance over security, it is error-prone, and is obscure for most library developers, increasing the potential for side-channel vulnerabilities. This dissertation presents an extensive side-channel analysis of OpenSSL and criticizes its fragile flagging mechanism. This analysis reveals several flaws affecting the library resulting in multiple side-channel attacks, improved cache-timing attack techniques, and a new side channel vector. The first part of this dissertation introduces the main topic and the necessary related work, including the microarchitecture, the cache hierarchy, and attack techniques; then it presents a brief troubled history of side-channel attacks and defenses in OpenSSL, setting the stage for the related publications. This dissertation includes seven original publications contributing to the area of side-channel analysis, microarchitecture timing attacks, and applied cryptography. From an SCA perspective, the results identify several vulnerabilities and flaws enabling protocol-level attacks on RSA, DSA, and ECDSA, in addition to full SCA of the SM2 cryptosystem. With respect to microarchitecture timing attacks, the dissertation presents a new side-channel vector due to port contention in the CPU execution units. And finally, on the applied cryptography front, OpenSSL now enjoys a revamped code base securing several cryptosystems against SCA, favoring a secure-by-default protection against side-channel attacks, instead of the insecure opt-in flagging mechanism provided by the fragile BN_FLG_CONSTTIME flag