Poly-Logarithmic Side Channel Rank Estimation via Exponential Sampling

Rank estimation is an important tool for a side-channel evaluations laboratories. It allows estimating the remaining security after an attack has been performed, quantified as the time complexity and the memory consumption required to brute force the key given the leakages as probability distributions over $d$ subkeys (usually key bytes). These estimations are particularly useful where the key is not reachable with exhaustive search. We propose ESrank, the first rank estimation algorithm that enjoys provable poly-logarithmic time- and space-complexity, which also achieves excellent practical performance. Our main idea is to use exponential sampling to drastically reduce the algorithm\u27s complexity. Importantly, ESrank is simple to build from scratch, and requires no algorithmic tools beyond a sorting function. After rigorously bounding the accuracy, time and space complexities, we evaluated the performance of ESrank on a real SCA data corpus, and compared it to the currently-best histogram-based algorithm. We show that ESrank gives excellent rank estimation (with roughly a 1-bit margin between lower and upper bounds), with a performance that is on-par with the Histogram algorithm: a run-time of under 1 second on a standard laptop using 6.5 MB RAM

Rank Estimation with Bounded Error via Exponential Sampling

Efficient rank estimation algorithms are of prime interest in security evaluation against side-channel attacks (SCA) and recently also for password strength estimators. In a side channel setting it allows estimating the remaining security after an attack has been performed, quantified as the time complexity and the memory consumption required to brute force the key given the leakages as probability distributions over $d$ subkeys (usually key bytes). In password strength estimators the rank estimation allows estimating how many attempts a password cracker would need until it finds a given password. We propose ESrank, the first rank estimation algorithm with a bounded error ratio: its error ratio is bounded by $\gamma^{2d-2}$, for any probability distribution, where $d$ is the number of subkey dimensions and $\gamma>1$ can be chosen according to the desired accuracy. ESrank is also the first rank estimation algorithm that enjoys provable poly-logarithmic time- and space-complexity. Our main idea is to use exponential sampling to drastically reduce the algorithm\u27s complexity. We evaluated the performance of ESrank on real SCA and password strength corpora. We show ESrank gives excellent rank estimation with roughly a 1-bit margin between lower and upper bounds in less than 1 second on the SCA corpus and 4 seconds preprocessing time and 7$\mu$sec lookup time on the password strength corpus

The Side-Channel Metrics Cheat Sheet

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

A Computer Network Model for the Evaluation of Moving Target Network Defense Mechanisms

In order to combat the increasing complexity of cyber attacks, a new category of cyber defense called dynamic cyber defense has been the focus of a significant amount of work. Dynamic cyber defense mechanisms aim to protect networks by modifying their attributes in order to confuse would-be attackers. Currently, the majority of the existing mechanisms are purely theoretical and have been the subject of minimal performance analysis. There has also been almost no effort to perform comparative analysis of different techniques. As a result, there is a great need for a method of modeling different mechanisms within a single system in order to conduct comprehensive, comparative performance analysis. This work develops the framework of a system called Dynamic Virtual Terrain (DVT), which can be used for comparative analysis of dynamic cyber defense mechanisms under identical conditions. DVT models network topology using nodes, which represent members of a network, and access permissions, which describe the connectivity of the network. DVT also defines a generic dynamic cyber defense algorithm that can be extended in order to implement a hierarchy of techniques. An implementation of DVT is created in order to perform experiments with IP address hopping, port hopping, and dynamic firewall mechanisms in a cyber attack simulation environment. Attack scenarios are developed to evaluate the performance of the mechanisms under identical conditions, and the results of simulating these scenarios are used to analyze the performance of the implemented mechanisms

CoTree: Push the Limits of Conquerable Space in Collision-Optimized Side-Channel Attacks

By introducing collision information into side-channel distinguishers, the existing collision-optimized attacks exploit collision detection algorithm to transform the original candidate space under consideration into a significantly smaller collision chain space, thus achieving more efficient key recovery. However, collision information is detected very repeatedly since collision chains are created from the same sub-chains, i.e., with the same candidates on their first several sub-keys. This aggravates when exploiting more collision information. The existing collision detection algorithms try to alleviate this, but the problem is still very serious. In this paper, we propose a highly-efficient detection algorithm named Collision Tree (CoTree) for collision-optimized attacks. CoTree exploits tree structure to store the chains creating from the same sub-chain on the same branch. It then exploits a top-down tree building procedure and traverses each node only once when detecting their collisions with a candidate of the sub-key currently under consideration. Finally, it launches a bottom-up branch removal procedure to remove the chains unsatisfying the collision conditions from the tree after traversing all candidates (within given threshold) of this sub-key, thus avoiding the traversal of the branches satisfying the collision condition. These strategies make our CoTree significantly alleviate the repetitive collision detection, and our experiments verify that it significantly outperforms the existing works

The Science of Guessing in Collision Optimized Divide-and-Conquer Attacks

Recovering keys ranked in very deep candidate space efficiently is a very important but challenging issue in Side-Channel Attacks (SCAs). State-of-the-art Collision Optimized Divide-and-Conquer Attacks (CODCAs) extract collision information from a collision attack to optimize the key recovery of a divide-and-conquer attack, and transform the very huge guessing space to a much smaller collision space. However, the inefficient collision detection makes them time-consuming. The very limited collisions exploited and large performance difference between the collision attack and the divide-and-conquer attack in CODCAs also prevent their application in much larger spaces. In this paper, we propose a Minkowski Distance enhanced Collision Attack (MDCA) with performance closer to Template Attack (TA) compared to traditional Correlation-Enhanced Collision Attack (CECA), thus making the optimization more practical and meaningful. Next, we build a more advanced CODCA named Full-Collision Chain (FCC) from TA and MDCA to exploit all collisions. Moreover, to minimize the thresholds while guaranteeing a high success probability of key recovery, we propose a fault-tolerant scheme to optimize FCC. The full-key is divided into several big ``blocks\u27\u27, on which a Fault-Tolerant Vector (FTV) is exploited to flexibly adjust its chain space. Finally, guessing theory is exploited to optimize thresholds determination and search orders of sub-keys. Experimental results show that FCC notably outperforms the existing CODCAs

Security Technologies and Methods for Advanced Cyber Threat Intelligence, Detection and Mitigation

The rapid growth of the Internet interconnectivity and complexity of communication systems has led us to a significant growth of cyberattacks globally often with severe and disastrous consequences. The swift development of more innovative and effective (cyber)security solutions and approaches are vital which can detect, mitigate and prevent from these serious consequences. Cybersecurity is gaining momentum and is scaling up in very many areas. This book builds on the experience of the Cyber-Trust EU project’s methods, use cases, technology development, testing and validation and extends into a broader science, lead IT industry market and applied research with practical cases. It offers new perspectives on advanced (cyber) security innovation (eco) systems covering key different perspectives. The book provides insights on new security technologies and methods for advanced cyber threat intelligence, detection and mitigation. We cover topics such as cyber-security and AI, cyber-threat intelligence, digital forensics, moving target defense, intrusion detection systems, post-quantum security, privacy and data protection, security visualization, smart contracts security, software security, blockchain, security architectures, system and data integrity, trust management systems, distributed systems security, dynamic risk management, privacy and ethics