An Evolutionary Approach for Learning Attack Specifications in Network Graphs

This paper presents an evolutionary algorithm that learns attack scenarios, called attack specifications, from a network graph. This learning process aims to find attack specifications that minimise cost and maximise the value that an attacker gets from a successful attack. The attack specifications that the algorithm learns are represented using an approach based on Hoare's CSP (Communicating Sequential Processes). This new approach is able to represent several elements found in attacks, for example synchronisation. These attack specifications can be used by network administrators to find vulnerable scenarios, composed from the basic constructs Sequence, Parallel and Choice, that lead to valuable assets in the network

Sequential Circuit Design for Embedded Cryptographic Applications Resilient to Adversarial Faults

In the relatively young field of fault-tolerant cryptography, the main research effort has focused exclusively on the protection of the data path of cryptographic circuits. To date, however, we have not found any work that aims at protecting the control logic of these circuits against fault attacks, which thus remains the proverbial Achilles’ heel. Motivated by a hypothetical yet realistic fault analysis attack that, in principle, could be mounted against any modular exponentiation engine, even one with appropriate data path protection, we set out to close this remaining gap. In this paper, we present guidelines for the design of multifault-resilient sequential control logic based on standard Error-Detecting Codes (EDCs) with large minimum distance. We introduce a metric that measures the effectiveness of the error detection technique in terms of the effort the attacker has to make in relation to the area overhead spent in implementing the EDC. Our comparison shows that the proposed EDC-based technique provides superior performance when compared against regular N-modular redundancy techniques. Furthermore, our technique scales well and does not affect the critical path delay

An Evasion and Counter-Evasion Study in Malicious Websites Detection

Malicious websites are a major cyber attack vector, and effective detection of them is an important cyber defense task. The main defense paradigm in this regard is that the defender uses some kind of machine learning algorithms to train a detection model, which is then used to classify websites in question. Unlike other settings, the following issue is inherent to the problem of malicious websites detection: the attacker essentially has access to the same data that the defender uses to train its detection models. This 'symmetry' can be exploited by the attacker, at least in principle, to evade the defender's detection models. In this paper, we present a framework for characterizing the evasion and counter-evasion interactions between the attacker and the defender, where the attacker attempts to evade the defender's detection models by taking advantage of this symmetry. Within this framework, we show that an adaptive attacker can make malicious websites evade powerful detection models, but proactive training can be an effective counter-evasion defense mechanism. The framework is geared toward the popular detection model of decision tree, but can be adapted to accommodate other classifiers