Practical Attacks Against Graph-based Clustering

Graph modeling allows numerous security problems to be tackled in a general way, however, little work has been done to understand their ability to withstand adversarial attacks. We design and evaluate two novel graph attacks against a state-of-the-art network-level, graph-based detection system. Our work highlights areas in adversarial machine learning that have not yet been addressed, specifically: graph-based clustering techniques, and a global feature space where realistic attackers without perfect knowledge must be accounted for (by the defenders) in order to be practical. Even though less informed attackers can evade graph clustering with low cost, we show that some practical defenses are possible.Comment: ACM CCS 201

A Network Topology Approach to Bot Classification

Automated social agents, or bots, are increasingly becoming a problem on social media platforms. There is a growing body of literature and multiple tools to aid in the detection of such agents on online social networking platforms. We propose that the social network topology of a user would be sufficient to determine whether the user is a automated agent or a human. To test this, we use a publicly available dataset containing users on Twitter labelled as either automated social agent or human. Using an unsupervised machine learning approach, we obtain a detection accuracy rate of 70%

Mitigating smart card fault injection with link-time code rewriting: a feasibility study

We present a feasibility study to protect smart card software against fault-injection attacks by means of binary code rewriting. We implemented a range of protection techniques in a link-time rewriter and evaluate and discuss the obtained coverage, the associated overhead and engineering effort, as well as its practical usability