Biblio

With the rapid development of sophisticated attack techniques, individual security systems that base all of their decisions and actions of attack prevention and response on their own observations and knowledge become incompetent. To cope with this problem, collaborative security in which a set of security entities are coordinated to perform specific security actions is proposed in literature. In collaborative security schemes, multiple entities collaborate with each other by sharing threat evidence or analytics to make more effective decisions. Nevertheless, the anticipated information exchange raises privacy concerns, especially for those privacy-sensitive entities. In order to obtain a quantitative understanding of the fundamental tradeoff between the effectiveness of collaboration and the entities' privacy, a repeated two-layer single-leader multi-follower game is proposed in this work. Based on our game-theoretic analysis, the expected behaviors of both the attacker and the security entities are derived and the utility-privacy tradeoff curve is obtained. In addition, the existence of Nash equilibrium (NE) for the collaborative entities is proven, and an asynchronous dynamic update algorithm is proposed to compute the optimal collaboration strategies of the entities. Furthermore, the existence of Byzantine entities is considered and its influence is investigated. Finally, simulation results are presented to validate the analysis.

Botnets have long been used for malicious purposes with huge economic costs to the society. With the proliferation of cheap but non-secure Internet-of-Things (IoT) devices generating large amounts of data, the potential for damage from botnets has increased manifold. There are several approaches to detect bots or botnets, though many traditional techniques are becoming less effective as botnets with centralized command & control structure are being replaced by peer-to-peer (P2P) botnets which are harder to detect. Several algorithms have been proposed in literature that use graph analysis or machine learning techniques to detect the overlay structure of P2P networks in communication graphs. Many of these algorithms however, depend on the availability of a universal communication graph or a communication graph aggregated from several ISPs, which is not likely to be available in reality. In real world deployments, significant gaps in communication graphs are expected and any solution proposed should be able to work with partial information. In this paper, we analyze the effectiveness of some community detection algorithms in detecting P2P botnets, especially with partial information. We show that the approach can work with only about half of the nodes reporting their communication graphs, with only small increase in detection errors.