Biblio

As social networking sites such as Facebook and Twitter are becoming increasingly popular, a growing number of malicious attacks, such as phishing and malware, are exploiting them. Among these attacks, social botnets have sophisticated infrastructure that leverages compromised user accounts, known as bots, to automate the creation of new social networking accounts for spamming and malware propagation. Traditional defense mechanisms are often passive and reactive to non-zero-day attacks. In this paper, we adopt a proactive approach for enhancing security in social networks by infiltrating botnets with honeybots. We propose an integrated system named SODEXO which can be interfaced with social networking sites for creating deceptive honeybots and leveraging them for gaining information from botnets. We establish a Stackelberg game framework to capture strategic interactions between honeybots and botnets, and use quantitative methods to understand the tradeoffs of honeybots for their deployment and exploitation in social networks. We design a protection and alert system that integrates both microscopic and macroscopic models of honeybots and optimally determines the security strategies for honeybots. We corroborate the proposed mechanism with extensive simulations and comparisons with passive defenses.

The use of a shared medium leaves wireless networks, including mobile ad hoc and sensor networks, vulnerable to jamming attacks. In this paper, we introduce a jamming defense mechanism for multiple-path routing networks based on maintaining deceptive flows, consisting of fake packets, between a source and a destination. An adversary observing a deceptive flow will expend energy on disrupting the fake packets, allowing the real data packets to arrive at the destination unharmed. We model this deceptive flow-based defense within a multi-stage stochastic game framework between the network nodes, which choose a routing path and flow rates for the real and fake data, and an adversary, which chooses which fraction of each flow to target at each hop. We develop an efficient, distributed procedure for computing the optimal routing at each hop and the optimal flow allocation at the destination. Furthermore, by studying the equilibria of the game, we quantify the benefit arising from deception, as reflected in an increase in the valid throughput. Our results are demonstrated via a simulation study.

The Stuxnet worm is a sophisticated malware designed to sabotage industrial control systems (ICSs). It exploits vulnerabilities in removable drives, local area communication networks, and programmable logic controllers (PLCs) to penetrate the process control network (PCN) and the control system network (CSN). Stuxnet was successful in penetrating the control system network and sabotaging industrial control processes since the targeted control systems lacked security mechanisms for verifying message integrity and source authentication. In this work, we propose a novel proactive defense system framework, in which commands from the system operator to the PLC are authenticated using a randomized set of cryptographic keys. The framework leverages cryptographic analysis and controland game-theoretic methods to quantify the impact of malicious commands on the performance of the physical plant. We derive the worst-case optimal randomization strategy as a saddle-point equilibrium of a game between an adversary attempting to insert commands and the system operator, and show that the proposed scheme can achieve arbitrarily low adversary success probability for a sufficiently large number of keys. We evaluate our proposed scheme, using a linear-quadratic regulator (LQR) as a case study, through theoretical and numerical analysis.