Biblio

The ubiquitous nature of the Internet of Things (IoT) devices and their wide-scale deployment have remarkably attracted hackers to exploit weakly-configured and vulnerable devices, allowing them to form large IoT botnets and launch unprecedented attacks. Modeling the behavior of IoT botnets leads to a better understanding of their spreading mechanisms and the state of the network at different levels of the attack. In this paper, we propose a generic model to capture the behavior of IoT botnets. The proposed model uses Markov Chains to study the botnet behavior. Discrete Event System Specifications environment is used to simulate the proposed model.

This paper is concerned with the stabilization problem of cyber physical system (CPS) exposed to a random replay attack. The study will ignore the effects of communication delays and packet losses, and the attention will be focused on the effect of replay attack on the stability of (CPS). The closed-loop system is modeled as Markovian jump linear system with two jumping parameters. Linear matrix inequality (LMI) formulation is used to give a condition for stochastic stabilization of the system. Finally the theory is illustrated through a numerical example.

In recent years, Moving Target Defense (MTD) has emerged as a potential game changer in the security landscape, due to its potential to create asymmetric uncertainty that favors the defender. Many different MTD techniques have then been proposed, each addressing an often very specific set of attack vectors. Despite the huge progress made in this area, there are still some critical gaps with respect to the analysis and quantification of the cost and benefits of deploying MTD techniques. In fact, common metrics to assess the performance of these techniques are still lacking and most of them tend to assess their performance in different and often incompatible ways. This paper addresses these gaps by proposing a quantitative analytic model for assessing the resource availability and performance of MTDs, and a method for the determination of the highest possible reconfiguration rate, and thus smallest probability of attacker's success, that meets performance and stability constraints. Finally, we present an experimental validation of the proposed approach.

Machine learning has been widely used and achieved considerable results in various research areas. On the other hand, machine learning becomes a big threat when malicious attackers make use it for the wrong purpose. As such a threat, self-evolving botnets have been considered in the past. The self-evolving botnets autonomously predict vulnerabilities by implementing machine learning with computing resources of zombie computers. Furthermore, they evolve based on the vulnerability, and thus have high infectivity. In this paper, we consider several models of Markov chains to counter the spreading of the self-evolving botnets. Through simulation experiments, this paper shows the behaviors of these models.