Biblio

In this chapter, we develop a defense strategy to secure Internet of Battlefield Things (IoBT) based on a hypergame employing deceptive techniques. The hypergame is played multiple rounds. At each round, the adversary updates its perception of the attack graph and chooses the next node to compromise. The defender updates its perceived list of compromised nodes and actively feeds false signals to the adversary to create deception. The hypergame developed in this chapter provides an important theoretical framework for us to model how a cyberattack spreads on a network and the interaction between the adversary and the defender. It also provides quantitative metrics such as the time it takes the adversary to explore the network and compromise the target nodes. Based on these metrics, the defender can reboot the network devices and reset the network topology in time to clean up all potentially compromised devices and to protect the critical nodes. The hypergame provides useful guidance on how to create cyber deceptions so that the adversary cannot obtain information about the correct network topology and can be deterred from reaching the target critical nodes on a military network while it is in service.

As more and more cyber security incident data ranging from systems logs to vulnerability scan results are collected, manually analyzing these collected data to detect important cyber security events become impossible. Hence, data mining techniques are becoming an essential tool for real-world cyber security applications. For example, a report from Gartner [gartner12] claims that "Information security is becoming a big data analytics problem, where massive amounts of data will be correlated, analyzed and mined for meaningful patterns". Of course, data mining/analytics is a means to an end where the ultimate goal is to provide cyber security analysts with prioritized actionable insights derived from big data. This raises the question, can we directly apply existing techniques to cyber security applications? One of the most important differences between data mining for cyber security and many other data mining applications is the existence of malicious adversaries that continuously adapt their behavior to hide their actions and to make the data mining models ineffective. Unfortunately, traditional data mining techniques are insufficient to handle such adversarial problems directly. The adversaries adapt to the data miner's reactions, and data mining algorithms constructed based on a training dataset degrades quickly. To address these concerns, over the last couple of years new and novel data mining techniques which is more resilient to such adversarial behavior are being developed in machine learning and data mining community. We believe that lessons learned as a part of this research direction would be beneficial for cyber security researchers who are increasingly applying machine learning and data mining techniques in practice. To give an overview of recent developments in adversarial data mining, in this three hour long tutorial, we introduce the foundations, the techniques, and the applications of adversarial data mining to cyber security applications. We first introduce various approaches proposed in the past to defend against active adversaries, such as a minimax approach to minimize the worst case error through a zero-sum game. We then discuss a game theoretic framework to model the sequential actions of the adversary and the data miner, while both parties try to maximize their utilities. We also introduce a modified support vector machine method and a relevance vector machine method to defend against active adversaries. Intrusion detection and malware detection are two important application areas for adversarial data mining models that will be discussed in details during the tutorial. Finally, we discuss some practical guidelines on how to use adversarial data mining ideas in generic cyber security applications and how to leverage existing big data management tools for building data mining algorithms for cyber security.