Biblio

Large scale cloud networks consist of distributed networking and computing elements that process critical information and thus security is a key requirement for any environment. Unfortunately, assessing the security state of such networks is a challenging task and the tools used in the past by security experts such as packet filtering, firewall, Intrusion Detection Systems (IDS) etc., provide a reactive security mechanism. In this paper, we introduce a Moving Target Defense (MTD) based proactive security framework for monitoring attacks which lets us identify and reason about multi-stage attacks that target software vulnerabilities present in a cloud network. We formulate the multi-stage attack scenario as a two-player zero-sum Markov Game (between the attacker and the network administrator) on attack graphs. The rewards and transition probabilities are obtained by leveraging the expert knowledge present in the Common Vulnerability Scoring System (CVSS). Our framework identifies an attacker's optimal policy and places countermeasures to ensure that this attack policy is always detected, thus forcing the attacker to use a sub-optimal policy with higher cost.

The Supervisory Control and Data Acquisition (SCADA) system is the most commonly used industrial control system but is subject to a wide range of serious threats. Intrusion detection systems are deployed to promote the security of SCADA systems, but they continuously generate tremendous number of alerts without further comprehending them. There is a need for an efficient system to correlate alerts and discover attack strategies to provide explainable situational awareness to SCADA operators. In this paper, we present a causal-polytree-based anomaly reasoning framework for SCADA networks, named CAPTAR. CAPTAR takes the meta-alerts from our previous anomaly detection framework EDMAND, correlates the them using a naive Bayes classifier, and matches them to predefined causal polytrees. Utilizing Bayesian inference on the causal polytrees, CAPTAR can produces a high-level view of the security state of the protected SCADA network. Experiments on a prototype of CAPTAR proves its anomaly reasoning ability and its capabilities of satisfying the real-time reasoning requirement.

Companies develop their software in versions and iterations. Ensuring the security of each additional version using code review is costly and time consuming. This paper investigates automated tracing of the impacts of code changes on the security of a given software. To this end, we use call graphs to model the software code, and security assurance cases to model the security requirements of the software. Then we relate assurance case elements to code through the entry point methods of the software, creating a map of monitored security functions. This mapping allows to evaluate the security requirements that are affected by code changes. The approach is implemented in a set of tools and evaluated using three open-source ERP/E-commerce software applications. The limited evaluation showed that the approach is effective in identifying the impacts of code changes on the security of the software. The approach promises to considerably reduce the security assessment time of the subsequent releases and iterations of software, keeping the initial security state throughout the software lifetime.

Prior remote attestation may not be suitable to scale to the proliferation of IoT device swarms. To this end, we propose a cooperative remote attestation scheme, which validates the security state and verifies the system integrity with the cooperation of neighbor devices. The efficiency of the proposed mechanism is analyzed as a cryptographic protocol and then discussed in terms of security and scalability.