Biblio

Industrial control systems have stringent safety and security demands. High safety assurance can be obtained by specifying the system with possible faults and monitoring it to ensure these faults are properly addressed. Addressing security requires considering unpredictable attacker behavior. Anomaly detection, with its data driven approach, can detect simple unusual behavior and system-based attacks like the propagation of malware; on the other hand, anomaly detection is less suitable to detect more complex \textbackslashtextbackslashemph\process-based\ attacks and it provides little actionability in presence of an alert. The alternative to anomaly detection is to use specification-based intrusion detection, which is more suitable to detect process-based attacks, but is typically expensive to set up and less scalable. We propose to combine a lightweight formal system specification with anomaly detection, providing data-driven monitoring. The combination is based on mapping elements of the specification to elements of the network traffic. This allows extracting locations to monitor and relevant context information from the formal specification, thus semantically enriching the raised alerts and making them actionable. On the other hand, it also allows under-specification of data-based properties in the formal model; some predicates can be left uninterpreted and the monitoring can be used to learn a model for them. We demonstrate our methodology on a smart manufacturing use case.

Recent years have seen an exponential growth of the collection and processing of data from heterogeneous sources for a variety of purposes. Several methods and techniques have been proposed to transform and fuse data into "useful" information. However, the security aspects concerning the fusion of sensitive data are often overlooked. This paper investigates the problem of data fusion and derived data control. In particular, we identify the requirements for regulating the fusion process and eliciting restrictions on the access and usage of derived data. Based on these requirements, we propose an attribute-based policy framework to control the fusion of data from different information sources and under the control of different authorities. The framework comprises two types of policies: access control policies, which define the authorizations governing the resources used in the fusion process, and fusion policies, which define constraints on allowed fusion processes. We also discuss how such policies can be obtained for derived data.

In international military coalitions, situation awareness is achieved by gathering critical intel from different authorities. Authorities want to retain control over their data, as they are sensitive by nature, and, thus, usually employ their own authorization solutions to regulate access to them. In this paper, we highlight that harmonizing authorization solutions at the coalition level raises many challenges. We demonstrate how we address authorization challenges in the context of a scenario defined by military experts using a prototype implementation of SAFAX, an XACML-based architectural framework tailored to the development of authorization services for distributed systems.

Detection of previously unknown attacks and malicious messages is a challenging problem faced by modern network intrusion detection systems. Anomaly-based solutions, despite being able to detect unknown attacks, have not been used often in practice due to their high false positive rate, and because they provide little actionable information to the security officer in case of an alert. In this paper we focus on intrusion detection in industrial control systems networks and we propose an innovative, practical and semantics-aware framework for anomaly detection. The network communication model and alerts generated by our framework are userunderstandable, making them much easier to manage. At the same time the framework exhibits an excellent tradeoff between detection rate and false positive rate, which we show by comparing it with two existing payload-based anomaly detection methods on several ICS datasets.