Biblio

In modern societies, critical services such as transportation, power supply, water treatment and distribution are strongly dependent on Industrial Control Systems (ICS). As technology moves along, new features improve services provided by such ICS. On the other hand, this progress also introduces new risks of cyber attacks due to the multiple direct and indirect dependencies between cyber and physical components of such systems. Performing rigorous security tests and risk analysis in these critical systems is thus a challenging task, because of the non-trivial interactions between digital and physical assets and the domain-specific knowledge necessary to analyse a particular system. In this work, we propose a methodology to model and analyse a System Under Test (SUT) as a data flow graph that highlights interactions among internal entities throughout the SUT. This model is automatically extracted from production code available in Programmable Logic Controllers (PLCs). We also propose a reachability algorithm and an attack diagram that will emphasize the dependencies between cyber and physical domains, thus enabling a human analyst to gauge various attack vectors that arise from subtle dependencies in data and information propagation. We test our methodology in a functional water treatment testbed and demonstrate how an analyst could make use of our designed attack diagrams to reason on possible threats to various targets of the SUT.

An attack detection scheme is proposed to detect data integrity attacks on sensors in Cyber-Physical Systems (CPSs). A combined fingerprint for sensor and process noise is created during the normal operation of the system. Under sensor spoofing attack, noise pattern deviates from the fingerprinted pattern enabling the proposed scheme to detect attacks. To extract the noise (difference between expected and observed value) a representative model of the system is derived. A Kalman filter is used for the purpose of state estimation. By subtracting the state estimates from the real system states, a residual vector is obtained. It is shown that in steady state the residual vector is a function of process and sensor noise. A set of time domain and frequency domain features is extracted from the residual vector. Feature set is provided to a machine learning algorithm to identify the sensor and process. Experiments are performed on two testbeds, a real-world water treatment (SWaT) facility and a water distribution (WADI) testbed. A class of zero-alarm attacks, designed for statistical detectors on SWaT are detected by the proposed scheme. It is shown that a multitude of sensors can be uniquely identified with accuracy higher than 90% based on the noise fingerprint.

In a software system it is possible to quantify the amount of information that is leaked or corrupted by analysing the flows of information present in the source code. In a cyber-physical system, information flows are not only present at the digital level but also at a physical level, and they are also present to and fro the two levels. In this work, we provide a methodology to formally analyse a composite, cyber-physical system model (combining physics and control) using an information flow-theoretic approach. We use this approach to quantify the level of vulnerability of a system with respect to attackers with different capabilities. We illustrate our approach by means of a water distribution case study.

In recent years, the emerging Internet-of-Things (IoT) has led to rising concerns about the security of networked embedded devices. In this work, we propose the SIPHON architecture–-a Scalable high-Interaction Honeypot platform for IoT devices. Our architecture leverages IoT devices that are physically at one location and are connected to the Internet through so-called $\backslash$emph\wormholes\ distributed around the world. The resulting architecture allows exposing few physical devices over a large number of geographically distributed IP addresses. We demonstrate the proposed architecture in a large scale experiment with 39 wormhole instances in 16 cities in 9 countries. Based on this setup, five physical IP cameras, one NVR and one IP printer are presented as 85 real IoT devices on the Internet, attracting a daily traffic of 700MB for a period of two months. A preliminary analysis of the collected traffic indicates that devices in some cities attracted significantly more traffic than others (ranging from 600 000 incoming TCP connections for the most popular destination to less than 50 000 for the least popular). We recorded over 400 brute-force login attempts to the web-interface of our devices using a total of 1826 distinct credentials, from which 11 attempts were successful. Moreover, we noted login attempts to Telnet and SSH ports some of which used credentials found in the recently disclosed Mirai malware.