Biblio

We discuss the use of formal modeling to discover potential attacks on Cyber-Physical systems, in particular Industrial Control Systems. We propose a general approach to achieve that goal considering physical-layer interactions, time and state discretization of the physical process and logic, and the use of suitable attacker profiles. We then apply the approach to model a real-world water treatment testbed using ASLan++ and analyze the resulting transition system using CL-AtSe, identifying four attack classes. To show that the attacks identified by our formal assessment represent valid attacks, we compare them against practical attacks on the same system found independently by six teams from industry and academia. We find that 7 out of the 8 practical attacks were also identified by our formal assessment. We discuss limitations resulting from our chosen level of abstraction, and a number of modeling shortcuts to reduce the runtime of the analysis.

In the context of the emerging Internet of Things (IoT), a proliferation of wireless connectivity can be expected. That ubiquitous wireless communication will be hard to centrally manage and control, and can be expected to be opaque to end users. As a result, owners and users of physical space are threatened to lose control over their digital environments. In this work, we propose the idea of an IoTScanner. The IoTScanner integrates a range of radios to allow local reconnaissance of existing wireless infrastructure and participating nodes. It enumerates such devices, identifies connection patterns, and provides valuable insights for technical support and home users alike. Using our IoTScanner, we investigate metrics that could be used to classify devices and identify privacy threats in an IoT neighborhood.

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.

While attacks on information systems have for most practical purposes binary outcomes (information was manipulated/eavesdropped, or not), attacks manipulating the sensor or control signals of Industrial Control Systems (ICS) can be tuned by the attacker to cause a continuous spectrum in damages. Attackers that want to remain undetected can attempt to hide their manipulation of the system by following closely the expected behavior of the system, while injecting just enough false information at each time step to achieve their goals. In this work, we study if attack-detection can limit the impact of such stealthy attacks. We start with a comprehensive review of related work on attack detection schemes in the security and control systems community. We then show that many of those works use detection schemes that are not limiting the impact of stealthy attacks. We propose a new metric to measure the impact of stealthy attacks and how they relate to our selection on an upper bound on false alarms. We finally show that the impact of such attacks can be mitigated in several cases by the proper combination and configuration of detection schemes. We demonstrate the effectiveness of our algorithms through simulations and experiments using real ICS testbeds and real ICS systems.

In this work, we address the problem of designing and implementing honeypots for Industrial Control Systems (ICS). Honeypots are vulnerable systems that are set up with the intent to be probed and compromised by attackers. Analysis of those attacks then allows the defender to learn about novel attacks and general strategy of the attacker. Honeypots for ICS systems need to satisfy both traditional ICT requirements, such as cost and maintainability, and more specific ICS requirements, such as time and determinism. We propose the design of a virtual, high-interaction and server-based ICS honeypot to satisfy the requirements, and the deployment of a realistic, cost-effective, and maintainable ICS honeypot. An attacker model is introduced to complete the problem statement and requirements. Based on our design and the MiniCPS framework, we implemented a honeypot mimicking a water treatment testbed. To the best of our knowledge, the presented honeypot implementation is the first academic work targeting Ethernet/IP based ICS honeypots, the first ICS virtual honeypot that is high-interactive without the use of full virtualization technologies (such as a network of virtual machines), and the first ICS honeypot that can be managed with a Software-Defined Network (SDN) controller.

Spoofing is a serious threat to the widespread use of Global Navigation Satellite Systems (GNSSs) such as GPS and can be expected to play an important role in the security of many future IoT systems that rely on time, location, or navigation information. In this paper, we focus on the technique of multi-receiver GPS spoofing detection, so far only proposed theoretically. This technique promises to detect malicious spoofing signals by making use of the reported positions of several GPS receivers deployed in a fixed constellation. We scrutinize the assumptions of prior work, in particular the error models, and investigate how these models and their results can be improved due to the correlation of errors at co-located receiver positions. We show that by leveraging spatial noise correlations, the false acceptance rate of the countermeasure can be improved while preserving the sensitivity to attacks. As a result, receivers can be placed significantly closer together than previously expected, which broadens the applicability of the countermeasure. Based on theoretical and practical investigations, we build the first realization of a multi-receiver countermeasure and experimentally evaluate its performance both in authentic and in spoofing scenarios.

In this paper, we propose a hierarchical monitoring intrusion detection system (HAMIDS) for industrial control systems (ICS). The HAMIDS framework detects the anomalies in both level 0 and level 1 of an industrial control plant. In addition, the framework aggregates the cyber-physical process data in one point for further analysis as part of the intrusion detection process. The novelty of this framework is its ability to detect anomalies that have a distributed impact on the cyber-physical process. The performance of the proposed framework evaluated as part of SWaT security showdown (S3) in which six international teams were invited to test the framework in a real industrial control system. The proposed framework outperformed other proposed academic IDS in term of detection of ICS threats during the S3 event, which was held from July 25-29, 2016 at Singapore University of Technology and Design.