Aditya P. Mathur

Muhammad Azmi Umer, Khurum Nazir Junejo, Muhammad Taha Jilani et al.

Methods from machine learning are being applied to design Industrial Control Systems resilient to cyber-attacks. Such methods focus on two major areas: the detection of intrusions at the network-level using the information acquired through network packets, and detection of anomalies at the physical process level using data that represents the physical behavior of the system. This survey focuses on four types of methods from machine learning in use for intrusion and anomaly detection, namely, supervised, semi-supervised, unsupervised, and reinforcement learning. Literature available in the public domain was carefully selected, analyzed, and placed in a 7-dimensional space for ease of comparison. The survey is targeted at researchers, students, and practitioners. Challenges associated in using the methods and research gaps are identified and recommendations are made to fill the gaps.

Muhammad Azmi Umer, Chuadhry Mujeeb Ahmed, Muhammad Taha Jilani et al.

Adversarial learning is used to test the robustness of machine learning algorithms under attack and create attacks that deceive the anomaly detection methods in Industrial Control System (ICS). Given that security assessment of an ICS demands that an exhaustive set of possible attack patterns is studied, in this work, we propose an association rule mining-based attack generation technique. The technique has been implemented using data from a secure Water Treatment plant. The proposed technique was able to generate more than 300,000 attack patterns constituting a vast majority of new attack vectors which were not seen before. Automatically generated attacks improve our understanding of the potential attacks and enable the design of robust attack detection techniques.

Chuadhry Mujeeb Ahmed, Martin Ochoa, Jianying Zhou et al.

Programmable Logic Controllers (PLCs) are a core component of an Industrial Control System (ICS). However, if a PLC is compromised or the commands sent across a network from the PLCs are spoofed, consequences could be catastrophic. In this work, a novel technique to authenticate PLCs is proposed that aims at raising the bar against powerful attackers while being compatible with real-time systems. The proposed technique captures timing information for each controller in a non-invasive manner. It is argued that Scan Cycle is a unique feature of a PLC that can be approximated passively by observing network traffic. An attacker that spoofs commands issued by the PLCs would deviate from such fingerprints. To detect replay attacks a PLC Watermarking technique is proposed. PLC Watermarking models the relationship between the scan cycle and the control logic by modeling the input/output as a function of request/response messages of a PLC. The proposed technique is validated on an operational water treatment plant (SWaT) and smart grid (EPIC) testbed. Results from experiments indicate that PLCs can be distinguished based on their scan cycle timing characteristics.

Sridhar Adepu, Venkata Reddy Palleti, Gyanendra Mishra et al.

A Cyber Physical System (CPS) consists of cyber components for computation and communication, and physical components such as sensors and actuators for process control. These components are networked and interact in a feedback loop. CPS are found in critical infrastructure such as water distribution, power grid, and mass transportation. Often these systems are vulnerable to attacks as the cyber components such as Supervisory Control and Data Acquisition workstations, Human Machine Interface and Programmable Logic Controllers are potential targets for attackers. In this work, we report a study to investigate the impact of cyber attacks on a water distribution (WADI) system. Attacks were designed to meet attacker objectives and launched on WADI using a specially designed tool. This tool enables the launch of single and multi-point attacks where the latter are designed to specifically hide one or more attacks. The outcome of the experiments led to a better understanding of attack propagation and behavior of WADI in response to the attacks as well as to the design of an attack detection mechanism for water distribution system.

Sridhar Adepu, Aditya Mathur

A hackfest named SWaT Security Showdown (S3) has been organized consecutively for two years. S3 has enabled researchers and practitioners to assess the effectiveness of methods and products aimed at detecting cyber attacks launched in real-time on an operational water treatment plant, namely, Secure Water Treatment (SWaT). In S3 independent attack teams design and launch attacks on SWaT while defence teams protect the plant passively and raise alarms upon attack detection. Attack teams are scored according to how successful they are in performing attacks based on specific intents while the defense teams are scored based on the effectiveness of their methods to detect the attacks. This paper focuses on the first two instances of S3 and summarizes the benefits of hackfest and the performance of an attack detection mechanism, named Water Defense, that was exposed to attackers during S3.

Chuadhry Mujeeb Ahmed, Aditya Mathur, Martin Ochoa

In recent years fingerprinting of various physical and logical devices has been proposed with the goal of uniquely identifying users or devices of mainstream IT systems such as PCs, Laptops and smart phones. On the other hand, the application of such techniques in Cyber-Physical Systems (CPS) is less explored due to various reasons, such as difficulty of direct access to critical systems and the cost involved in faithfully reproducing realistic scenarios. In this work we evaluate the feasibility of using fingerprinting techniques in the context of realistic Industrial Control Systems related to water treatment and distribution. Based on experiments conducted with 44 sensors of six different types, it is shown that noise patterns due to microscopic imperfections in hardware manufacturing can be used to uniquely identify sensors in a CPS with up to 97% accuracy. The proposed technique can be used in to detect physical attacks, such as the replacement of legitimate sensors by faulty or manipulated sensors. We also show that, unexpectedly, sensor fingerprinting can effectively detect advanced physical attacks such as analog sensor spoofing due to variations in received energy at the transducer of an active sensor. Also, it can be leveraged to construct a novel challenge-response protocol that exposes cyber-attacks.