Assessing and augmenting SCADA cyber security: a survey of techniques

SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability

Game-Theoretic and Machine-Learning Techniques for Cyber-Physical Security and Resilience in Smart Grid

The smart grid is the next-generation electrical infrastructure utilizing Information and Communication Technologies (ICTs), whose architecture is evolving from a utility-centric structure to a distributed Cyber-Physical System (CPS) integrated with a large-scale of renewable energy resources. However, meeting reliability objectives in the smart grid becomes increasingly challenging owing to the high penetration of renewable resources and changing weather conditions. Moreover, the cyber-physical attack targeted at the smart grid has become a major threat because millions of electronic devices interconnected via communication networks expose unprecedented vulnerabilities, thereby increasing the potential attack surface. This dissertation is aimed at developing novel game-theoretic and machine-learning techniques for addressing the reliability and security issues residing at multiple layers of the smart grid, including power distribution system reliability forecasting, risk assessment of cyber-physical attacks targeted at the grid, and cyber attack detection in the Advanced Metering Infrastructure (AMI) and renewable resources. This dissertation first comprehensively investigates the combined effect of various weather parameters on the reliability performance of the smart grid, and proposes a multilayer perceptron (MLP)-based framework to forecast the daily number of power interruptions in the distribution system using time series of common weather data. Regarding evaluating the risk of cyber-physical attacks faced by the smart grid, a stochastic budget allocation game is proposed to analyze the strategic interactions between a malicious attacker and the grid defender. A reinforcement learning algorithm is developed to enable the two players to reach a game equilibrium, where the optimal budget allocation strategies of the two players, in terms of attacking/protecting the critical elements of the grid, can be obtained. In addition, the risk of the cyber-physical attack can be derived based on the successful attack probability to various grid elements. Furthermore, this dissertation develops a multimodal data-driven framework for the cyber attack detection in the power distribution system integrated with renewable resources. This approach introduces the spare feature learning into an ensemble classifier for improving the detection efficiency, and implements the spatiotemporal correlation analysis for differentiating the attacked renewable energy measurements from fault scenarios. Numerical results based on the IEEE 34-bus system show that the proposed framework achieves the most accurate detection of cyber attacks reported in the literature. To address the electricity theft in the AMI, a Distributed Intelligent Framework for Electricity Theft Detection (DIFETD) is proposed, which is equipped with Benford’s analysis for initial diagnostics on large smart meter data. A Stackelberg game between utility and multiple electricity thieves is then formulated to model the electricity theft actions. Finally, a Likelihood Ratio Test (LRT) is utilized to detect potentially fraudulent meters

Cyber-Based Contingency Analysis and Insurance Implications of Power Grid

Cybersecurity for power communication infrastructure is a serious subject that has been discussed for a decade since the first North American Electric Reliability Corporation (NERC) critical infrastructure protection (CIP) initiative in 2006. Its credibility on plausibility has been evidenced by attack events in the recent past. Although this is a very high impact, rare probability event, the establishment of quantitative measures would help asset owners in making a series of investment decisions. First, this dissertation tackles attackers\u27 strategies based on the current communication architecture between remote IP-based (unmanned) power substations and energy control centers. Hypothetically, the identification of intrusion paths will lead to the worst-case scenarios that the attackers could do harm to the grid, e.g., how this switching attack may perturb to future cascading outages within a control area when an IP-based substation is compromised. Systematic approaches are proposed in this dissertation on how to systematically determine pivotal substations and how investment can be prioritized to maintain and appropriate a reasonable investment in protecting their existing cyberinfrastructure. More specifically, the second essay of this dissertation focuses on digital protecting relaying, which could have similar detrimental effects on the overall grid\u27s stability. The R-k contingency analyses are proposed to verify with steady-state and dynamic simulations to ensure consistencies of simulation outcome in the proposed modeling in a power system. This is under the assumption that attackers are able to enumerate all electronic devices and computers within a compromised substation network. The essay also assists stakeholders (the defenders) in planning out exhaustively to identify the critical digital relays to be deployed in substations. The systematic methods are the combinatorial evaluation to incorporate the simulated statistics in the proposed metrics that are used based on the physics and simulation studies using existing power system tools. Finally, a risk transfer mechanism of cyber insurance against disruptive switching attacks is studied comprehensively based on the aforementioned two attackers\u27 tactics. The evaluation hypothetically assesses the occurrence of anomalies and how these footprints of attackers can lead to a potential cascading blackout as well as to restore the power back to normal stage. The research proposes a framework of cyber insurance premium calculation based on the ruin probability theory, by modeling potential electronic intrusion and its direct impacts. This preliminary actuarial model can further improve the security of the protective parameters of the critical infrastructure via incentivizing investment in security technologies

Distributed Fault Detection in Formation of Multi-Agent Systems with Attack Impact Analysis

Autonomous Underwater Vehicles (AUVs) are capable of performing a variety of deepwater marine applications as in multiple mobile robots and cooperative robot reconnaissance. Due to the environment that AUVs operate in, fault detection and isolation as well as the formation control of AUVs are more challenging than other Multi-Agent Systems (MASs). In this thesis, two main challenges are tackled. We first investigate the formation control and fault accommodation algorithms for AUVs in presence of abnormal events such as faults and communication attacks in any of the team members. These undesirable events can prevent the entire team to achieve a safe, reliable, and efficient performance while executing underwater mission tasks. For instance, AUVs may face unexpected actuator/sensor faults and the communication between AUVs can be compromised, and consequently make the entire multi-agent system vulnerable to cyber-attacks. Moreover, a possible deception attack on network system may have a negative impact on the environment and more importantly the national security. Furthermore, there are certain requirements for speed, position or depth of the AUV team. For this reason, we propose a distributed fault detection scheme that is able to detect and isolate faults in AUVs while maintaining their formation under security constraints. The effects of faults and communication attacks with a control theoretical perspective will be studied. Another contribution of this thesis is to study a state estimation problem for a linear dynamical system in presence of a Bias Injection Attack (BIA). For this purpose, a Kalman Filter (KF) is used, where we show that the impact of an attack can be analyzed as the solution of a quadratically constrained problem for which the exact solution can be found efficiently. We also introduce a lower bound for the attack impact in terms of the number of compromised actuators and a combination of sensors and actuators. The theoretical findings are accompanied by simulation results and numerical can study examples