Stealthy Deception Attacks Against SCADA Systems

SCADA protocols for Industrial Control Systems (ICS) are vulnerable to network attacks such as session hijacking. Hence, research focuses on network anomaly detection based on meta--data (message sizes, timing, command sequence), or on the state values of the physical process. In this work we present a class of semantic network-based attacks against SCADA systems that are undetectable by the above mentioned anomaly detection. After hijacking the communication channels between the Human Machine Interface (HMI) and Programmable Logic Controllers (PLCs), our attacks cause the HMI to present a fake view of the industrial process, deceiving the human operator into taking manual actions. Our most advanced attack also manipulates the messages generated by the operator's actions, reversing their semantic meaning while causing the HMI to present a view that is consistent with the attempted human actions. The attacks are totaly stealthy because the message sizes and timing, the command sequences, and the data values of the ICS's state all remain legitimate. We implemented and tested several attack scenarios in the test lab of our local electric company, against a real HMI and real PLCs, separated by a commercial-grade firewall. We developed a real-time security assessment tool, that can simultaneously manipulate the communication to multiple PLCs and cause the HMI to display a coherent system--wide fake view. Our tool is configured with message-manipulating rules written in an ICS Attack Markup Language (IAML) we designed, which may be of independent interest. Our semantic attacks all successfully fooled the operator and brought the system to states of blackout and possible equipment damage

SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach

This paper presents the development of a Supervisory Control and Data Acquisition (SCADA) system testbed used for cybersecurity research. The testbed consists of a water storage tank's control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naive Bayes and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environmentsComment: E-Preprin

HADES: a Hybrid Anomaly Detection System for Large-Scale Cyber-Physical Systems

Smart cities rely on large-scale heterogeneous distributed systems known as Cyber-Physical Systems (CPS). Information systems based on CPS typically analyse a massive amount of data collected from various data sources that operate under noisy and dynamic conditions. How to determine the quality and reliability of such data is an open research problem that concerns the overall system safety, reliability and security. Our research goal is to tackle the challenge of real-time data quality assessment for large-scale CPS applications with a hybrid anomaly detection system. In this paper we describe the architecture of HADES, our Hybrid Anomaly DEtection System for sensors data monitoring, storage, processing, analysis, and management. Such data will be filtered with correlation-based outlier detection techniques, and then processed by predictive analytics for anomaly detection

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

A Framework for Determining Robust Context-Aware Attack-Detection Thresholds for Cyber-Physical Systems

Process-aware attack detection plays a key role in securing cyber-physical systems. A process-aware detection system (PADS) identifies a baseline behaviour of the physical process in cyber-physical systems and continuously attempts to detect deviations from the baseline attributed to malicious modifications in the process operation. Typically, a PADS triggers an alarm whenever the detection score crosses a fixed and predetermined threshold. In this paper, we argue that in the context of securing cyber-physical systems, relying on a single fixed threshold can undermine the effectiveness of the PADS, and propose a context-aware framework for determining two-dimensional thresholds that enhance the sensibility and reliability of such detection systems by rendering them more robust to false detection. In addition, we propose an algorithm, out of many possible, within this framework as a practical example

Process-Aware Defenses for Cyber-Physical Systems

The increasing connectivity is exposing safety-critical systems to cyberattacks that can cause real physical damage and jeopardize human lives. With billions of IoT devices added to the Internet every year, the cybersecurity landscape is drastically shifting from IT systems and networks to systems that comprise both cyber and physical components, commonly referred to as cyber-physical systems (CPS). The difficulty of applying classical IT security solutions in CPS environments has given rise to new security techniques known as process-aware defense mechanisms, which are designed to monitor and protect industrial processes supervised and controlled by cyber elements from sabotage attempts via cyberattacks. In this thesis, we critically examine the emerging CPS-driven cybersecurity landscape and investigate how process-aware defenses can contribute to the sustainability of highly connected cyber-physical systems by making them less susceptible to crippling cyberattacks. We introduce a novel data-driven model-free methodology for real-time monitoring of physical processes to detect and report suspicious behaviour before damage occurs. We show how our model-free approach is very lightweight, does not require detailed specifications, and is applicable in various CPS environments including IoT systems and networks. We further design, implement, evaluate, and deploy process-aware techniques, study their efficacy and applicability in real-world settings, and address their deployment challenges