Security and Privacy in Industrial Cooperation

Cyberattacks have grown into a major risk for organizations, with common consequences being data theft, sabotage, and extortion. Since preventive measures do not suffice to repel attacks, timely detection of successful intruders is crucial to stop them from reaching their final goals. For this purpose, many organizations utilize Security Information and Event Management (SIEM) systems to centrally collect security-related events and scan them for attack indicators using expert-written detection rules. However, as we show by analyzing a set of widespread SIEM detection rules, adversaries can evade almost half of them easily, allowing them to perform common malicious actions within an enterprise network without being detected. To remedy these critical detection blind spots, we propose the idea of adaptive misuse detection, which utilizes machine learning to compare incoming events to SIEM rules on the one hand and known-benign events on the other hand to discover successful evasions. Based on this idea, we present AMIDES, an open-source proof-of-concept adaptive misuse detection system. Using four weeks of SIEM events from a large enterprise network and more than 500 hand-crafted evasions, we show that AMIDES successfully detects a majority of these evasions without any false alerts. In addition, AMIDES eases alert analysis by assessing which rules were evaded. Its computational efficiency qualifies AMIDES for real-world operation and hence enables organizations to significantly reduce detection blind spots with moderate effort.

Industrial Intrusion Detection Systems (IIDSs) play a critical role in safeguarding Industrial Control Systems (ICSs) against targeted cyberattacks. Unsupervised anomaly detectors, capable of learning the expected behavior of physical processes, have proven effective in detecting even novel cyberattacks. While offering decent attack detection, these systems, however, still suffer from too many False-Positive Alarms (FPAs) that operators need to investigate, eventually leading to alarm fatigue. To address this issue, in this paper, we challenge the notion of relying on a single IIDS and explore the benefits of combining multiple IIDSs. To this end, we examine the concept of ensemble learning, where a collection of classifiers (IIDSs in our case) are combined to optimize attack detection and reduce FPAs. While training ensembles for supervised classifiers is relatively straightforward, retaining the unsupervised nature of IIDSs proves challenging. In that regard, novel time-aware ensemble methods that incorporate temporal correlations between alerts and transfer-learning to best utilize the scarce training data constitute viable solutions. By combining diverse IIDSs, the detection performance can be improved beyond the individual approaches with close to no FPAs, resulting in a promising path for strengthening ICS cybersecurity.

The increasing digitalization of power grids and especially the shift towards IP-based communication drastically increase the susceptibility to cyberattacks, potentially leading to blackouts and physical damage. Understanding the involved risks, the interplay of communication and physical assets, and the effects of cyberattacks are paramount for the uninterrupted operation of this critical infrastructure. However, as the impact of cyberattacks cannot be researched in real-world power grids, current efforts tend to focus on analyzing isolated aspects at small scales, often covering only either physical or communication assets. To fill this gap, we present WATTSON, a comprehensive research environment that facilitates reproducing, implementing, and analyzing cyberattacks against power grids and, in particular, their impact on both communication and physical processes. We validate WATTSON’s accuracy against a physical testbed and show its scalability to realistic power grid sizes. We then perform authentic cyberattacks, such as Industroyer, within the environment and study their impact on the power grid’s energy and communication side. Besides known vulnerabilities, our results reveal the ripple effects of susceptible communication on complex cyber-physical processes and thus lay the foundation for effective countermeasures.

The increasing interconnection of industrial networks exposes them to an ever-growing risk of cyber attacks. To reveal such attacks early and prevent any damage, industrial intrusion detection searches for anomalies in otherwise predictable communication or process behavior. However, current efforts mostly focus on specific domains and protocols, leading to a research landscape broken up into isolated silos. Thus, existing approaches cannot be applied to other industries that would equally benefit from powerful detection. To better understand this issue, we survey 53 detection systems and find no fundamental reason for their narrow focus. Although they are often coupled to specific industrial protocols in practice, many approaches could generalize to new industrial scenarios in theory. To unlock this potential, we propose IPAL, our industrial protocol abstraction layer, to decouple intrusion detection from domain-specific industrial protocols. After proving IPAL’s correctness in a reproducibility study of related work, we showcase its unique benefits by studying the generalizability of existing approaches to new datasets and conclude that they are indeed not restricted to specific domains or protocols and can perform outside their restricted silos.

Cyberattacks against industrial control systems pose a serious risk to the safety of humans and the environment. Industrial intrusion detection systems oppose this threat by continuously monitoring industrial processes and alerting any deviations from learned normal behavior. To this end, various streams of research rely on advanced and complex approaches, i.e., artificial neural networks, thus achieving allegedly high detection rates. However, as we show in an analysis of 70 approaches from related work, their inherent complexity comes with undesired properties. For example, they exhibit incomprehensible alarms and models only specialized personnel can understand, thus limiting their broad applicability in a heterogeneous industrial domain. Consequentially, we ask whether industrial intrusion detection indeed has to be complex or can be SIMPLE instead, i.e., Sufficient to detect most attacks, Independent of hyperparameters to dial-in, Meaningful in model and alerts, Portable to other industrial domains, Local to a part of the physical process, and computationally Efficient. To answer this question, we propose our design of four SIMPLE industrial intrusion detection systems, such as simple tests for the minima and maxima of process values or the rate at which process values change. Our evaluation of these SIMPLE approaches on four state-of-the-art industrial security datasets reveals that SIMPLE approaches can perform on par with existing complex approaches from related work while simultaneously being comprehensible and easily portable to other scenarios. Thus, it is indeed justified to raise the question of whether industrial intrusion detection needs to be inherently complex.