Critical infrastructures and industrial organizations aggressively move towards integrating elements of modern Information Technology (IT) into their monolithic Operational Technology (OT) architectures. Yet, as OT systems progressively become more and more interconnected, they silently have turned into alluring targets for diverse groups of adversaries. Meanwhile, the inherent complexity of these systems, along with their advanced-in-age nature, prevents defenders from fully applying contemporary security controls in a timely manner. Forsooth, the combination of these hindering factors has led to some of the most severe cybersecurity incidents of the past years. This work contributes a full-fledged and up-to-date survey of the most prominent threats and attacks against Industrial Control Systems and critical infrastructures, along with the communication protocols and devices adopted in these environments. Our study highlights that threats against critical infrastructure follow an upward spiral due to the mushrooming of commodity tools and techniques that can facilitate either the early or late stages of attacks. Furthermore, our survey exposes that existing vulnerabilities in the design and implementation of several of the OT-specific network protocols and devices may easily grant adversaries the ability to decisively impact physical processes. We provide a categorization of such threats and the corresponding vulnerabilities based on various criteria. The selection of the discussed incidents and identified vulnerabilities aims to provide a holistic view of the specific threats that target Industrial Control Systems and critical infrastructures. As far as we are aware, this is the first time an exhaustive and detailed survey of this kind is attempted.

Protocol stacks specifically designed for the Internet of Things (IoT) have become commonplace. At the same time, security and privacy concerns regarding IoT technologies are also attracting significant attention given the risks that are inherently associated with the respective devices and their numerous applications, ranging from healthcare, smart homes, and cities, to intelligent transportation systems and industrial automation. Considering the still heterogeneous nature of the majority of IoT protocols, a major concern is to find common references for investigating and analyzing their security and privacy threats. To this end, and on top of the current literature, this work provides a comprehensive, vis-à-vis comparison of the security aspects of the thus far most widespread IoT Wireless Personal Area Network (WPAN) protocols, namely BLE, Z-Wave, ZigBee, Thread, and EnOcean. A succinct but exhaustive review of the relevant literature from 2013 up to now is offered as a side contribution.

The difficulty in applying traditional security mechanisms in Industrial Control System (ICS) environments makes a large portion of these mission-critical assets vulnerable to cyber attacks. Therefore, there is a dire need for the development of novel security mechanisms specifically designed to protect such critical systems. Recently a lot of attention has been given to mechanisms that exploit the EM emanations of devices for defense purposes. Such practices may lead to the development of robust external and non-intrusive anomaly detection systems. Nevertheless, the majority of current work in the area neglects to consider the implications of real-life environments, particularly environmental noise. In this work, we explore the limits of EM-based anomaly detection towards identifying injection attacks in control logic software in noisy environments. Our study conducted upon both synthetically generated and real signals identified that indeed environmental noise might significantly degrade the accuracy of the anomaly detection process. Experiments done upon synthetic data indicated that assuming that signals are captured with high sampling rates, even minor code injections can be detected with above-90% accuracy in noisy environments where SNR is up to −2dB. This is true even if naive detection methods are considered. Moreover, experiments done using a real-life testbed attest that even single-instruction injections can be detected with near-perfect accuracy in relatively clean environments. Finally, noise-elimination techniques can drastically improve the reliability of the detection mechanism even in noisy environments.

Low-rate Denial of Service (LDoS) attacks are today considered one of the biggest threats against modern data centers and industrial infrastructures. Unlike traditional Distributed Denial of Service (DDoS) attacks that are mainly volumetric, LDoS attacks exhibit a very small network footprint, and therefore can easily elude standard detection and defense mechanisms. This work introduces a defense strategy that may prove particularly effective against attacks that are based on long-lived connections, an inherent trait of LDoS attacks. Our approach is based on iteratively partitioning the active connections of a victim server across a number of replica servers, and then re-evaluating the health status of each replica instance. At its core, this approach relies on live migration and containerization technologies. The main advantage of the proposed approach is that it can discover and isolate malicious connections with virtually no information about the type and characteristics of the performed attack. Additionally, while the defense takes place, there is little to no indication of the fact to the attacker. We assess various rudimentary schemes to quantify the scalability of our approach. The results from the simulations indicate that it is possible to save the vast majority of the benign connections (80%) in less than 5 min.

The nuclear power industry has historically used paper-based procedures, but a shift towards computer-based procedures (CBPs) has the potential to reduce human errors, alleviate mental workload, and improve work performance. Twenty-seven participants were randomly assigned to one of three CBP types using the Rancor Microworld Simulator, and each performed two different types of operational scenarios (startup and loss of feedwater). The three CBP types varied in levels of digitalization. It was hypothesized that there would be less favorable impressions of the most basic procedures, and that these would demonstrate lower usability than types with greater digitalization. Overall, our predictions were partially supported with some interesting caveats, specifically with some performance benefits for CBPs that provided indicators but not embedded controls. We discuss our findings in terms of optimal levels of digitalization/automation within nuclear operations and suggest pathways for future directions.