Cyber-security is the practice of protecting computing systems and networks from digital attacks, which are a rising concern in the Information Age. With the growing pace at which new attacks are developed, conventional signature based attack detection methods are often not enough, and machine learning poses as a potential solution. Adversarial machine learning is a research area that examines both the generation and detection of adversarial examples, which are inputs specially crafted to deceive classifiers, and has been extensively studied specifically in the area of image recognition, where minor modifications are performed on images that cause a classifier to produce incorrect predictions. However, in other fields, such as intrusion and malware detection, the exploration of such methods is still growing. The aim of this survey is to explore works that apply adversarial machine learning concepts to intrusion and malware detection scenarios. We concluded that a wide variety of attacks were tested and proven effective in malware and intrusion detection, although their practicality was not tested in intrusion scenarios. Adversarial defenses were substantially less explored, although their effectiveness was also proven at resisting adversarial attacks. We also concluded that, contrarily to malware scenarios, the variety of datasets in intrusion scenarios is still very small, with the most used dataset being greatly outdated.

This paper presents a distributed intrusion detection system (DIDS) for supervisory control and data acquisition (SCADA) industrial control systems, which was developed for the CockpitCI project. Its architecture was designed to address the specific characteristics and requirements for SCADA cybersecurity that cannot be adequately fulfilled by techniques from the information technology world, thus requiring a domain-specific approach. DIDS components are described in terms of their functionality, operation, integration, and management. Moreover, system evaluation and validation are undertaken within an especially designed hybrid testbed emulating the SCADA system for an electrical distribution grid.

Electrical grids generate, transport, distribute and deliver electrical power to consumers through a complex Critical Infrastructure which progressively shifted from an air-gaped to a connected architecture. Specifically, Smart Substations are important parts of Smart Grids, providing switching, transforming, monitoring, metering and protection functions to offer a safe, efficient and reliable distribution of electrical power to consumers. The evolution of electrical power grids was closely followed by the digitization of all its parts and improvements in communication and computing infrastructures, leading to an evolution towards digital smart substations with improved connectivity. However, connected smart substations are exposed to cyber threats which can result in blackouts and faults which may propagate in a chain reaction and damage electrical appliances connected across the electrical grid. This work organizes and offers a comprehensive review of architectural, communications and cybersecurity standards for smart substations, complemented by a threat landscape analysis and the presentation of a Defense-in-Depth strategy blueprint. Furthermore, this work examines several defense mechanisms documented in the literature, existing datasets, testbeds and evaluation methodologies, identifying the most relevant open issues which may guide and inspire future research work.

Computing and networking systems traditionally record their activity in log files, which have been used for multiple purposes, such as troubleshooting, accounting, post-incident analysis of security breaches, capacity planning and anomaly detection. In earlier systems those log files were processed manually by system administrators, or with the support of basic applications for filtering, compiling and pre-processing the logs for specific purposes. However, as the volume of these log files continues to grow (more logs per system, more systems per domain), it is becoming increasingly difficult to process those logs using traditional tools, especially for less straightforward purposes such as anomaly detection. On the other hand, as systems continue to become more complex, the potential of using large datasets built of logs from heterogeneous sources for detecting anomalies without prior domain knowledge becomes higher. Anomaly detection tools for such scenarios face two challenges. First, devising appropriate data analysis solutions for effectively detecting anomalies from large data sources, possibly without prior domain knowledge. Second, adopting data processing platforms able to cope with the large datasets and complex data analysis algorithms required for such purposes. In this paper we address those challenges by proposing an integrated scalable framework that aims at efficiently detecting anomalous events on large amounts of unlabeled data logs. Detection is supported by clustering and classification methods that take advantage of parallel computing environments. We validate our approach using the the well known NASA Hypertext Transfer Protocol (HTTP) logs datasets. Fourteen features were extracted in order to train a k-means model for separating anomalous and normal events in highly coherent clusters. A second model, making use of the XGBoost system implementing a gradient tree boosting algorithm, uses the previous binary clustered data for producing a set of simple interpretable rules. These rules represent the rationale for generalizing its application over a massive number of unseen events in a distributed computing environment. The classified anomaly events produced by our framework can be used, for instance, as candidates for further forensic and compliance auditing analysis in security management.

Industrial Automation and Control systems have matured into a stable infrastructure model that has been kept fundamentally unchanged, using discrete embedded systems (such as Programmable Logic Controllers) to implement the first line of sensorization, actuation, and process control and stations and servers providing monitoring, supervision, logging/database and data-sharing capabilities, among others. More recently, with the emergence of the Industry 4.0 paradigm and the need for more flexibility, there has been a steady trend towards virtualizing some of the automation station/server components, first by using virtual machines and, more recently, by using container technology. This trend is pushing for better support for real-time requirements on enabling virtualization technologies such as virtual machines and containers. This article provides a systematic review on the use of container virtualization in real-time environments such as cyber-physical systems, assessing how existing and emerging technologies can fulfill the associated requirements. Starting by reviewing fundamental concepts related to container technology and real-time requirements, it goes on to present the methodology and results of a systematic study of 37 selected papers covering aspects related to the enforcement of real-time constrains within container hosts and the expected task latency on such environments, as well as an overview of container platforms and orchestration mechanisms for RT systems.