NETWORK TRAFFIC CHARACTERIZATION AND INTRUSION DETECTION IN BUILDING AUTOMATION SYSTEMS

The goal of this research was threefold: (1) to learn the operational trends and behaviors of a realworld building automation system (BAS) network for creating building device models to detect anomalous behaviors and attacks, (2) to design a framework for evaluating BA device security from both the device and network perspectives, and (3) to leverage new sources of building automation device documentation for developing robust network security rules for BAS intrusion detection systems (IDSs). These goals were achieved in three phases, first through the detailed longitudinal study and characterization of a real university campus building automation network (BAN) and with the application of machine learning techniques on field level traffic for anomaly detection. Next, through the systematization of literature in the BAS security domain to analyze cross protocol device vulnerabilities, attacks, and defenses for uncovering research gaps as the foundational basis of our proposed BA device security evaluation framework. Then, to evaluate our proposed framework the largest multiprotocol BAS testbed discussed in the literature was built and several side-channel vulnerabilities and software/firmware shortcomings were exposed. Finally, through the development of a semi-automated specification gathering, device documentation extracting, IDS rule generating framework that leveraged PICS files and BIM models.Ph.D

SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection

Machine Learning (ML) can be incredibly valuable to automate anomaly detection and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is performed. However, despite the benefits of ML models, they are highly susceptible to adversarial cyber-attack examples specifically crafted to exploit them. A wide range of adversarial attacks have been created and researchers have worked on various defense strategies to safeguard ML models, but most were not intended for the specific constraints of a communication network and its communication protocols, so they may lead to unrealistic examples in the NID domain. This Systematization of Knowledge (SoK) consolidates and summarizes the state-of-the-art adversarial learning approaches that can generate realistic examples and could be used in real ML development and deployment scenarios with real network traffic flows. This SoK also describes the open challenges regarding the use of adversarial ML in the NID domain, defines the fundamental properties that are required for an adversarial example to be realistic, and provides guidelines for researchers to ensure that their future experiments are adequate for a real communication network.Comment: 31 pages, 3 tables, 6 figures, Computers and Security journa

A Flashback on Control Logic Injection Attacks against Programmable Logic Controllers

Programmable logic controllers (PLCs) make up a substantial part of critical infrastructures (CIs) and industrial control systems (ICSs). They are programmed with a control logic that defines how to drive and operate critical processes such as nuclear power plants, petrochemical factories, water treatment systems, and other facilities. Unfortunately, these devices are not fully secure and are prone to malicious threats, especially those exploiting vulnerabilities in the control logic of PLCs. Such threats are known as control logic injection attacks. They mainly aim at sabotaging physical processes controlled by exposed PLCs, causing catastrophic damage to target systems as shown by Stuxnet. Looking back over the last decade, many research endeavors exploring and discussing these threats have been published. In this article, we present a flashback on the recent works related to control logic injection attacks against PLCs. To this end, we provide the security research community with a new systematization based on the attacker techniques under three main attack scenarios. For each study presented in this work, we overview the attack strategies, tools, security goals, infected devices, and underlying vulnerabilities. Based on our analysis, we highlight the current security challenges in protecting PLCs from such severe attacks and suggest security recommendations for future research directions

SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection

Machine Learning (ML) can be incredibly valuable to automate anomaly detection and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is performed. However, despite the benefits of ML models, they are highly susceptible to adversarial cyber-attack examples specifically crafted to exploit them. A wide range of adversarial attacks have been created and researchers have worked on various defense strategies to safeguard ML models, but most were not intended for the specific constraints of a communication network and its communication protocols, so they may lead to unrealistic examples in the NID domain. This Systematization of Knowledge (SoK) consolidates and summarizes the state-of-the-art adversarial learning approaches that can generate realistic examples and could be used in real ML development and deployment scenarios with real network traffic flows. This SoK also describes the open challenges regarding the use of adversarial ML in the NID domain, defines the fundamental properties that are required for an adversarial example to be realistic, and provides guidelines for researchers to ensure that their future experiments are adequate for a real communication network.The present work was partially supported by the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), within project ”Cybers SeC IP” (NORTE-01-0145-FEDER000044). This work has also received funding from UIDB/00760/2020.info:eu-repo/semantics/acceptedVersio

SoK: Security of Programmable Logic Controllers

Billions of people rely on essential utility and manufacturing infrastructures such as water treatment plants, energy management, and food production. Our dependence on reliable infrastructures makes them valuable targets for cyberattacks. One of the prime targets for adversaries attacking physical infrastructures are Programmable Logic Controllers (PLCs) because they connect the cyber and physical worlds. In this study, we conduct the first comprehensive systematization of knowledge that explores the security of PLCs: We present an in-depth analysis of PLC attacks and defenses and discover trends in the security of PLCs from the last 17 years of research. We introduce a novel threat taxonomy for PLCs and Industrial Control Systems (ICS). Finally, we identify and point out research gaps that, if left ignored, could lead to new catastrophic attacks against critical infrastructures.Comment: 25 pages, 13 figures, Extended version February 2024, A shortened version is to be published in the 33rd USENIX Security Symposium, for more information, see https://efrenlopez.org

Comprehensive Security Research to Contribute to Critical Infrastructure Protection Contributions to Security Governance in Disaster Risk Reduction

Critical infrastructure protection (CIP) has become a major issue in civil security, emergency management and natural hazard management. The all-hazard approach has gained ground on the international scale, and the “comprehensive approach” in security policies and security research has been advanced in order to meet current and future threats based on better integrated information, assessment, policies and capabilities. This paper aims to showcase this “comprehensive approach”, highlighting its character and cross-links to CI and natural hazard and disaster management. The paper also contributes to a broader perspective on CIP by addressing current European political concepts and socio-cultural conditions, as well as possible future EU roles. A focus is put on international critical infrastructure (CI) risks, and results from an Integrated Risk Taxonomy are presented. The paper concludes with proposing socio-cultural aspects for future research topics related to CI risks and security governance

Modèles algorithmes et méthodologie pour la conception de systèmes de sécurité physique basés sur des microcontrôleurs protégés des attaques cyber-physiques

Un moyen d'assurer la sécurité de systèmes basés sur des micro-contrôleurs (mCS) est de considérer une approche de génération à partir de spécifications. Malheureusement, les approches existantes souffrent d'inconvénients, et le but de la méthodologie présentée dans cette thèse est de les éviter dans le cas particulier des mCS pour la sécurité physique (mCS-Sec). Les principaux résultats de ce travail sont le développement de modèles, d'algorithmes, et d'une méthodologie originale de création de mCS-Sec, et leur implémentation. L'applicabilité de la méthode a été évaluée sur un système de robot de surveillance d'une zone. Dans ce cas, notre évaluation a montré que l'approche développée satisfaisait toutes les contraintes imposées, tout en offrant certains avantages par rapport aux solutions existantes. Nous pensons que cette approche permettra de réduire le nombre de faiblesses et les problèmes d'architecture dans les mCS, ce qui en réduira la surface d'attaque.One of the possible ways to ensure the security of microcontroller-based systems is the implementation of security by design approach. Unfortunately, existing approaches are not without drawbacks, that is why this thesis is aimed at developing the new one. Moreover, among all possible systems, in this work, only physical security systems were chosen as an area of the application. The main findings of the work are containing original models, algorithms, methodology and software implementation. Their correctness was checked on a system of mobile robots for perimeter monitoring. The evaluation of the developed solution showed that it satisfies all requirements while having advantages over commercial and scientific analogues, which means that the goal of this work was reached. It is assumed that the use of the developed solution will help to reduce the number of weak places and architectural defects in microcontroller-based systems, thereby significantly reducing their attack surface