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

On Ladder Logic Bombs in Industrial Control Systems

In industrial control systems, devices such as Programmable Logic Controllers (PLCs) are commonly used to directly interact with sensors and actuators, and perform local automatic control. PLCs run software on two different layers: a) firmware (i.e. the OS) and b) control logic (processing sensor readings to determine control actions). In this work, we discuss ladder logic bombs, i.e. malware written in ladder logic (or one of the other IEC 61131-3-compatible languages). Such malware would be inserted by an attacker into existing control logic on a PLC, and either persistently change the behavior, or wait for specific trigger signals to activate malicious behaviour. For example, the LLB could replace legitimate sensor readings with manipulated values. We see the concept of LLBs as a generalization of attacks such as the Stuxnet attack. We introduce LLBs on an abstract level, and then demonstrate several designs based on real PLC devices in our lab. In particular, we also focus on stealthy LLBs, i.e. LLBs that are hard to detect by human operators manually validating the program running in PLCs. In addition to introducing vulnerabilities on the logic layer, we also discuss countermeasures and we propose two detection techniques.Comment: 11 pages, 14 figures, 2 tables, 1 algorith

Present and Desired Network Management to Cope with the Expected Expansion, NM-AIST Study Case.

The network management as defined by the International Standards Organization (ISO) has five functional areas of network management. In this work we explore all functional areas for the present and desired network management to cope with the expected expansions.  We provide recommendations on each functional area to increase the overall effectiveness of current and future management tools and practices for the NM-AIST (Nelson Mandela African Institute of Science and Technology) network. The design guidelines for future implementation of network management tools are also explored

Machine Learning based Anomaly Detection for Cybersecurity Monitoring of Critical Infrastructures

openManaging critical infrastructures requires to increasingly rely on Information and Communi- cation Technologies. The last past years showed an incredible increase in the sophistication of attacks. For this reason, it is necessary to develop new algorithms for monitoring these infrastructures. In this scenario, Machine Learning can represent a very useful ally. After a brief introduction on the issue of cybersecurity in Industrial Control Systems and an overview of the state of the art regarding Machine Learning based cybersecurity monitoring, the present work proposes three approaches that target different layers of the control network architecture. The first one focuses on covert channels based on the DNS protocol, which can be used to establish a command and control channel, allowing attackers to send malicious commands. The second one focuses on the field layer of electrical power systems, proposing a physics-based anomaly detection algorithm for Distributed Energy Resources. The third one proposed a first attempt to integrate physical and cyber security systems, in order to face complex threats. All these three approaches are supported by promising results, which gives hope to practical applications in the next future.openXXXIV CICLO - SCIENZE E TECNOLOGIE PER L'INGEGNERIA ELETTRONICA E DELLE TELECOMUNICAZIONI - Elettromagnetismo, elettronica, telecomunicazioniGaggero, GIOVANNI BATTIST

A log mining approach for process monitoring in SCADA

SCADA (Supervisory Control and Data Acquisition) systems are used for controlling and monitoring industrial processes. We propose a methodology to systematically identify potential process-related threats in SCADA. Process-related threats take place when an attacker gains user access rights and performs actions, which look legitimate, but which are intended to disrupt the SCADA process. To detect such threats, we propose a semi-automated approach of log processing. We conduct experiments on a real-life water treatment facility. A preliminary case study suggests that our approach is effective in detecting anomalous events that might alter the regular process workflow

AI-enabled modeling and monitoring of data-rich advanced manufacturing systems

The infrastructure of cyber-physical systems (CPS) is based on a meta-concept of cybermanufacturing systems (CMS) that synchronizes the Industrial Internet of Things (IIoTs), Cloud Computing, Industrial Control Systems (ICSs), and Big Data analytics in manufacturing operations. Artificial Intelligence (AI) can be incorporated to make intelligent decisions in the day-to-day operations of CMS. Cyberattack spaces in AI-based cybermanufacturing operations pose significant challenges, including unauthorized modification of systems, loss of historical data, destructive malware, software malfunctioning, etc. However, a cybersecurity framework can be implemented to prevent unauthorized access, theft, damage, or other harmful attacks on electronic equipment, networks, and sensitive data. The five main cybersecurity framework steps are divided into procedures and countermeasure efforts, including identifying, protecting, detecting, responding, and recovering. Given the major challenges in AI-enabled cybermanufacturing systems, three research objectives are proposed in this dissertation by incorporating cybersecurity frameworks. The first research aims to detect the in-situ additive manufacturing (AM) process authentication problem using high-volume video streaming data. A side-channel monitoring approach based on an in-situ optical imaging system is established, and a tensor-based layer-wise texture descriptor is constructed to describe the observed printing path. Subsequently, multilinear principal component analysis (MPCA) is leveraged to reduce the dimension of the tensor-based texture descriptor, and low-dimensional features can be extracted for detecting attack-induced alterations. The second research work seeks to address the high-volume data stream problems in multi-channel sensor fusion for diverse bearing fault diagnosis. This second approach proposes a new multi-channel sensor fusion method by integrating acoustics and vibration signals with different sampling rates and limited training data. The frequency-domain tensor is decomposed by MPCA, resulting in low-dimensional process features for diverse bearing fault diagnosis by incorporating a Neural Network classifier. By linking the second proposed method, the third research endeavor is aligned to recovery systems of multi-channel sensing signals when a substantial amount of missing data exists due to sensor malfunction or transmission issues. This study has leveraged a fully Bayesian CANDECOMP/PARAFAC (FBCP) factorization method that enables to capture of multi-linear interaction (channels × signals) among latent factors of sensor signals and imputes missing entries based on observed signals

Fine-grained reasoning about the security and usability trade-off in modern security tools

Defense techniques detect or prevent attacks based on their ability to model the attacks. A balance between security and usability should always be established in any kind of defense technique. Attacks that exploit the weak points in security tools are very powerful and thus can go undetected. One source of those weak points in security tools comes when security is compromised for usability reasons, where if a security tool completely secures a system against attacks the whole system will not be usable because of the large false alarms or the very restricted policies it will create, or if the security tool decides not to secure a system against certain attacks, those attacks will simply and easily succeed. The key contribution of this dissertation is that it digs deeply into modern security tools and reasons about the inherent security and usability trade-offs based on identifying the low-level, contributing factors to known issues. This is accomplished by implementing full systems and then testing those systems in realistic scenarios. The thesis that this dissertation tests is that we can reason about security and usability trade-offs in fine-grained ways by building and testing full systems. Furthermore, this dissertation provides practical solutions and suggestions to reach a good balance between security and usability. We study two modern security tools, Dynamic Information Flow Tracking (DIFT) and Antivirus (AV) software, for their importance and wide usage. DIFT is a powerful technique that is used in various aspects of security systems. It works by tagging certain inputs and propagating the tags along with the inputs in the target system. However, current DIFT systems do not track implicit information flow because if all DIFT propagation rules are directly applied in a conservative way, the target system will be full of tagged data (a problem called overtagging) and thus useless because the tags tell us very little about the actual information flow of the system. So, current DIFT systems drop some security for usability. In this dissertation, we reason about the sources of the overtagging problem and provide practical ways to deal with it, while previous approaches have focused on abstract descriptions of the main causes of the problem based on limited experiments. The second security tool we consider in this dissertation is antivirus (AV) software. AV is a very important tool that protects systems against worms and viruses by scanning data against a database of signatures. Despite its importance and wide usage, AV has received little attention from the security research community. In this dissertation, we examine the AV internals and reason about the possibility of creating timing channel attacks against AV software. The attacker could infer information about the AV based only on the scanning time the AV spends to scan benign inputs. The other aspect of AV this dissertation explores is the low-level AV performance impact on systems. Even though the performance overhead of AV is a well known issue, the exact reasons behind this overhead are not well-studied. In this dissertation, we design a methodology that utilizes Event Tracing for Windows technology (ETW), a technology that accounts for all OS events, to reason about AV performance impact from the OS point of view. We show that the main performance impact of the AV on a task is the longer waiting time the task spends waiting on events

Electric Power Grid Resilience to Cyber Adversaries: State of the Art

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The smart electricity grids have been evolving to a more complex cyber-physical ecosystem of infrastructures with integrated communication networks, new carbon-free sources of powergeneratio n, advanced monitoring and control systems, and a myriad of emerging modern physical hardware technologies. With the unprecedented complexity and heterogeneity in dynamic smart grid networks comes additional vulnerability to emerging threats such as cyber attacks. Rapid development and deployment of advanced network monitoring and communication systems on one hand, and the growing interdependence of the electric power grids to a multitude of lifeline critical infrastructures on the other, calls for holistic defense strategies to safeguard the power grids against cyber adversaries. In order to improve the resilience of the power grid against adversarial attacks and cyber intrusions, advancements should be sought on detection techniques, protection plans, and mitigation practices in all electricity generation, transmission, and distribution sectors. This survey discusses such major directions and recent advancements from a lens of different detection techniques, equipment protection plans, and mitigation strategies to enhance the energy delivery infrastructure resilience and operational endurance against cyber attacks. This undertaking is essential since even modest improvements in resilience of the power grid against cyber threats could lead to sizeable monetary savings and an enriched overall social welfare