Cyber-physical Attacks Research Articles

Dense Overload Subgraph Induced by Cyber–Physical Attacks in Smart Grid

With the tight couplings between the power grid and communication networks, smart grid is vulnerable to cyber-physical attacks, which can inject imperceptive perturbations to cause cascading failures. Nevertheless, as a dynamic and evolving process, it remains challenging to precisely lock the susceptible components that initiate a cascading event. To address this issue, we formulate a bi-level optimization problem in the context of cyber-physical attacks to model the attacker-operator adversarial game and generate an optimal attack strategy for electricity vulnerability assessments. Specifically, we merge the electrical overloading, topological proximity and outage correlations to construct a dense overload subgraph, which is critical for cascading propagation, by making use of fractional program to capture the density of subgraph with maximum average degree. We then compute exact solutions by adopting multiple linearization techniques to convert this bi-level fractional program into a mixed-integer linear program. Numerical studies in IEEE different-scale power systems verify the effectiveness of our model in understanding the cascading risk for a smart grid.

Secure Event-Triggered Distributed Kalman Filters for State Estimation Over Wireless Sensor Networks

In this article, we analyze the adverse effects of cyber–physical attacks as well as mitigate their impacts on the event-triggered distributed Kalman filter (DKF). We first show that although event-triggered mechanisms are highly desirable, the attacker can leverage the event-triggered mechanism to cause nontriggering misbehavior, which significantly harms the network connectivity and its collective observability. We also show that an attacker can mislead the event-triggered mechanism to achieve continuous-triggering misbehavior, which not only drains the communication resources but also harms the network’s performance. An information-theoretic approach is presented next to detect attacks on both sensors and communication channels. In contrast to the existing results, the restrictive Gaussian assumption on the attack signal’s probability distribution is not required. To mitigate attacks, a meta-Bayesian approach is presented that incorporates the outcome of the attack detection mechanism to perform second-order inference. The proposed second-order inference forms confidence and trust values about the truthfulness or legitimacy of sensors’ own estimates and those of their neighbors, respectively. Each sensor communicates its confidence to its neighbors. Sensors then incorporate the confidence they receive from their neighbors and the trust they formed about their neighbors into their posterior update laws to successfully discard corrupted information. Finally, the simulation result validates the effectiveness of the presented resilient event-triggered DKF.

Quantifying the econometric loss of a cyber-physical attack on a seaport

Quantifying the econometric loss of a cyber-physical attack on a seaport

Quantifying the econometric loss of a cyber-physical attack on a seaport

Modern Cyber-Physical Systems (CPS) show increasing levels of automation, intelligence, and optimization capabilities. Both creators and adopters of these technologies emphasize the benefits: safety, cost-savings, and efficiencies. Existing literature under-represents the potential risks associated with the adoption of individually complex systems and interconnected Systems-of-Systems (SoS). Research primarily focuses on the potential benefits, and many recent deployments of new technology have received little rigorous scrutiny. The studies that do explore issues, such as cyber-attacks on CPS, often lack an analysis of the potential impact based on the way cyber-physical risks are communicated. Due to high levels of interaction and inter dependencies in CPS, disruptions in one sector can propagate to other sectors. The initial economic impacts may start locally but will often lead to global effects if response mechanisms prove insufficient. This case study aims to address a research gap in modeling, and quantifies how a cyber-attack, with physical consequences, can affect local and global trade if a single maritime port is affected. Additionally, to further understanding of the public's perception of risk about cyber-attacks and its emergent threat to global supply chains, members of the public were asked to react to the potential econometric losses. This study produced the five-part CyPEM (Cyber Physical Econometric Model) that has the capability to translate a cyber-attack to an econometric loss. Each framework part may also be swapped out to analyze different CPS. With this, we discovered a gap in the perceptions of cyber-physical risk regarding maritime transport.

Attack Detection in Automatic Generation Control Systems using LSTM-Based Stacked Autoencoders

Automatic generation control (AGC) is paramount in maintaining the stability and operation of power grids. Its dependence on communication systems makes it vulnerable to various cyberphysical attacks. False data injection attacks (FDIA) are particularly difficult to detect and represent a major threat to AGC systems. This article proposes a novel spatio-temporal learning algorithm that can learn the normal dynamics of the power grid with AGC system to deal with this problem. The algorithm first uses a long short-term memory autoencoder to learn the normal dynamics. It then utilizes this unsupervised learned model in detecting the various possibilities of FDIA affecting the AGC system by evaluating the reconstruction residual of each measurements sample. The proposed algorithm is data-driven which makes it resilient against AGC's parameters uncertainties and modeling nonlinearities. The effectiveness of the developed algorithm is evaluated through test cases with various basic and stealth FDIAs.

Incremental Security Enforcement for Cyber-Physical Systems

Cyber-Physical attacks (CP-attacks) are launched either from the cyber-space or from the physical-space to take control of a Cyber-Physical System (CPS). Unlike conventional cyber-attacks, which are prevented through new security patches as new attacks emerge, there are no known mechanisms for incrementally patching CPS in the event of new attacks. To this end, we develop a novel approach based on recent advances in mitigating CP-attacks using run-time enforcement (RE). RE-methods have been developed for CPS, such as industrial processes and pacemakers. However, the proposed solutions are not developed considering the need for future patching as new attacks emerge. To this end, we develop the first compositional RE framework, which is specifically developed to be able to add new security patches as new security policies are added. We illustrate our approach using the case study of a drone swarm. The experimental results show that the proposed compositional/incremental approach does not suffer from the state space explosion, unlike the monolithic composition. We demonstrate a linear relationship between compile time, compile size, and execution time as the number of policies increases in the proposed compositional scheme.

Cascading Failures Mitigation Strategy for Resilient Water Infrastructures*

Critical infrastructures are required to provide high quality of services and operation in present modern societies. Recent incidents of cyber-physical attacks and failures in water distribution systems (WDSs) have necessitated finding new solutions to mitigate large-scale cascading failures. In this paper, we propose a graph partitioning method based on the most vulnerable typed-graphlets to contain the damage impact in the most vulnerable target partition by preventing the failures from propagating outside the said partition. Towards this objective, we first formulate the optimal partitioning problem with the objective of minimizing the water leakages subject to hydraulic constraints, and then we solve it using a low-complexity algorithm. We conduct simulations on a 396-node WDS. Our investigations reveal a 54% average decrease in the overall damage compared to the case without any partitioning.

An Integrated Approach of Threat Analysis for Autonomous Vehicles Perception System

Automated vehicles are a revolutionary step in mobility, providing a safe and convenient riding experience while keeping the human-driving task minimal to none. Therefore, these intelligent vehicles are equipped with sophisticated perception sensors (e.g., cameras and radars), high-performance computers, artificial intelligence (AI)-driven algorithms, and connectivity with other internet-of-things (IoT) devices. This makes autonomous vehicles (AVs) a special kind of cyber-physical system (CPS) that is moving at speed in highly interactive and dynamic environments (e.g., public roads). Thus, AV is a potential target for cyber attackers to weaponize, compromising safety and mobility on the road. The first step in addressing this problem is to have a robust threat modeling framework that can address the evolving cyber-physical threats, especially to AV applications. In this regard, two areas are studied in this paper: the common practice of threat modeling in automotive and the ISO/SAE 21434 standard, and sensors and machine learning (ML) algorithms for AV perception systems and potential cyber-physical attacks. A comparative threat analysis for an AV perception system with the ISO/SAE 21434 standard and a system-theoretic process analysis for security (STPA-Sec) approach is also demonstrated in this paper. Based on the analysis, this paper proposes a robust threat analysis and risk assessment framework with mathematical modeling to identify cyber-physical threats to AV perception systems that are critical for the driving behaviors and complex interactions of AVs in their operational design domain.

A Residual-Driven Secure Transmission and Detection Approach Against Stealthy Cyber-Physical Attacks for Accident Prevention

A Residual-Driven Secure Transmission and Detection Approach Against Stealthy Cyber-Physical Attacks for Accident Prevention

Analysis of threats to interfaces of unmanned vehicles

Statement of the problem: the intelligent environment of a smart city includes many interfaces of various types, many of which are subject to a number of cyber-physical attacks and information security threats. At the same time, unmanned transport technologies are actively developing, and various intellectual infrastructure of a smart city is becoming more widespread. For this reason, attacks on the intelligent environment of a smart city and the issues of their protection are becoming increasingly relevant. The purpose of this work is to review the most relevant threats for various components of an unmanned transport environment and their classification depending on the type of interface and the type of attack, depending on the technology used by each specific component of an unmanned transport environment. Methods used: studying previous scientific papers and reports on the use of various interfaces of the intelligent transport environment of a smart city, as well as reports on previously implemented attacks on these interfaces, systematizing attacks, classifying them depending on the type of attack and type of interface. Novelty: The novelty of the work lies in the presented descriptive model of interaction and classification of the components of an unmanned transport environment. The main contribution lies in the identification of the main threats and their relationship with the components of the unmanned transport environment. Result: the resulting threat classification allows generalizing threats for each component of an unmanned transport environment, which allows developers and computer security specialists to appeal to attacks on a specific interface component of interest for further work with them in order to take the necessary measures to prevent possible threats. A descriptive model of interaction between the components of an unmanned transport environment is proposed. Practical Relevance: The overview presented is summarized in the form of a table, which provides the correspondence of each component of the unmanned vehicle environment with the interface, possible threats and links to previous work and security incident reports for each specific component.

A Machine Learning Based Framework for Real-Time Detection and Mitigation of Sensor False Data Injection Cyber-Physical Attacks in Industrial Control Systems

A Machine Learning Based Framework for Real-Time Detection and Mitigation of Sensor False Data Injection Cyber-Physical Attacks in Industrial Control Systems

Secure Data Transmission and Trustworthiness Judgement Approaches Against Cyber-Physical Attacks in an Integrated Data-Driven Framework

Threats of cyberattacks have penetrated from disclosing critical user information to destroying/manipulating industrial control systems. Study on data security during network transmission has raised increasing attention in the systems and control community, which is found very necessary and timely in the context of Industry 4.0. In most existing approaches, the protection of the transmitted data from eavesdropping attacks and the detection of malicious integrity attacks are usually carried out separately. In this study, an integrated data-driven framework applicable at the control level is proposed to deal with secure transmission and attack detection simultaneously. In the framework, a secure correlation-based encryption/decryption approach and a trustworthiness judgement approach are proposed. Comprehensive discussions are made regarding the analysis of the sensitivity to attacks, the introduced time delay, and the design degree-of-free. Executable algorithms are presented, corresponding to which hardware is modularized and can work standalone independent from the configuration of the monitoring and control systems or any third-party authentication agencies. Evaluation results on a simulated two-area frequency-load control power grid system are provided to show the effectiveness and performance of the proposed approaches.

Reliability of a Contamination-Detection Sensor Network in Water Distribution Systems during a Cyber-Physical Attack

The vastness of water distribution systems (WDS) makes them vulnerable to exposure to different types of accidental/intentional contamination. Although most such contamination events that occurred in the recent past were accidental, criminal intent was involved in a few. Considering the accessibility of WDS and the potentially harmful outcomes of drinking-water contamination, online water-quality monitoring sensors are typically positioned in selected locations throughout WDS as a preventive strategy. These sensors, once positioned, communicate over a cyber-infrastructure layer and are liable to cyber-physical attacks—the sensor and/or its communication system becoming compromised or the sensor network becoming malfunctioned such that part of its components is deactivated. However, the sensor network placement state-of-the-art has thus far overlooked these cyber-physical attack scenarios. The current study attempts to overcome this limitation in the state-of-the-art by developing and demonstrating a methodology for evaluating the impact of a cyber-physical attack on a sensor network, compromising its functionality partially. Our proof-of-concept, using a simple network and a straightforward cyber-physical attack scenario, has revealed the vast potential of examining the performance of sensor networks under accidental/intentional malfunctioning and providing valuable information for decision makers in water utilities and regulators.

Coordinated cyber-physical attack considering false overload of lines

The application of many sensor devices and complex information communication networks in a power system brings new potential threats to the electrical cyber-physical system. In this paper, a coordinated cyber-physical attack method considering false line overload is proposed. Through the combination of physical side attack and information side attack, the false data injection attack is used to cover up the physical attack and create false overload events, leading the dispatcher to make unreasonable scheduling strategies to achieve the purpose of affecting the economic operation of the system. In response to false overload events, this paper proposes a method to select the target line of false overload. At the same time, the output cost of the generator, the cost of load reduction and the number of overload lines are used as the evaluation indices to measure the damage capability of the coordinated attack, and the evaluation indices of different dimensions are normalized using fuzzy methods. Finally, the system losses caused by different attack modes are compared and analyzed through simulation calculations, and the effectiveness of the proposed model is verified.

A Conditional Privacy Preserving Generalized Ring Signcryption Scheme for Micro Aerial Vehicles.

Micro Aerial Vehicles (MAVs) are a type of UAV that are both small and fully autonomous, making them ideal for both civilian and military applications. Modern MAVs can hover and navigate while carrying several sensors, operate over long distances, and send data to a portable base station. Despite their many benefits, MAVs often encounter obstacles due to limitations in the embedded system (such as memory, processing power, energy, etc.). Due to these obstacles and the use of open wireless communication channels, MAVs are vulnerable to a variety of cyber-physical attacks. Consequently, MAVs cannot execute complex cryptographic algorithms due to their limited computing power. In light of these considerations, this article proposes a conditional privacy-preserving generalized ring signcryption scheme for MAVs using an identity-based cryptosystem. Elliptic Curve Cryptography (ECC), with a key size of 160 bits, is used in the proposed scheme. The proposed scheme's security robustness has been analyzed using the Random Oracle Model (ROM), a formal security evaluation method. The proposed scheme is also compared in terms of computation cost, communication cost and memory overhead against relevant existing schemes. The total computation cost of the proposed scheme is 7.76 ms, which is 8.14%, 5.20%, and 11.40% schemes. The results show that the proposed scheme is both efficient and secure, proving its viability.

Deep Learning-Based Intrusion Detection Methods in Cyber-Physical Systems: Challenges and Future Trends

A cyber-physical system (CPS) integrates various interconnected physical processes, computing resources, and networking units, as well as monitors the process and applications of the computing systems. Interconnection of the physical and cyber world initiates threatening security challenges, especially with the increasing complexity of communication networks. Despite efforts to combat these challenges, it is difficult to detect and analyze cyber-physical attacks in a complex CPS. Machine learning-based models have been adopted by researchers to analyze cyber-physical security systems. This paper discusses the security threats, vulnerabilities, challenges, and attacks of CPS. Initially, the CPS architecture is presented as a layered approach including the physical layer, network layer, and application layer in terms of functionality. Then, different cyber-physical attacks regarding each layer are elaborated, in addition to challenges and key issues associated with each layer. Afterward, deep learning models are analyzed for malicious URLs and intrusion detection in cyber-physical systems. A multilayer perceptron architecture is utilized for experiments using the malicious URL detection dataset and KDD Cup99 dataset, and its performance is compared with existing works. Lastly, we provide a roadmap of future research directions for cyber-physical security to investigate attacks concerning their source, complexity, and impact.

A Double-Benefit Moving Target Defense Against Cyber–Physical Attacks in Smart Grid

The smart grid (SG), as one of the largest evolutionary critical infrastructures, witnesses the deep integration of electricity facilities and Internet of Things (IoT). But recent events show that the vulnerabilities exposed in IoT devices can be exploited by adversaries to construct the cyberattacks on SG. To address this threat, plenty of countermeasures have been proposed to enhance the SG’s security. However, the additional costs introduced by some countermeasures make the utilities hesitate to implement them practically. To alleviate this concern, in this article, we propose a double-benefit moving target defense (dB-MTD) to protect the SG from cyber–physical attacks (CPAs) and also gain generation-cost benefits. The dB-MTD enables the prevention of stealthy CPAs on the transmission lines by perturbing the reactances with the distributed flexible AC transmission system (D-FACTS). To reduce the infrastructure cost, we minimize the number of required D-FACTS devices for a specific protection goal. Although it needs investment on D-FACTS, we find that the utility can make profits from the generation costs by appropriately setting the reactance perturbations. Therefore, we formulate an optimization problem to compute the optimal reactance perturbations to maximize the generation-cost benefits, without sacrificing the protection performance of dB-MTD. Finally, using the real-world load profiles, we conduct extensive simulations to evaluate the impact of CPA on the system operation and the benefits obtained by the dB-MTD from the aspects of the D-FACTS deployment and the generation-cost profits.

A Cyber-Physical Risk Assessment Approach for Internet of Things Enabled Transportation Infrastructure

A critical transportation infrastructure integrated with the Internet of Things based wireless sensor network, operates as a cyber-physical system. However, the new form of IoT enabled transportation infrastructure is susceptible to cyber-physical attacks in the sensing area, due to inherent cyber vulnerabilities of IoT devices and deficient control barriers that could protect it. Traditional risk assessment processes, consider the physical and cyber space as isolated environments, resulting in IoT enabled transportation infrastructure not being assessed by stakeholders (i.e., operators, civil and security engineers) for cyber-physical attacks. In this paper, a new risk assessment approach for cyber-physical attacks against IoT based wireless sensor network is proposed. The approach relies on the identification and proposal of novel cyber-physical characteristics, in the aspect of threat source (e.g., motives), vulnerability (e.g., lack of authentication mechanisms) and types of physical impacts (e.g., casualties). Cyber-physical risk is computed as a product of the level and importance of these characteristics. Monte Carlo simulations and sensitivity analysis are performed to evaluate the results of an IoT enabled bridge subjected to cyber-physical attack scenarios. The results indicate that 76.6% of simulated cases have high-risk and control barriers operating in physical and cyber space can reduce the cyber-physical risk by 71.8%. Additionally, cyber-physical risk differentiates when the importance of the characteristics that are considered during risk assessment is overlooked. The approach is of interest to stakeholders who attempt to incorporate the cyber domain in risk assessment procedures of their system.

Reliability Evaluation of Smart Substation Based on Time-Varying Probabilistic Hybrid Attack Graph

A substation is the portion of a power grid that forms a link between the cyber system and the physical system. Reliability evaluation of smart substations based on a time-varying probabilistic hybrid attack graph (TVPHAG) is studied in this paper. First, the topology network of the smart substation is established, whose attributes are represented by probability. Then, in order to solve the problem of asynchrony in the cyber-physical system and the hybrid caused by heterogeneity, time-varying state equation in topology and cuts in algebra are introduced to TVPHAG. Based on TVPHAG, the evaluation of the reliability of cyber-physical systems with multiple equipment and multiple timescales is established. On this basis, the influences of physical conditions, cyberattacks, physical attacks, and cyber-physical attacks on substations are analyzed, respectively. Finally, the simulation shows that the method is effective in evaluating the reliability of smart substations, providing a new method for the evaluation of reliability.

Link State Estimation Under Cyber-Physical Attacks: Theory and Algorithms

Effective defense against cyber-physical attacks in power grids requires accurate damage assessment. While some solutions have been proposed to recover the line states within the attacked area, existing solutions are limited by the assumption of a connected post-attack grid and the lack of verifiable performance guarantees. To fill this gap, we study the recovery of line states under a cyber-physical attack that disconnects lines while blocking information from the attacked area. In contrast to existing solutions assuming a connected post-attack grid or known post-attack power injections, we consider a more challenging scenario where the attack may partition the grid into islands, which causes unknown changes in power injections. To address this problem, under the DC model we (i) propose a linear programming-based algorithm to recover the line states within the attacked area under unknown post-attack power injections, (ii) characterize the accuracy of the proposed recovery algorithm under certain conditions, and (iii) develop efficient algorithms to verify the recovery results using observable information. Then, we extend the DC-based algorithms to AC model. Our numerical evaluations demonstrate that the proposed recovery algorithm is highly accurate in localizing failed lines, most of which can be successfully verified by the proposed verification algorithms.