Integrity Attacks Research Articles

Exploring Integrity of AEADs with Faults: Definitions and Constructions

Implementation-based attacks are major concerns for modern cryptography. For symmetric-key cryptography, a significant amount of exploration has taken place in this regard for primitives such as block ciphers. Concerning symmetric-key operating modes, such as Authenticated Encryption with Associated Data (AEAD), the stateof-the-art mainly addresses the passive Side-Channel Attacks (SCA) in the form of leakage resilient cryptography. So far, only a handful of work address Fault Attacks (FA) in the context of AEADs concerning the fundamental properties – integrity and confidentiality. In this paper, we address this gap by exploring mode-level issues arising due to FAs. We emphasize that FAs can be fatal even in cases where the adversary does not aim to extract the long-term secret, but rather tries to violate the basic security requirements (integrity and confidentiality). Notably, we show novel integrity attack examples on state-of-the-art AEAD constructions and even on a prior fault-resilient AEAD construction called SIV$. On the constructive side, we first present new security notions of fault-resilience, for PRF (frPRF), MAC (frMAC) and AEAD (frAE), the latter can be seen as an improved version of the notion introduced by Fischlin and Gunther at CT-RSA’20. Then, we propose new constructions to turn a frPRF into a fault-resilient MAC frMAC (hash-then-frPRF) and into a fault-resilient AEAD frAE (MAC-then-Encrypt-then-MAC or MEM).

Blockchain-Assisted Onion Routing Protocol for Internet of Underwater Vehicle Communication

Leveraging the advancements in the Internet of underwater vehicles (IoUVs) has been actively explored with remarkable potential applications, such as ocean mapping, exploring marine and geological formation, monitoring shipwrecks, ocean patrolling, and surveillance. However, the communication between IoUVs and the control station is highly affected by modern security threats, such as insecure authentication, unpatched access control, and data integrity attacks. To mitigate the aforementioned security issues, in this article, we have proposed an intelligent collaborative framework by amalgamating artificial intelligence (AI) and onion routing (OR) in the IoUV environment. First, AI-enabled classifiers classify the normal and malicious IoUV data to reduce the computational overhead of the OR network. Then, the data is traversed from the multi-layered encrypted OR network and simultaneously validated by verifying token and time-to-live to securely reach its destination. The incorporation of inter planetary file system (IPFS) makes the proposed framework cost-effective and scalable. Finally, the proposed framework is assessed by various evaluation metrics, such as accuracy, decryption time, and scalability.

Stealthy Integrity Attacks for a Class of Nonlinear Cyber-Physical Systems

This article proposes a stealthy integrity attack generation methodology for a class of nonlinear cyber-physical systems. Geometric control theory and stability theory of incremental systems are used to design an attack generation scheme with stealthiness properties. An attack model is proposed as a closed-loop dynamical system with an arbitrary input signal. This model is developed based on a controlled invariant subspace that results from geometric control theory and is decoupled with the system outputs and the nonlinear function. The presence of the arbitrary signal in the attack model provides an additional degree of freedom and constitutes a novel component compared with existing results. The stealthiness of the attack model is rigorously investigated based on the incremental stability of the closed-loop control system, and the incremental input-to-state stability of the anomaly detector. As a result, a sufficient condition in terms of the initial condition of the attack model is derived to guarantee stealthiness. Finally, a case study is presented to illustrate the effectiveness of the developed attack generation scheme.

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.

Multiscale Adaptive Multifractal Detrended Fluctuation Analysis-Based Source Identification of Synchrophasor Data

As a typical cyber-physical system, dispersed Phasor Measurement Units (PMUs) are networked together with advanced communication infrastructures to record the Distribution Synchrophasor (DS) which represents the states and dynamics of distribution power networks. Source information of DS is critical for many DS-based applications which is potentially vulnerable to data integrity attacks. To ensure the reliability of DS-based applications, it is imperative to efficiently authenticate the DS source locations before any DS data analytics is initiated. This letter presents a cost-effective method for accurate source identification by realising the multifractality of DS data. First, Multiscale Adaptive Multifractal Detrended Fluctuation Analysis (MSA-MFDFA) is executed to reveal the scale which possesses the most significant multifractality of the time-series DS. Subsequently, Adaptive Multifractal Interpolation (AMFI) is proposed to generate quasi high-resolution DS where unique time-frequency signatures are extracted. Such signatures are further fed into a deep learning model - deep forest for source identification. Experimental results using real-life DS of a distribution network illustrate the excellent performance of the proposed approach.

Resilient Consensus-Based AC Optimal Power Flow Against Data Integrity Attacks Using PLC

This paper investigates the resilience of consensus-based AC optimal power flow (OPF) for distribution networks against the data integrity attacks (DIA). The distribution network is first decomposed into several autonomous microgrids to achieve consensus-based AC OPF through the alternating direction multiplier method (ADMM). Then the vulnerability of consensus-based AC OPF is analyzed by showing how the dispatch results can be easily altered by attackers through falsifying the information (active power, reactive power, and voltage) shared between neighboring microgrids. Two different attack scenarios, viz., economic-driven attacks and disruptive attacks, are studied to show the malicious influence of cyber attacks on consensus-based AC OPF. A detection and mitigation strategy based on the existing power line communication (PLC) infrastructure is then described, where the critical information is not only shared in the cyber layer, but also through the power lines. Game-theoretic analysis is provided to demonstrate the effectiveness of the proposed mitigation strategy. Furthermore, to validate the authenticity of the information through PLC, a physical encryption method based on the Lorenz system is proposed. The effectiveness of the proposed attack-resilient mechanism is verified using the IEEE 123-bus test system.

Impact of cyber‐attack on coordinated voltage control in low voltage grids

AbstractPower grid is facing several challenges such as voltage violation, power losses, and power quality issues due to the high integration of renewable energy sources such as solar and wind. One of the several strategies to overcome the voltage violation problem is the provision of reactive power from wind and solar power plants. Local controller of these plants coordinates with the controller to generate the reactive power. The coordinated controller performs its function based on the information obtained from the whole grid via communication network. Communication network infrastructure has high responsibility to ensure secure and stable services in order to provide reliable voltage control in distribution grids (DGs). Unstable or insecure communication networks can lead to several problems in the power system, such as increased power losses and in worse case blackouts. Therefore, this paper analyses the impact of cyber‐attacks on the voltage quality control supported by the reactive power generation of PV plants in DGs. Two cyber‐attack scenarios are demonstrated via a voltage coordination scenario based on a real distribution grid in Northern Denmark. Simulations are done in a Simulink model of Thy Mors Energi Grid, Denmark. A cyber control block is implemented within the coordinated controller in order to mitigate the effect of cyber‐attack. Experimental results show that it is possible to detect and mitigate a cyber‐attack such as denial of service and integrity attack before sending a control signal thus contributing towards a secure, stable, and resilient power grid.

Electric load forecasting under False Data Injection Attacks using deep learning

Precise electric load forecasting at different time horizons is an essential aspect for electricity producers and consumers who participate in energy markets in order to maximize their economic efficiency. Moreover, accurate prediction of the electric load contributes toward robust and resilient power grids due to the error minimization of generators scheduling schemes. The accuracy of the existing electric load forecasting methods relies on data quality due to noisy real-world environments, and data integrity due to malicious cyber-attacks. This paper proposes a cyber-secure deep learning framework that accurately predicts electric load in power grids for a time horizon spanning from an hour to a week. The proposed deep learning framework systematically integrates Autoencoder (AE), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models (AE-CLSTM). The feasibility of the proposed solution is validated by using realistic grid data acquired from the distribution network of Tabriz, Iran. Compared to other load forecasting methods, the proposed method shows the highest accuracy in both a normal case with real-world noise and a stealthy False Data Injection Attack (FDIA). The proposed load forecasting method is practical and suitable for mitigating noise in real-world data and integrity attacks.

A coalitional security game against data integrity attacks in autonomous vehicle networks

Autonomous Vehicles (AV) possess an automated driving system that allows the vehicle to respond to external conditions and provide advanced and intelligent transportation services with limited to no human intervention. For AV, the legitimacy and reliability of sensed and transmitted data have direct influence on road network operations. For instance, the integrity of vehicle communications is vulnerable to various threats such as spoofing attacks. Due to the extremely volatile nature of AV, traditional centralized security solutions lack in flexibility and mobility support. Moreover, the very large scale of future AV networks mandates an innovative design of intelligent communications among the vehicles to identify authentic traffic information broadcasted all across the network. In this paper, we design a vehicular collaboration framework that drives the interactions among autonomous vehicles based on trustworthy communications to prevent data integrity attacks. The design involves a distributed, hedonic coalition formation game in which the vehicles evaluate their utilities according to their trust in each other and the data they transmit. The proposed vehicular coalition formation algorithm enables the vehicles to dynamically join and leave the collaborative communities based on trust updates. Vehicles' hybrid trust relationships are derived by integrating the three parameters of the REK model: Reputation, Experience, and Knowledge to optimally exploit the available information in the network and adapt the framework to different road traffic contexts. Furthermore, to protect the formed coalitions against data integrity attacks, a trust-based data aggregation procedure using Dempster-Shafer theory of evidence is adopted to limit the effect of false events on vehicles' decisions. The experimental results show that our collaboration framework is resilient to data corruption and fabrication attacks.

Data Integrity Attacks in Cloud Computing: An Overview of Identifying and Protecting Techniques

Data Integrity Attacks in Cloud Computing: An Overview of Identifying and Protecting Techniques

Optimal linear attack for multi-sensor network against state estimation

This paper is concerned with a security problem about malicious integrity attacks in state estimation system, in which multiple smart sensors locally measure information and transmit it to a remote fusion estimator though wireless channels. A joint constraint is considered for the attacker behaviour in each channel to keep stealthiness under a residual-based detector on the remote side. In order to degrade the estimator performance, the attacker will maximize the trace of the remote state estimation error covariance which is derived based on Kalman filter theory. It is proved that the optimal linear attack strategy design problem is convex and finally turned into a semi-definite programming problem. In addition, the tendency of attack behaviour on recursive and fixed Kalman filter system is analyzed. Several examples are given to illustrate the theoretical results.

A Review of Cyber–Physical Security for Photovoltaic Systems

In this article, the challenges and a future vision of the cyber–physical security of photovoltaic (PV) systems are discussed from a firmware, network, PV converter controls, and grid security perspective. The vulnerabilities of PV systems are investigated under a variety of cyberattacks, ranging from data integrity attacks to software-based attacks. A success rate metric is designed to evaluate the impact and facilitate decision-making. Model-based and data-driven methods for threat detection and mitigation are summarized. In addition, the blockchain technology that addresses cyberattacks in software and cybernetworks is described. Simulation and experimental results that show the impact of cyberattacks at the converter (device) and grid (system) levels are presented. Finally, potential research opportunities are discussed for next-generation, cybersecure power electronics systems. These opportunities include multiscale controllability, self-/event-triggering control, artificial intelligence/machine learning, hot patching, and online security. As of today, this study will be one of the few comprehensive studies in this emerging and fast-growing area.

Rollout approach to sensor scheduling for remote state estimation under integrity attack

We consider the sensor scheduling problem for remote state estimation under integrity attacks. We seek to optimize a trade-off between the energy consumption of communications and the state estimation error covariance when the acknowledgment (ACK) information, sent by the remote estimator to the local sensor, is compromised. The sensor scheduling problem is formulated as an infinite horizon discounted optimal control problem with infinite states. We first analyze the underlying Markov decision process (MDP) and show that the optimal scheduling without ACK attack is of the threshold type. Thus, we can simplify the problem by replacing the original state space with a finite state space. For the simplified MDP, when the ACK is under attack, the problem is modeled as a partially observable Markov decision process (POMDP). We analyze the induced MDP that uses a belief vector as its state for the POMDP. We investigate the properties of the exact optimal solution via contractive models and show that the threshold type of solution for the POMDP cannot be readily obtained. A suboptimal solution is then obtained via a rollout approach, which is a prominent class of reinforcement learning (RL) methods based on approximation in value space. We present two variants of rollout and provide performance bounds of those variants. Finally, numerical examples are used to demonstrate the effectiveness of the proposed rollout methods by comparing them with a finite history window approach that is widely used in RL for POMDP.

Efficient secure state estimation against sparse integrity attack for regular linear system

AbstractWe consider the problem of estimating the state of a time‐invariant linear Gaussian system in the presence of integrity attacks. The attacker can compromise out of sensors, the set of which is fixed over time and unknown to the system operator, and manipulate the measurements arbitrarily. Under the assumption that the system is regular and system matrix is non‐singular, we propose a secure estimation scheme that is resilient to ‐sparse attack as long as the system is ‐sparse detectable, which achieves the fundamental limit of secure dynamic estimation. In the absence of attack, the proposed estimation coincides with Kalman estimation with a certain probability that can be adjusted to trade‐off between performance with and without attack. Furthermore, the detectability condition checking in the designing phase and the estimation computing in the online operating phase are both computationally efficient. Two numerical examples including the IEEE 68 bus test system are provided to corroborate the results and illustrate the performance of the proposed estimator.

Secure Distributed Estimation Against Data Integrity Attacks in Internet-of-Things Systems

A secure distributed estimation method against data integrity attacks is presented for general Internet-of-Things (IoT) systems. By exploiting the spatial sparsity of the attacks, a robust optimal estimation objective to protect the entire IoT system is established. Then, a false data processor is developed to suppress the bad effects of the attacks. The convergence of the presented method is analyzed. It shows that, under accessible conditions, the estimation error will converge to be uniformly bounded for all cases, i.e., all communication links and transmitted data are arbitrarily compromised. Furthermore, by utilizing the property of persistence, a quantitative trust model is established for IoT systems based on the past behaviors of the nodes. Then, a trust-based distributed estimation method is proposed to enhance security. Simulation results by considering an IoT system with 50 nodes and 145 edges are also provided to verify our theoretical results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The increasingly serious attack events that happened in recent years demonstrate the importance of security in the development of Internet-of-Things (IoT) systems. Different from the traditional Internet, IoT systems are usually constrained by computing and storage resources. Then, complex encryption and authentication algorithms or protocols will not be applicable to IoT systems. Distributed estimation plays an important role in the collaborative control, monitoring, and management of IoT systems. Inevitably, attacks to distributed estimation could make false data spread throughout the networks and further cause terrible consequences, while the issues of network security have not been yet taken into consideration in the existing design of distributed estimation. In this article, we propose a secure distributed estimation method. By adding a security mechanism into the distributed estimation algorithm, the ability of resistance to any data integrity attacks can be achieved. Moreover, the possible damage will be completely controllable for arbitrary attacks. That is, our method can guarantee that the estimation performance will always be acceptable even in the worst case. Besides, it is simple and lightweight, and no additional communication consumption and hardware devices are needed for its deployment and operations.

Data Integrity Attack in Dynamic State Estimation of Smart Grid: Attack Model and Countermeasures

A smart grid integrates advanced sensors, efficient measurement methods, progressive control technologies, and other techniques and devices to achieve safe, efficient and economical operation of the grid system. However, the diversified and open environment of a smart grid makes energy and information of the smart grid vulnerable to malicious attacks. As a representative cyber-physical attack, the data integrity attack has an extremely severe impact on the grid operation for it can bypass the traditional detection mechanisms by adjusting the attack vector. In this paper, we first present the attack strategy against dynamic state estimation of power grid in the perspective of adversary and formulate the data integrity attack detection problem that has the characteristic of sequential decision making as a partially observable Markov decision process. Then, a deep reinforcement learning-based approach is proposed to detect against data integrity attacks, which utilizes the Long Short-Term Memory layer to extract the state features of previous time steps in determining whether the system is currently under attack. Moreover, the noisy networks are employed to ensure effective agent exploration, which prevents the agent from sticking to the non-optimal policy. The principle of a multi-step learning is adopted to increase the estimation accuracy of Q value. To address the sparse rewards problem, the prioritized experience replay is proposed to increase training efficiency. Simulation results demonstrated that the proposed detection approach surpasses the benchmarks in the comparison metrics: delay error rate and false rate. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this paper, we present a deep reinforcement learning-based algorithm to defend against the data integrity attacks of smart grid. Most of the previous works discretized the system states and utilized the current state information to identify whether the system is under attack. For this reason, the detection policy may totally ignored the continuously changing characteristics of the grid states, which will lead to poor detection performance. Moreover, the attacked system states only accounts for a small part of the entire grid operation states, the probability of sampling the experience containing the attack state is extremely small, which limits the learning efficiency of previous RL-based detection approaches. In order to increase the accuracy of detection, we first present the attack strategy against power grid’s dynamic state estimation in the perspective of adversary and formulate the partially observable Markov decision process model of attack detection problem. Moreover, we propose a deep reinforcement learning-based detection approach combining the LSTM network to extract the system state features of the previous time steps to determine whether the system is currently being attacked. To address the sparse rewards problem, the prioritized experience replay is used to increase learning efficiency. The experiments demonstrate the effectiveness of proposed detection scheme compared with benchmarks in terms of detection delay as well as accuracy. In conclusion, the proposed detection scheme is helpful in defending against the data integrity attacks without obtaining the opponent’s strategy in advance and can be conveniently applied to the real-world security management system of smart grid.

CLPA: Clean-Label Poisoning Availability Attacks Using Generative Adversarial Nets

Poisoning attacks are emerging threats to deep neural networks where the adversaries attempt to compromise the models by injecting malicious data points in the clean training data. Poisoning attacks target either the availability or integrity of a model. The availability attack aims to degrade the overall accuracy while the integrity attack causes misclassification only for specific instances without affecting the accuracy of clean data. Although clean-label integrity attacks are proven to be effective in recent studies, the feasibility of clean-label availability attacks remains unclear. This paper, for the first time, proposes a clean-label approach, CLPA, for the poisoning availability attack. We reveal that due to the intrinsic imperfection of classifiers, naturally misclassified inputs can be considered as a special type of poisoned data, which we refer to as "natural poisoned data''. We then propose a two-phase generative adversarial net (GAN) based poisoned data generation framework along with a triplet loss function for synthesizing clean-label poisoned samples that locate in a similar distribution as natural poisoned data. The generated poisoned data are plausible to human perception and can also bypass the singular vector decomposition (SVD) based defense. We demonstrate the effectiveness of our approach on CIFAR-10 and ImageNet dataset over a variety type of models. Codes are available at: https://github.com/bxz9200/CLPA.

A Cyber-Secure generalized supermodel for wind power forecasting based on deep federated learning and image processing

Accurate wind power forecasting is one of the most important operations within the economic dispatch problem to increase the performance of power and energy systems. Accordingly, this study proposes a cyber-resilient hybrid approach based on the Federated Learning and Convolutional Neural Network (CNN) procedure for short-term wind power generation forecasting in different regions of Iran. Generalizability, data independence, forecasting for regions where no training data is available, and preserving the security and privacy of data are prominent features of the proposed method. The federated network was designed with an architecture of 9 clients to perform the training process and extract the salient features from the data associated with each region in each client via the CNN technique. Then, the generalized global supermodel is produced based on the extracted features in each client to forecast the wind power in new and unknown regions such as Mahshahr, Bojnord, and Lootak that had no training data available and had no effect on global supermodel generation. Various scenarios were developed to test the robustness of the suggested methodology. In the first scenario, wind power forecasting is performed based on the suggested technique. In this scenario, the accuracy of the generalized supermodel to forecast wind power generation in each of the Mahshahr, Bojnord, and Lootak regions is 84%, 85%, and 74%, respectively. The second scenario models the scaling attack by changing the wind speed parameters to evaluate the performance of forecasting models against the data integrity attack. In this scenario, an evaluation of the forecast results based on various performance metrics is conducted highlighting the accuracy reduction of the forecast model, due to the damage caused by cyber-attacks on the input data. In the third scenario, the detection of cyber-attack is done based on the image processing-based technique. The presented results emphasize the accurate performance and high generalizability of the cyber-resilient global supermodel in forecasting wind power in various regions of Iran.

A robust support vector regression model for electric load forecasting

Electric load forecasting is a crucial part of business operations in the energy industry. Various load forecasting methods and techniques have been proposed and tested. With growing concerns about cybersecurity and malicious data manipulations, an emerging topic is to develop robust load forecasting models. In this paper, we propose a robust support vector regression (SVR) model to forecast the electricity demand under data integrity attacks. We first introduce a weight function to calculate the relative importance of each observation in the load history. We then construct a weighted quadratic surface SVR model. Some theoretical properties of the proposed model are derived. Extensive computational experiments are based on the publicly available data from Global Energy Forecasting Competition 2012 and ISO New England. To imitate data integrity attacks, we have deliberately increased or decreased the historical load data. Finally, the computational results demonstrate better accuracy of the proposed robust model over other recently proposed robust models in the load forecasting literature.

A data quarantine model to secure data in edge computing

&lt;p&gt;Edge computing provides an agile data processing platform for latency-sensitive and communication-intensive applications through a decentralized cloud and geographically distributed edge nodes. Gaining centralized control over the edge nodes can be challenging due to security issues and threats. Among several security issues, data integrity attacks can lead to inconsistent data and intrude edge data analytics. Further intensification of the attack makes it challenging to mitigate and identify the root cause. Therefore, this paper proposes a new concept of data quarantine model to mitigate data integrity attacks by quarantining intruders. The efficient security solutions in cloud, ad-hoc networks, and computer systems using quarantine have motivated adopting it in edge computing. The data acquisition edge nodes identify the intruders and quarantine all the suspected devices through dimensionality reduction. During quarantine, the proposed concept builds the reputation scores to determine the falsely identified legitimate devices and sanitize their affected data to regain data integrity. As a preliminary investigation, this work identifies an appropriate machine learning method, linear discriminant analysis (LDA), for dimensionality reduction. The LDA results in 72.83% quarantine accuracy and 0.9 seconds training time, which is efficient than other state-of-the-art methods. In future, this would be implemented and validated with ground truth data.&lt;/p&gt;