Attack Model Research Articles

Data poisoning attacks on traffic state estimation and prediction

Data has become ubiquitous nowadays in transportation, including vehicular data and infrastructure-generated data. The growing reliance on data poses potential cybersecurity issues to transportation systems, among which the so-called “data poisoning” attacks by adversaries are becoming increasingly critical. Such attacks aim to compromise a system’s performance by adding systematic and malicious noises, perturbations, or deviations to the dataset used by the system. Formal investigations of data poisoning attacks are essential for understanding the attacks and developing effective defense methods. This study develops a general data poisoning attack model for traffic state estimation and prediction (TSEP) that is a basic application in transportation. We first formulate data poisoning attacks as a general sensitivity analysis of parameterized optimization problems over parameter changes (i.e., data perturbations) and study the Lipschitz continuity property of the solution with the presence of general (equality and inequality) constraints. Then, we develop attack models that fit a broader spectrum of learning applications (such as TSEP) by extending existing models that only focus on learning problems with no or equality constraints (widely used in the cybersecurity field). Since the solution of such general problems is often continuous but not differentiable with data changes, we apply the generalized implicit function theorem to compute the semi-derivatives that express how the TSEP solution responds to data perturbations. The semi-derivatives enable us to evaluate TSEP models’ vulnerability (at each data point) and solve the proposed attack model. We demonstrate the generality and effectiveness of the proposed method on two TSEP models using mobile sensing data.

Analysis of safe electricity consumption on load side based on attack and defence game model

AbstractWith the improvement of digitalization and intelligence in the power system, the safe operation of the power system is facing enormous challenges. The safe use of electricity on the load side is the key to achieving safe and reliable power system operation. The detection party needs amounts of human and material resources when the power network is attacked. In response to the current difficulties of low detection ability and high detection costs, this paper proposes an attack and defence game model that considers the differences between different nodes, ensuring the safety and economy of electricity consumption while reducing energy waste. At first, the structure of smart meters and the attack characteristics of intruders are summarized, and a basic attack and defence game model is constructed. The Nash equilibrium is then solved, and the optimal strategy for the game between the defender and the intruder is given to balance the relation between detection performance and energy consumption. In response to the differences generated by each node, strategies for attackers to launch attacks on different nodes and the setting of optimal thresholds for other nodes in the defence system are explored. Finally, case studies verify that the proposed model could reduce the cost of intruder detection while ensuring a specific detection rate.

PowerScout: Security-Oriented Power Delivery Network Modeling for Side-Channel Vulnerability Analysis

The growing complexity of modern electronic systems leads to the design of more sophisticated power delivery networks (PDNs). Similar to other system-level shared hardware resources, the on-board PDN unintentionally introduces side channels across design layers and voltage domains which are not explicitly specified in the functional specification. Recent works have demonstrated that the exploitation of the side channel can compromise the system security such as information leakage and fault injection. In this work, we systematically investigate the PDN-based side channel as well as potential countermeasures. To facilitate this goal, we develop PowerScout, a security-oriented PDN simulation framework that unifies the modeling of different PDN-based side-channel attacks. PowerScout performs a fast nodal analysis of complex PDNs at the system level to quantitatively evaluate the severity of side-channel vulnerabilities. With the support of PowerScout, for the first time, we validate PDN side-channel attacks in the literature via simulation. Furthermore, we are able to quantitatively measure the security impact of PDN parameters and configurations. For example, towards information leakage, removing near-chip capacitors can increase intra-chip information leakage by a maximum of 23.23 dB at mid-frequency range and inter-chip leakage by an average of 31.68 dB at mid- and high-frequency range. Similarly, the optimal toggling frequency and duty cycle are derived to achieve fault injection attacks with higher success rate and more precise control. In addition, the vulnerabilities are evaluated when hiding-based countermeasures are implemented. Based on the evaluation, we can understand the optimal defense configuration and explore the trade-off between information leakage mitigation and power supply stability.

Digital Twin-Based Cyber-Attack Detection Framework for Cyber-Physical Manufacturing Systems

Smart manufacturing (SM) systems utilize run-time data to improve productivity via intelligent decision-making and analysis mechanisms on both machine and system levels. The increased adoption of cyber-physical systems in SM leads to the comprehensive framework of cyber-physical manufacturing systems (CPMS) where data-enabled decision-making mechanisms are coupled with cyber-physical resources on the plant floor. Due to their cyber-physical nature, CPMS are susceptible to cyber-attacks that may cause harm to the manufacturing system, products, or even the human workers involved in this context. Therefore, detecting cyber-attacks efficiently and timely is a crucial step toward implementing and securing high-performance CPMS in practice. This paper addresses two key challenges to CPMS cyber-attack detection. The first challenge is distinguishing expected anomalies in the system from cyber-attacks. The second challenge is the identification of cyber-attacks during the transient response of CPMS due to closed-loop controllers. Digital twin (DT) technology emerges as a promising solution for providing additional insights into the physical process (twin) by leveraging run-time data, models, and analytics. In this work, we propose a DT framework for detecting cyber-attacks in CPMS during controlled transient behavior as well as expected anomalies of the physical process. We present a DT framework and provide details on structuring the architecture to support cyber-attack detection. Additionally, we present an experimental case study on off-the-shelf 3D printers to detect cyber-attacks utilizing the proposed DT framework to illustrate the effectiveness of our proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by developing a general-purpose and extensible digital twin-enabled cyber-attack detection framework for manufacturing systems. Existing works in the field consider specialized attack scenarios and models that may not be extensible in practical manufacturing scenarios. We utilize digital twin (DT) technology as a key enabler to develop a systematic and extensible framework where we identify the abnormality of a resource and detect if the abnormality is due to an attack or an expected anomaly. We provide several remarks on how our proposed framework can extend existing industrial control systems (ICS) and can accommodate further extensions. The presented DTs utilize data-driven machine learning models, physics-based models, and subject matter expert knowledge to perform detection and differentiation tasks in the context of expected anomalies and model-based controllers that control the manufacturing process between multiple setpoints. We utilize a model predictive controller on an off-the-shelf 3D printer to run the process, and stage anomalies and cyber-attacks that are successfully detected by the proposed framework.

An Overview of Cyber-Resilient Smart Inverters Based on Practical Attack Models

An Overview of Cyber-Resilient Smart Inverters Based on Practical Attack Models

Improved differential fault analysis of Grain128-AEAD

The number of smart devices connected to the Internet has been constantly increasing, and as a result, lightweight cryptography (LWC) has become more important in the past decade. The Lightweight Cryptography (LWC) Project is an initiative taken by the National Institute of Standards and Technology (NIST) to standardize such LWC algorithms. Grain128-AEAD, which was submitted to the NIST LWC project, is an encryption algorithm that provides both confidentiality and integrity assurance. Third-party security analysis of the submitted ciphers is an important aspect of the evaluation of the submission to the NIST LWC project. Although several pieces of existing research, such as the bit-flipping attack, random fault attack, and deterministic random fault attack, have examined the security of Grain128-AEAD, there is still room for improvement in the fault attack models of these studies. This work aims to fill this research gap by analyzing the security margin of Grain128-AEAD against a series of improved differential fault attacks. In this study, we developed a probabilistic random fault attack and applied it to Grain128-AEAD. As an improvement of the existing research, a probabilistic approach can be applied to a more relaxed moderate control attack model. The existing moderate control model assumes the fault to be injected within any bit of a given byte, whereas the faults in our improved approach can be injected within any bits of a two-byte/four-byte segment, thereby relaxing the fault precision. The results indicate that the improved moderate control requires 388 keystreams for the two-byte model and 279 for the four-byte model to identify the target fault locations for implementing a state recovery attack. The relaxed fault attack models presented in this work are more practical to implement; hence, the findings of this research have improved the existing studies and narrowed the current research gap on the fault attack models of Grain128-AEAD. % To maintain consistency in terminology, "Grain-128AEAD" has been revised to "Grain128-AEAD" in both the abstract and the main text. Please confirm this revision.

Trust management systems in Wireless Sensor Networks

The article begins by offering a comprehensive review of the current research landscape concerning trust management mechanisms. It elucidates the foundational concepts behind trust management mechanisms, subsequently detailing various attack models and the inherent vulnerabilities they exploit. A significant portion of the discussion delves into the primary computational methodologies employed in trust management. These encompass a range of techniques such as Bayesian statistics, subjective logic, fuzzy logic, D-s evidence theory, entropy theory, cloud theory, hierarchical analysis, fog computing, and machine learning. From this foundational understanding, the piece transitions to outline the challenges poised to shape the evolution of trust management mechanisms. This section not only emphasizes the hurdles currently faced by researchers and practitioners but also attempts to forecast the potential obstacles of the future. In culmination, the article encapsulates both the current state of research and the anticipated directions that promise to steer the trajectory of trust management mechanisms in forthcoming years. This holistic perspective aims to provide readers with a clear roadmap of the field's progression, emphasizing both its achievements and the milestones yet to be attained.

A Hybrid Perspective on Threat Analysis and Activity-Based Attack Modeling for Strengthening Access Control in IoT

The rapid expansion of Internet of Things (IoT) devices has resulted in an unparalleled surge in the production of data and interconnectivity. Nevertheless, as IoT ecosystems become increasingly intricate, security concerns become of utmost importance, particularly in access control systems. The objective of this research is to improve the security of IoT access control by utilizing a hybrid model for analyzing threats and modeling attacks based on activities. This study has two primary objectives: a) A hybrid classification model is used to predict labels (attack or not) in binary classification with an impressive accuracy of 98.18%. b) Another hybrid classification model is employed to predict types of attacks in M2M communication, achieving a commendable accuracy of 90%. The primary goal is to create and assess a hybrid classification model for binary classification. This model will differentiate between regular system behavior and malicious attacks on access control schemes in the Internet of Things (IoT). The hybrid model, which combines the strengths of Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) networks, achieves an exceptional accuracy rate of 98.18%. The model's high accuracy demonstrates its effectiveness in precisely detecting potential threats and minimizing false positives, thereby establishing a strong basis for improving access control security. The second objective focuses on the complex area of security, with the goal of categorizing distinct forms of attacks in Machine-to-Machine (M2M) communication within the Internet of Things (IoT) framework. The hybrid classification model, employing both GRU and LSTM networks, achieves a remarkable accuracy of 90%. This accomplishment showcases the model's aptitude in detecting and distinguishing different types of attacks, including Distributed Denial of Service (DDoS) and Man-in-the-Middle attacks. The hybrid model provides security professionals with valuable insights to proactively respond to diverse threats in M2M communication by accurately classifying attack types. This strengthens the overall security posture of IoT access control systems. Overall, this study offers a thorough and efficient combination of threat analysis and activity-based attack modeling to enhance access control in IoT. The obtained accuracies in binary classification and prediction of attack types highlight the practical usability of the suggested hybrid model, establishing a strong basis for improving the security of IoT access control systems against evolving cyber threats

Defending dominant cooperative probabilistic attack in CRNs by JS-divergence-based improved reputation algorithm

Rapid advances in wireless communication services has made limited spectrum resources increasingly scarce. One promising solution for enhancing spectrum utilization is cooperative spectrum sensing (CSS) in cognitive radio networks (CRNs). However CSS is vulnerable to Byzantine attack. Current researches show that Byzantine attack is easily defended for their fixed attack probability. In this context, we propose an improved attack model called the dominated cooperative probabilistic attack (DCPA) model in the actual situation, building upon the generalized collaborative probabilistic Byzantine attack model. This DCPA model contains auxiliary cooperative attackers (ACAs) who launch attacks and a dominant attacker (DA) who determines ACAs’ attack probability intervals based on their respective credibility. The DCPA model allows ACAs to flexibly launch attacks, without being identified by the traditional reputation defense algorithm, significantly compromising the sensing performance of CSS. To successfully resist the threat posed by the DCPA model to CSS, we propose a JS-divergence-based improved reputation algorithm that can distinguish honest users (HUs) from attackers. This algorithm analyzes two consecutive sensing reports to identify differences in sensing behavior between HUs and attackers. Through Python simulation analysis, we demonstrate that, compared to the generalized cooperative probabilistic attack (CPA) model and the attack-free CSS (AFC) model, the proposed DCPA model is more concealed and significantly more disruptive to the performance of traditional reputation defense algorithms. Furthermore, our approach greatly enhances the performance of CSS by promoting the participation of HUs and suppressing attackers during the final data fusion. And also compared with the PAM2 algorithm, the conventional voting rule (CVR) algorithm and the traditional reputation defense algorithm, our proposed algorithm improves the detection performance by at least 7%, 15% and 50%.

ATT&CK-based Advanced Persistent Threat attacks risk propagation assessment model for zero trust networks

In recent years, the growing frequency and intensity of Advanced Persistent Threats (APTs) have significantly undermined the legitimacy and financial stability of government agencies, enterprises, and other entities. Moreover, these attacks have shown the inherent vulnerabilities in conventional border defense strategies. The emergence of the zero trust network architecture can be attributed to the increasing complexity of the cyber threat landscape. With the application of risk assessment, this paper effectively tackles the challenges posed by conventional network defense limitations and enhances the efficiency of the access control decision-making process. Nevertheless, the existing risk assessment approaches primarily focus on conventional security assessment objectives, which exhibits a deficiency in the ability to dynamically assess APT attacks. The Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) Framework introduced in this paper is a novel approach to mitigating APT attacks. This paper aims to mine and analyze the frequent item set and correlation of cyber threat penetration attack techniques. The paper also intends to construct an attack technique relationship diagram and develop a tactical prediction model for cyber threat penetration attacks using the Markov chain model. Finally, our study aims to establish a risk propagation model for APT threats based on the aforementioned model. The approach presented in this paper significantly enhances the capacity of zero trust networks in addressing sophisticated cyber threats.

Meta learning-based few-shot intrusion detection for 5G-enabled industrial internet

AbstractWith the formation and popularization of the 5G-enabled industrial internet, cybersecurity risks are increasing, and the limited number of attack samples, such as zero-day, leaves a short response time for security protectors, making it substantially more difficult to protect industrial control systems from new types of malicious attacks. Traditional supervised intrusion detection models rely on a large number of samples for training and their performance needs to be improved. Therefore, there is an urgent need for few-shot intrusion detection. Aiming at the above problems, this paper proposes a detection model based on a meta-learning framework, which aims to effectively improve the accuracy and real-time performance of intrusion detection, and designs a meta-learning intrusion detection model containing a sample generation module, a feature mapping module and a feature metric module. Among them, the sample generation module introduces the residual block into the Natural GAN and proposes a new method to generate high-quality antagonistic samples—Res-Natural GAN, which is used to enhance the antagonism of the generated samples and the feature mining degree, to improve the accuracy of malicious traffic detection; the feature mapping module proposes a new attention mechanism, the multi-head fast attention mechanism, which is applied to the encoder structure of the transformer and combined with a parameter optimization algorithm based on particle swarm mutation to shorten the mapping time and improve the real-time performance of the model while mapping the features effectively; the feature metric module proposes a prototype structure based on a prototype storage update algorithm and combines it with a prototype network to achieve correct classification by measuring the Euclidean distance between the detected samples and the class of prototypes, and to shorten the inference time while ensuring the detection accuracy; finally, the three modules are combined to form a real-time meta-learning intrusion detection model. To evaluate the proposed model, five different types of experiments are conducted on multiple public datasets. The experimental results show that the model has higher detection accuracy than the traditional model for both few-shot and zero-shot malicious attacks, and is not only applicable to 5G-enabled industrial internet, but also generalized to different network environments and attack types.

Adversarial attack study on VGG16 for cat and dog image classification task

Contemporarily, adversarial attacks on deep learning models have garnered significant attention. With this in mind, this study delves into the effectiveness of adversarial attacks specifically targeted at the VGG16 model in the context of cat and dog image classification. Employing the Fast Gradient Sign Method (FGSM) for attack, the experimental findings reveal that, within a certain perturbation range, FGSM attacks can indeed reduce the model's average confidence, albeit with relatively minor impacts on accuracy. According to the analysis, the accuracy drops (decreased from 88.5% to 88.2%) is not significant, possibly due to limited classes. With small , perturbation results in a notable confidence drop. However, at higher , perturbation impact lessens, averaging around 50% confidence for cat and dog classes, indicating a 2-class scenario's upper limit in non-targeted FGSM attacks. Additionally, this research underscores the need for further exploration into various adversarial attack methods and model interpretability within the realm of image classification. Overall, these results shed light on guiding further exploration of adversarial attack defense strategies, holding significant potential for real-world applications in enhancing the robustness of AI systems against adversarial attacks.

Byzantine-Robust Decentralized Learning via Remove-then-Clip Aggregation

We consider decentralized learning over a network of workers with heterogeneous datasets, in the presence of Byzantine workers. Byzantine workers may transmit arbitrary or malicious values to neighboring workers, leading to degradation in overall performance. The heterogeneous nature of the training data across various workers complicates the identification and mitigation of Byzantine workers. To address this complex problem, we introduce a resilient decentralized learning approach that combines the gradient descent algorithm with a novel robust aggregator. Specifically, we propose a remove-then-clip aggregator, whereby each benign worker meticulously filters the neighbors' values and subsequently projects the remaining values to a sphere centered at its local value, with an appropriately selected radius. We prove that our proposed method converges to a neighborhood of a stationary point for non-convex objectives under standard assumptions. Furthermore, empirical evaluations are provided to demonstrate the superior performance of our method in comparison to existing algorithms, under various Byzantine attack models.

Model-based adaptive security control strategy against false data injection attacks in cyber–physical systems

This paper addresses the problem of adaptive security control for cyber–physical systems (CPSs) under external nonlinear disturbances and false data injection (FDI) attacks on actuators. To address FDI attacks, a full-order disturbance estimator is proposed for eliminating disturbances in the forward channel. Furthermore, an attack model based on the system state vector is developed to estimate the attack vector by considering potential attacks that can induce state errors between the reference model and the actual system. Subsequently, a novel model-based adaptive security control (MASC) strategy is devised using attack compensation and state feedback. The key advantage of our proposed strategy lies in its ability to effectively mitigate the adverse effects of attacks while accounting for nonlinear disturbances. Finally, experimental validation conducted on a brushless DC motor speed control system demonstrates the capability of achieving high output tracking accuracy.

Security assessment and improvement of building ethernet KNXnet/IP protocol

KNXnet/IP is a KNX-building automation protocol that communicates through an IP network. It is mainly used in the field of smart home and building automation control, allowing remote access to KNX devices through an IP network, so that building functions can be managed and controlled anywhere through an Internet connection. However, with the development of smart homes and building automation, such physical devices based on IP communication are more and more frequently in contact with the Internet, resulting in more and more security issues for home devices and buildings exposed to the Internet. This paper uses the formal analysis method-colored Petri net (CPN) combined with the CPN Tools to model the KNXnet/IP protocol and analyze the protocol interaction process. Based on the Dolev-Yao attacker model, the security of the KNXnet/IP protocol is evaluated and tested, and it is verified whether there are three types of attackable vulnerabilities in the protocol: replay, tampering, and spoofing. After CPN modeling analysis and verification, it is found that tampering and replay vulnerabilities in the original protocol. Therefore, we introduce timestamp and hash to strengthen the security mechanism of the protocol, which ensures the integrity, confidentiality, and freshness of the security mechanism of the protocol. After the final analysis and verification, the improvement scheme proposed in this paper can effectively improve the security performance of the protocol.

Stealing complex network attack detection method considering security situation awareness.

Tracking and detection have brought great challenges to network security. Therefore, this paper proposes a monitoring method of stealthy complex network attacks considering security situation awareness. By constructing a tracking model of invisible complex network attacks, public monitoring nodes are selected for monitoring. The cost of a single monitoring node is calculated by the algorithm, and the monitoring node is determined by the monitoring node algorithm, so as to reduce the resource occupancy rate of the monitoring node and improve the monitoring accuracy. The simulation results show that this method is stable in the range of 1000 to 4000 nodes, and can effectively monitor the complex network attacks of stealing secrets.

Novel cyber-physical architecture for optimal operation of renewable-based smart city considering false data injection attacks: Digital twin technologies for smart city infrastructure management

Smart grid is considered a cyber-physical system, which is a combination of physical devices and computational processes. Since there are lots of interactions between the cyber layer and the physical layer, the operation, management, and security of the entire system are crucial topics that must be taken into account. In this regard, this paper tackles two problems in AC MGs, as a special case of smart cities. Firstly, a framework is proposed to solve the optimal scheduling problem in these systems. In this stage, the optimal management and scheduling of the system are structured and modeled as an optimization problem. The dragonfly is utilized as a powerful optimization technique to solve this optimization problem. It is worth noting that in this paper different renewable energy sources, as well as batteries are considered. Since cyber-attacks are among the greatest threats to the system and can cause disruption and outage in smart grids, in this paper a deep-learning-based method called long short-term memory along with the concept of prediction interval is utilized to develop a cyber-attack detection model for false data injection attacks on smart meters. The proposed cyber-attack detection model is first trained using historical data and then is used in real-time for detection. In order to investigate the effectiveness of the proposed optimal scheduling scheme, the modified IEEE 33-bus test system is utilized. Also, for testing the proposed cyber-attack detection model a real-world dataset is used. The simulation results show the great performance and effectiveness of both proposed methodologies. It should be noted that all model are simulated in digital twin simulator. The novel LSTM-based cyber-attack detection model enhances security in electric grids, demonstrating remarkable accuracy.

Energy-Management Strategy of Battery Energy Storage Systems in DC Microgrids: A Distributed Fuzzy Output Consensus Control Considering Multiple Cyber Attacks

Distributed renewable sources are one of the most promising contributors for DC microgrids to reduce carbon emission and fuel consumption. Although the battery energy storage system (BESS) is widely applied to compensate the power imbalance between distributed generators (DGs) and loads, the impacts of disturbances, DGs, constant power loads (CPLs) and cyber attacks on this system are not simultaneously considered. Based on this, a distributed fuzzy output consensus control strategy is proposed to realize accurate current sharing and operate normally in the presence of denial of service (DoS) attacks and false data injection (FDI) attacks. Firstly, the whole model of the BESS in DC microgrids embedded into disturbance items, DGs, CPLs and resistive loads, is firstly built. This model could be further transformed into standard linear heterogeneous multi-agent systems with disturbance, which lays the foundation for the following control strategy. Then the model of FDI and DoS attacks are built. Meanwhile, the fuzzy logic controller (FLC) is applied to reduce the burden of communication among batteries. Based on these, a distributed output consensus fuzzy control is proposed to realize accurate current sharing among batteries. Moreover, the system under the proposed control in different cases is analyzed. Finally, the feasibility of the proposed control strategy is verified by numerical simulation results and experiment results.

Guarding Deep Learning Systems With Boosted Evasion Attack Detection and Model Update

Guarding Deep Learning Systems With Boosted Evasion Attack Detection and Model Update

Intelligent management process analysis and security performance evaluation of sports equipment based on information security

This paper discusses the application and analysis of information security in the intelligent management of sports equipment. With the rapid development of information security, the intelligent management of sports equipment has become more and more convenient, fast and safe. Through the information security situation assessment algorithm and Petri net model of natural language processing, the intelligent management of sports body is realized, personalized service and recommendation are provided, user participation and interaction are enhanced, marketing strategies are optimized, and user experience and user loyalty are enhanced. In the design of sports equipment management, SVM model, attack and defense tree model and SVM-Bayesian model are used in the classification of sports equipment. In the experimental comparison of the three models, it is found that the number of attack and defense SVM-Bayesian alarms is 11, the false alarm rate is 0.55%, the number of missed alarms is 57, the false alarm rate is 2.85%, and the accuracy rate is 96.6%. SVM-Bayesia has obvious advantages in attack success rate and risk defense probability. By constantly comparing the path data and the number of nodes, the algorithm in this paper also has a good application effect.