Proposed Defense Strategy Research Articles

Cyber‐attack and defense method for load frequency control in smart grid systems with electric vehicles

AbstractThe latest advancements in Vehicle‐to‐Grid (V2G) technology enable Electric Vehicles (EVs) to contribute to Frequency Regulation (FR), mitigating the impact of uncertain power fluctuations in renewable resources. However, diverse cyber‐attacks pose threats to the Load Frequency Control (LFC) system and smart grid infrastructure, compromising their accuracy and reliability. This research paper focuses on the effects of cyber‐attacks on frequency regulation in a smart grid system that relies on a communication network. The study proposes a two‐area system and a modified IEEE‐39 bus three‐area system, incorporating intermittent solar photovoltaic and wind turbine sources, traditional conventional sources, and electric vehicles. To ensure frequency stabilization and tie‐line power, the researchers introduce a cascade FOPIDN‐(1+TD) controller. Its parameters are optimized using a novel Quasi Opposition Arithmetic Optimization Algorithm (QOAOA). The proposed approach's dynamic response is compared with other commonly used controllers, demonstrating its effectiveness in maintaining stability. The article focuses on the LFC system and presents a novel approach involving a cyber‐physical model. It introduces a method for detecting and defending against cyber‐attacks using deep learning, specifically utilizing the Long‐Short‐Term‐Memory (LSTM) network. The effectiveness of the proposed defense strategy is demonstrated through experiments conducted on both a two‐area interconnected power system and the IEEE‐39 bus system. The outcomes indicate that the suggested defense mechanism effectively maintains acceptable levels of power system frequency and tie‐line power during cyber‐attacks. Additionally, the validity of the controller's performance is verified through real‐time analysis using Hardware‐In‐The‐Loop (HIL) simulation with the OPAL‐RT platform.

Robustness evaluation method and defense strategy of electricity-heat integrated energy system

Even though electricity-heat integrated energy systems (IESs) can improve energy utilization, the faults generated by extreme events can induce more complex and wider impacts. Therefore, it is urgent to study the effective defense strategies associated with electricity-heat IES. Considering the theory of complex networks, a dynamic model was established in this paper which comprehensively considered the actual operating characteristics and cascading failure process of the electricity-heat IES. The structural and functional robustness of the electricity-heat IES after failure was evaluated by link survivability, capacity survivability and power survivability. According to the post-disaster system robustness evaluation results and system characteristics without adopting the defense strategy, the critical links of the system were identified. Also, seven defense strategies were formulated based on the cascading failure mechanism of the electricity-heat IES. The testing network coupled with an improved IEEE118-node power grid and 23-node heat supply network was used to verify the effectiveness of the proposed defense strategy and to provide a theoretical basis for the resilience enhancement strategy and defense resource allocation of the electricity-heat IES.

Complex networks after centrality-based attacks and defense

Exploration in complex networks has surged. Centrality measures play a pivotal role in pinpointing essential components within these networks. Previous work focus on nodes with the highest Betweenness centrality through extensive simulations. This paper analyzes the attack and/or defense strategy using one more centrality metric, bridging centrality and Bridging-Betweenness Fusion Attack (combination of both betweenness and bridging centrality). Our two-fold contribution is (1) Using high centrality removal as an attacking strategy and inspired by the dynamic node removal process, recalculated node method after each node removal is proposed. (2) In our defense techniques, new nodes are added to existing lower centrality nodes. They are added after attacks to restore the graph’s connectivity according to proposed defense strategies. Note that some attacks and defense techniques were already introduced while others are presented first time, e.g., the combination of two centrality measures for attack and a bridging-based defense strategy. This innovative approach presents a promising advancement in enhancing the resilience and fortification of complex networks against potential attacks, signifying a notable advantage of this work.

Backdoor attack and defense in federated generative adversarial network-based medical image synthesis.

Deep Learning-based image synthesis techniques have been applied in healthcare research for generating medical images to support open research and augment medical datasets. Training generative adversarial neural networks (GANs) usually require large amounts of training data. Federated learning (FL) provides a way of training a central model using distributed data while keeping raw data locally. However, given that the FL server cannot access the raw data, it is vulnerable to backdoor attacks, an adversarial by poisoning training data. Most backdoor attack strategies focus on classification models and centralized domains. It is still an open question if the existing backdoor attacks can affect GAN training and, if so, how to defend against the attack in the FL setting. In this work, we investigate the overlooked issue of backdoor attacks in federated GANs (FedGANs). The success of this attack is subsequently determined to be the result of some local discriminators overfitting the poisoned data and corrupting the local GAN equilibrium, which then further contaminates other clients when averaging the generator's parameters and yields high generator loss. Therefore, we proposed FedDetect, an efficient and effective way of defending against the backdoor attack in the FL setting, which allows the server to detect the client's adversarial behavior based on their losses and block the malicious clients. Our extensive experiments on two medical datasets with different modalities demonstrate the backdoor attack on FedGANs can result in synthetic images with low fidelity. After detecting and suppressing the detected malicious clients using the proposed defense strategy, we show that FedGANs can synthesize high-quality medical datasets (with labels) for data augmentation to improve classification models' performance.

Location Privacy-Aware and Energy-Efficient Offloading for Distributed Edge Computing

Driven by the ever-increasing scale and intensity of the computing tasks arising from various mobile applications, distributed edge computing has fostered wide research interests. It can effectively reduce the task processing delay by partitioning the original large-scale task into several small subtasks and offloading them to multiple edge nodes (ENs) for parallel computing. In edge computing, as the mobile user usually tends to offload computing tasks to closer ENs to save transmit power, the attacker may stealthily infer user location by exploiting this feature. Although there have been some pioneering works on offloading related location privacy, they mainly focused on the scenario where each task can only be offloaded to a single EN, and may not be directly applicable to distributed edge computing. Besides, the privacy issues considered in existing works are mainly based on good heuristics, and there is still a lack of concrete examples of location privacy attacks in edge computing. To the best of our knowledge, the location privacy issue in distributed edge computing still remains largely unexplored in existing literature. With this consideration, a location inference attack based on matrix sequential probability ratio test (MSPRT) is identified in this work. Besides, a countermeasure based on dynamic multi-EN selection is proposed, together with a location privacy-aware and energy-efficient distributed offloading scheme based on the generic Lyapunov optimization framework. Both theoretic analysis and simulations based on real-world channel measurements are employed to validate the feasibility of the identified MPSRT attack and the effectiveness of the proposed defense scheme.

A defense strategy for false data injection attacks in multi-agent systems

The issue of defense strategy for cooperative control in multi-agent systems under false data injection attacks is investigated in this paper. False data injection attacks are typical cyber-attacks, some of which can evade detection and result in system instability. A communication strategy is proposed to ensure the security of multi-agent systems. In the proposed defense strategy, each agent is assigned to transmit its state to its neighbours and extra communication edges are stochastically triggered by the designed trigger signals. Furthermore, the communication topology condition is derived to ensure the security of multi-agent systems under the defense strategy. The theoretical results are verified through numerical simulation.

Adversarial Defensive Framework for State-of-Health Prediction of Lithium Batteries

Neural Networks are subject to malicious data poisoning attacks affecting the ability of the model to make accurate predictions. The attacks are generated using adversarial techniques imperceptible to the human eye since they use minimal noise to alter features which end up affecting boundary decisions of the prediction Model. Predicting lithium-ion batteries' State of Health (SOH) in a adversarial context becomes a challenging task especially if the model is expected to always predict at a very high accuracy level. Our article presents three novel contributions. The first contribution is a SOH prediction model that shows one of the best accuracy rates in the literature ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R2=99.82\%$</tex-math></inline-formula> ) and yet uses the simplest LSTM model configuration compared to literature. The second contribution is the implementation of three state-of-the-art adversarial data poisoning attacks at decision time, knowingly Fast Gradient Method (FGSM), Momentum Iterative Method (MIM) and Basic Iterative Method (BIM) and the assessment of their impact on the original prediction accuracy. Most of literature use the attacks in a classification context while we are applying it to a time series prediction context. The third and most important contribution of this article is presenting a generic defense strategy combined with a feature engineering method that can be genrelized to prevent any potential adversarial attack attempt on any prediction model in a time-series prediction context using the same suggested feature engineering approach as suggested in this paper. The accuracy of the model is assessed using error estimators Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the R square (R2).The results show that adversarial data poisoning attacks are lethal to a time-series prediction model and our proposed defense strategy is able to detect and flag the existence of malicious data using a Simple Vector Machine (SVM) classifier with a very high confidence rate ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$area\,uder\,curve=0.996$</tex-math></inline-formula> ) which allow our model to defend against any potential unseen adversarial attack.

A Universal Defense Strategy for Data-Driven Power System Stability Assessment Models Under Adversarial Examples

Based on machine learning (ML) technique, the datadriven power system stability assessment (SA) has received significant research interests in recent years. However, even with a high SA accuracy performance, the data-driven SA models may be vulnerable to adversarial examples (caused by some physical noises or adversarial attacks), which are very close to the original input but can result in a wrong SA result. To solve such threat, this paper firstly proposes a universal defense strategy for the MLbased SA models based on randomized smoothing algorithm to resist the adversarial attacks. Secondly, this paper proposes an effectiveness index for the proposed universal strategy to quantify the maximum ability of resistance to adversarial examples. Moreover, this paper provides the tight mathematical proof for the effectiveness index under the hard smoothing, soft smoothing, and binary scenarios. Simulation results verify that the proposed defense strategy can effectively resist the adversarial examples and the proposed effectiveness index can provide formal robustness guarantee for real-time power system SA applications.

A Study of the Data Security Attack and Defense Pattern in a Centralized UAV–Cloud Architecture

An unmanned aerial vehicle (UAV) is an autonomous flying robot that has attracted the interest of several communities because of its capacity to increase the safety and productivity of labor. In terms of software engineering, UAV system development is extremely difficult because the focus is not only on functional requirement fulfillment, but also on nonfunctional requirements such as security and safety, which play a crucial role in mission success. Consequently, architecture robustness is very important, and one of the most common architectures developed is based on a centralized pattern in which all UAVs are controlled from a central location. Even though this is a very important problem, many developers must expend a great deal of effort to adapt and improve security. This is because there are few practical perspectives of security development in the context of UAV system development; therefore, the study of attack and defense patterns in centralized architecture is required to fill this knowledge gap. This paper concentrates on enhancing the security aspect of UAV system development by examining attack and defense patterns in centralized architectures. We contribute to the field by identifying 26 attack variations, presenting corresponding countermeasures from a software analyst’s standpoint, and supplying a node.js code template for developers to strengthen their systems’ security. Our comprehensive analysis evaluates the proposed defense strategies in terms of time and space complexity, ensuring their effectiveness. By providing a focused and in-depth perspective on security patterns, our research offers crucial guidance for communities and developers working on UAV-based systems, facilitating the development of more secure and robust solutions.

Redundancy Attack: Breaking Logic Locking Through Oracleless Rationality Analysis

During the last decade, logic locking has been proposed to protect integrated circuits against piracy and reverse engineering threats. Functional pruning attacks, such as the Boolean satisfiability (SAT) attack, have greatly challenged the security of logic locking techniques, yet they require access to an oracle and suffer from muted efficacy on inherently SAT-hard circuits. In this article, we present a novel oracleless attack on both XOR and MUX-based logic locking by analyzing the redundancy level deviation under different keys. Our fundamental insight is that incorrect keys would produce circuits that violate universally followed design principles, prominent among which stands the minimization of logic redundancy. We leverage the redundancy-level deviation produced by individual and pairs of key bits to recover key bit values and establish pairwise equivalence, leading to an efficient nearly linear time attack algorithm. We experimentally verify that the proposed attack quickly unveils more than half of the key bits with high accuracy on 21 ISCAS’85 and MCNC circuits locked with a variety of locking techniques. To fortify logic locking in the face of this successful redundancy attack, we supplement this article with a key gate insertion methodology that smooths the deviation in redundancy level. We achieve this goal by selecting key gates that exhibit pairwise dissociation among a set of individually secure locations, which effectively breaks down correlations in arbitrary sets of key gates and, thus, imposes a consistent redundancy level throughout the key space. Experimental results confirm the strong redundancy attack resilience of the proposed defense strategy.

Resilient Path Planning for UAVs in Data Collection Under Adversarial Attacks

In this paper, we investigate jamming-resilient UAV path planning strategies for data collection in Internet of Things (IoT) networks, in which the typical UAV can learn the optimal trajectory to elude such jamming attacks. Specifically, the typical UAV is required to collect data from multiple distributed IoT nodes under collision avoidance, mission completion deadline, and kinematic constraints in the presence of jamming attacks. We first design a fixed ground jammer with continuous jamming attack and periodical jamming attack strategies to jam the link between the typical UAV and IoT nodes. Defensive strategies involving a reinforcement learning (RL) based virtual jammer and the adoption of higher SINR thresholds are proposed to counteract against such attacks. Secondly, we design an intelligent UAV jammer, which utilizes the RL algorithm to choose actions based on its observation. Then, an intelligent UAV anti-jamming strategy is constructed to deal with such attacks, and the optimal trajectory of the typical UAV is obtained via dueling double deep Q-network (D3QN). Simulation results show that both non-intelligent and intelligent jamming attacks have significant influence on the UAV’s performance, and the proposed defense strategies can recover the performance close to that in no-jammer scenarios.

Effective Selfish Mining Defense Strategies to Improve Bitcoin Dependability

Selfish mining is a typical malicious attack targeting the blockchain-based bitcoin system, an emerging crypto asset. Because of the non-incentive compatibility of the bitcoin mining protocol, the attackers are able to collect unfair mining rewards by intentionally withholding blocks. The existing works on selfish mining mostly focused on cryptography design, and malicious behavior detection based on different approaches, such as machine learning or timestamp. Most defense strategies show their effectiveness in the perspective of reward reduced. No work has been performed to design a defense strategy that aims to improve bitcoin dependability and provide a framework for quantitively evaluating the improvement. In this paper, we contribute by proposing two network-wide defensive strategies: the dynamic difficulty adjustment algorithm (DDAA) and the acceptance limitation policy (ALP). The DDAA increases the mining difficulty dynamically once a selfish mining behavior is detected, while the ALP incorporates a limitation to the acceptance rate when multiple blocks are broadcast at the same time. Both strategies are designed to disincentivize dishonest selfish miners and increase the system’s resilience to the selfish mining attack. A continuous-time Markov chain model is used to quantify the improvement in bitcoin dependability made by the proposed defense strategies. Statistical analysis is applied to evaluate the feasibility of the proposed strategies. The proposed DDAA and ALP methods are also compared to an existing timestamp-based defense strategy, revealing that the DDAA is the most effective in improving bitcoin’s dependability.

Leveraging Ferroelectric Stochasticity and In-Memory Computing for DNN IP Obfuscation

With the emergence of the Internet of Things (IoT), deep neural networks (DNNs) are widely used in different domains, such as computer vision, healthcare, social media, and defense. The hardware-level architecture of a DNN can be built using an in-memory computing-based design, which is loaded with the weights of a well-trained DNN model. However, such hardware-based DNN systems are vulnerable to model stealing attacks where an attacker reverse-engineers (REs) and extracts the weights of the DNN model. In this work, we propose an energy-efficient defense technique that combines a ferroelectric field effect transistor (FeFET)-based reconfigurable physically unclonable function (PUF) with an in-memory FeFET XNOR to thwart model stealing attacks. We leverage the inherent stochasticity in the FE domains to build a PUF that helps to corrupt the neural network’s (NN) weights when an adversarial attack is detected. We showcase the efficacy of the proposed defense scheme by performing experiments on graph-NNs (GNNs), a particular type of DNN. The proposed defense scheme is a first of its kind that evaluates the security of GNNs. We investigate the effect of corrupting the weights on different layers of the GNN on the accuracy degradation of the graph classification application for two specific error models of corrupting the FeFET-based PUFs and five different bioinformatics datasets. We demonstrate that our approach successfully degrades the inference accuracy of the graph classification by corrupting any layer of the GNN after a small rewrite pulse.

Evaluating Cybersecurity Risks of Cooperative Ramp Merging in Mixed Traffic Environments

Connected and automated vehicle (CAV) technology has the potential to greatly improve transportation mobility, safety, and energy efficiency. However, ubiquitous vehicular connectivity also opens up the door to cyberattacks. In this study, we investigate cybersecurity risks of a representative cooperative traffic management application, i.e., highway on-ramp merging, in a mixed traffic environment. We develop threat models with two trajectory spoofing strategies on CAVs to create traffic congestion and devise an attack-resilient strategy for system defense. Furthermore, we leverage VEhicular NeTwork Open Simulator, a Veins extension simulator made for CAV applications, to evaluate cybersecurity risks of the attacks and performance of the proposed defense strategy. A comprehensive case study is conducted across different traffic congestion levels, penetration rates of CAVs, and attack ratios. As expected, the results show that mobility performance decreases up to 55.19% in the worst case when the attack ratio increases, as do safety and energy. With our proposed mitigation defense algorithm, the system’s cyberattack resiliency is greatly improved.

Trustworthy AI Solutions for Cyberbiosecurity Challenges in Water Supply Systems

The recent increased adoption of AI into cyber-physical systems requires that AI models perform as intended and without security blunders. However, for such critical AI deployments, serious adversaries and data poisoning issues are still persistent. The digital (data-driven) infrastructure of the bioeconomy is progressively being utilized by state and private sector entities; especially through leveraging models and data sets for real-time decision support. Models however, can be easily targeted by adversaries or be prone to unintentional quality issues. Accordingly, our research is dedicated to defining methods that mitigate different types of attacks and their undesired consequences. We are building an AI framework that can detect anomalies and malicious acts (in water supply systems) as well as analyze cause-effect measures in multiple scenarios. The proposed defense strategies include assessing the potential vulnerabilities that can be exploited in the convergence of AI and physical systems. Such outliers can cause extensive civilian harm, produce damaging bio-security incidents, and threaten agricultural and food production. The new framework will be applied to multiple physical systems with the aim of creating meta-learning outcomes for supporting the wide-scale adoption of trustworthy AI.

Signal Emulation Attack and Defense for Smart Home IoT

Internet of Things (IoT) is transforming every corner of our daily life and plays important roles in the smart home. Depending on different requirements on wireless transmission, dedicated wireless protocols have been adopted on various types of IoT devices. Recent advances in Cross-Technology Communication (CTC) enable direct communication across those wireless protocols, which will greatly improve the spectrum utilization efficiency. However, it incurs serious security concerns on heterogeneous IoT devices. In this paper, we identify a new physical-layer attack, cross-technology signal emulation attack, where a WiFi device eavesdrops a ZigBee packet on the fly, and further manipulates the ZigBee device by emulating a ZigBee signal. To defend against this attack, we propose two defense strategies with the help of a commonly found WiFi router. Particularly, the passive defense strategy focuses on misleading the ZigBee signal eavesdropping, while the proactive approach develops a real-time detection mechanism on distinguishing between a common ZigBee signal and an emulated signal. We implement the complete attacking process and defense strategies with TI CC26x2R LaunchPad, USRP-N210 platform, and a self-designed prototype. Extensive experiments have demonstrated the existence of the attack, and the feasibility, effectiveness, and accuracy of the proposed defense strategies.

Defending against false data injection attack on demand response program: A bi-level strategy

For economic reasons, consumer appliances and communication channels do not have high cyber-security. So, due to the consumer section’s vulnerability and the communication channel, the consumer section is a potential candidate for some cyber attackers. In this paper, the destructive impact of the false data injection attacks (FDIAs) on the demand response real-time pricing is investigated. Attacker manipulating the real-time price (RTP) signal in the smart meter causes the change in the consumer’s load profile that affects network operation. This condition helps the attacker maximize the power losses in the distribution networks subject to the available attacker budget and security levels (SLs) of smart meters. A defense strategy based on the distribution network reconfiguration is proposed against FDIAs to mitigate the harmful effects of launched cyber attacks. Also, a bi-level optimization is used to model FDIA and defense strategy. At the upper level, smart meters’ optimal location is modeled to launch the FDIA constrained by the attacker’s total budget. Also, at the lower level, the network reconfiguration is modeled to find the switch’s optimal state to defend against attack. Simulation carried out on the modified IEEE 94-bus system to evaluate the proposed defense strategy efficiency.

A Novel Defensive Strategy for Facial Manipulation Detection Combining Bilateral Filtering and Joint Adversarial Training

Facial manipulation enables facial expressions to be tampered with or facial identities to be replaced in videos. The fake videos are so realistic that they are even difficult for human eyes to distinguish. This poses a great threat to social and public information security. A number of facial manipulation detectors have been proposed to address this threat. However, previous studies have shown that the accuracy of these detectors is sensitive to adversarial examples. The existing defense methods are very limited in terms of applicable scenes and defense effects. This paper proposes a new defense strategy for facial manipulation detectors, which combines a passive defense method, bilateral filtering, and a proactive defense method, joint adversarial training, to mitigate the vulnerability of facial manipulation detectors against adversarial examples. The bilateral filtering method is applied in the preprocessing stage of the model without any modification to denoise the input adversarial examples. The joint adversarial training starts from the training stage of the model, which mixes various adversarial examples and original examples to train the model. The introduction of joint adversarial training can train a model that defends against multiple adversarial attacks. The experimental results show that the proposed defense strategy positively helps facial manipulation detectors counter adversarial examples.

Performance Analysis of Cluster-Based DDoS Defense System with Different Reactive Routing Protocols

Background &amp; Objective: DDoS attack poses a huge threat to communication and security of mobile nodes in MANETs. The number of approaches proposed to defense against DDoS attacks in MANETs is much less as compared to those for the wire-based networks. The aim of this paper is to test the effectiveness of proposed cluster based DDoS attacks mechanism with various reactive routing protocols. Method: The scheme proposed here is clustering based DDoS defense mechanism, in which the Accepted: cluster heads monitors the incoming traffic to identify the presence of suspicious behaviour. After the successful identification of suspicious behavior, the flow responsible behind it will be identified and confirmed whether it is related to DDoS attack or not. Once DDoS attack is confirmed, all the packet related to it will be discarded. Results &amp; Discussions: OMNeT++ along with the INET framework is used to evaluate the effectiveness of the proposed defense scheme with different routing protocols. In attack situations, DYMO exhibited higher throughput and able to deliver approximately 95% legitimate packets. DYMO, in comparison to AODV and DSR, managed to control end-to-end delay at its best levels (i.e. 0.40 to 0.70 seconds). In terms of packet delivery ratio, AODV and DYMO both perform better than DSR and able to maintain PDR at their highest levels (i.e. 0.90 to 0.94). Conclusion: The attack detection mechanism proposed here performs various tasks like monitoring, characterization, and identification of attack traffic from the incoming flow with the help neighbouring cluster heads. The flow identified as attack is discarded and attack related information would be shared with neighbouring cluster heads to achieve distributed defense. The performance of proposed defense system is assessed with different reactive routing protocols and identified that DYMO protocols performs better than AODV and DSR.

Autonomous Balloon Controls for Protection against Projectiles with Known Destinations

This article tackles autonomous balloon controls for protection against projectiles with known destinations. We introduce a defense strategy against an enemy projectile trying to reach a destination, such as a military base, which is known a priori. We further assume that the position of the platform that launches the projectile is known in advance. Because both the platform and the projectile’s destination are known in advance, we can predict the trajectory of the projectile before the projectile is launched. The proposed defense strategy is to deploy multiple balloons on the projectile’s feasible paths so that they block the incoming projectile effectively. Each balloon has GPS sensors for locating itself and IR sensors to detect an incoming projectile. Once the projectile is sufficiently close to a balloon, the balloon explodes to destroy the projectile. Since the projectile’s purpose is reaching its destination, the balloons can effectively intercept the projectile using this blocking strategy. As far as we know, this article is novel in utilizing multiple balloons for protection against an enemy projectile. The effectiveness of our defense strategy is further verified utilizing MATLAB simulations.