Attack Strategies Research Articles

Phage therapeutic delivery methods and clinical trials for combating clinically relevant pathogens.

The ongoing global health crisis caused by multidrug-resistant (MDR) bacteria necessitates quick interventions to introduce new management strategies for MDR-associated infections and antimicrobial agents' resistance. Phage therapy emerges as an antibiotic substitute for its high specificity, efficacy, and safety profiles in treating MDR-associated infections. Various in vitro and in vivo studies denoted their eminent bactericidal and anti-biofilm potential. This review addresses the latest developments in phage therapy regarding their attack strategies, formulations, and administration routes. It additionally discusses and elaborates on the status of phage therapy undergoing clinical trials, and the challenges encountered in their usage, and explores prospects in phage therapy research and application.

Template Attack Based on the Weighted Quadruple Residual Network

ABSTRACTTemplate attack, as a classic side‐channel attack method, has attracted extensive attention due to its ease of deployment and small number of hyperparameters. However, traditional template attacks (TA) suffer from high preprocessing complexity and poor performance in certain masked applications. To address these issues, this paper proposes a novel template attack method leveraging a weighted quadruple residual network. Specifically, we improve the quadruple network model by assigning different weights to the negative samples selected by the model, which balances the influence of the samples on the model's feedback. Aiming at the fact that the quadruple network ignores the label characteristics of samples in the side‐channel, the distance metric in the quadruple network model is improved, which can help the model select more suitable samples. The residual network designed in this paper enables the quadruplet network model to reduce feature dimensionality, thereby facilitating more effective analysis attacks in TA. Experimental results demonstrate a notable reduction in leaked traces required by the optimized template attack on the synchronous ASCAD dataset, achieving a 62.5% decrease compared to an advanced template attack scheme. On the AES_HD dataset, the energy trace needed for a successful attack diminishes by 46.5% in comparison to the advanced template attack scheme.

Signaling the Capability of New Counterterrorism Technologies in the Face of a Strategic Threat

ABSTRACTWhen implementing new security technologies (e.g., cargo scanners at ports), security and defense agencies face important decisions regarding the disclosure of related information to the public. Public disclosure of these technologies can grant adversarial organizations access to critical security‐related information, enabling them to potentially develop or adjust their attack strategies to circumvent defense systems. Alternatively, withholding information about newly deployed technologies can create uncertainties for adversaries regarding the effectiveness of defense systems. In this work, we study defensive information disclosure related to the deployment of new security technology, focusing on the level of technical information released by the defender and how this affects the adversary's (i) belief about the technology's capability (in detecting or otherwise thwarting an attack) and (ii) attack plan. Specifically, we introduce a novel signaling model in which the defender has private information about the (level of) capability of her technology, and decides on the level of related technical detail to be disclosed. The attacker, upon receiving the defender's signal, updates his belief about the capability of the technology and subsequently decides whether to launch an attack. We obtain and prove closed‐form equilibrium solutions to the model and conduct numerical sensitivity analyses to understand how the equilibrium strategies change as the model's data varies. Based on the equilibrium conditions, we also characterize and analyze four unique scenarios that partition the solution space based on relations between the defender's signaling costs.

Analysis of War Optimization Algorithm in a Multi-Loop Power System Based on Directional Overcurrent Relays

In electrical power systems, ensuring a reliable, precise, and efficient relay strategy is crucial for safe and trustworthy operation, especially in multi-loop distribution systems. Overcurrent relays (OCRs) have emerged as effective solutions for these challenges. This study focuses on optimizing the coordination of OCRs to minimize the overall operational time of main relays, thereby reducing power outages. The optimization problem is addressed by adjusting the time multiplier setting (TMS) using the War Strategy Optimization (WSO) algorithm, which efficiently solves this constrained problem. This algorithm mimics ancient warfare strategies of attack and defense to solve complex optimization problems efficiently. The results show that WSO provides superior performance in minimizing total operating time and achieving global optimum solutions with reduced computational effort, outperforming traditional optimization methods (i.e., SM, HPSO, GA, RTO, and JAYA). The proposed algorithm shows a net time gains of 7.77 s, 2.57 s, and 0.8484 s when compared to GA, RTO, and JAYA respectively. This robust protection coordination ensures better reliability and efficiency in multi-loop power systems.

An evacuation model considering pedestrian group behavior under violent attacks

In recent years, violent attacks have garnered significant attention from the international community and gradually evolved into a global issue. In responding to such emergencies, effective emergency evacuation is an essential means to ensure public safety. In order to explore the pedestrian evacuation dynamics in violent attacks and understand the behavioral characteristics of individuals and the dynamic interactions of groups during the evacuation process, this paper proposes an agent-based cellular automata evacuation model. First, each agent is assigned several internal attributes such as life value, panic value, and movement speed, and the assignments fully take into account the heterogeneity of individuals. Next, by setting the attack strategy of attackers and the evacuation mechanism of pedestrians, the complex system is modeled from a microscopic perspective. Finally, the effects of critical factors on the pedestrian evacuation process, such as panic coefficient, crowd structure, group ratio, attack distance and intensity, attacker location and number, and counterattack probability, are discussed through simulation. The results show that the tension caused by moderate panic is beneficial, but excessive panic will lead to increased evacuation time and fatalities. Meanwhile, the group behavior among pedestrians causes them to move relatively lagging, and the evacuation efficiency decreases as the group scale increases. Additionally, increases in attack intensity, attack distance, and attacker number all result in more casualties, and the consequences are most severe when multiple attackers operate separately, but counterattacks by pedestrians can significantly improve the overall safety of evacuation. Our study can provide some reference and basis for emergency management under violent attacks.

Optimal stealthy deception attack strategy under energy constraints

This article explores the issue of devising optimal stealthy deception attack strategy based on remote state estimation in cyber-phsical systens (CPSs) under energy constraints. Taking the energy limitations into consideration, the attacker needs to launch signals under stealth conditions and alters the transmitted data in a limited time span with a limited number of changes. The stealth attack model is proposed, which enables the attack signal to evade the detector without being detected. By analyzing various forms of continuous attacks and discrete attacks, we study five different attack sequences and find the optimal attack sequence by evaluating the long-range remote estimation error. Using the Lagrange multiplier means, the optimal parameters are obtained to maximize the long-range estimation error while guaranteeing the integral stealth of the detector. The calculation process is summarized in Algorithm 1. Simulation instances are given to demonstrate the authenticity of this paper’s work.

Dynamic trigger-based attacks against next-generation IoT malware family classifiers

The evolution of IoT malware and the effectiveness of defense strategies, e.g., leveraging malware family classification, have driven the development of advanced classification learning models. These models, particularly those that utilize model-extracted features, significantly enhance classification performance while minimizing the need for extensive expert knowledge from developers. However, a critical challenge lies in the interpretability of these learning models, which can obscure potential security risks. Among these risks are backdoor attacks, a sophisticated and deceptive threat where attackers induce malicious behaviors in the model under specific triggers.In response to the growing need for integrity and reliability in these models, this work assesses the vulnerability of state-of-the-art IoT malware classification models to backdoor attacks. Given the complexities of attacking model-based classifiers, we propose a novel trigger generation framework, B-CTG, supported by a specialized training procedure. This framework enables B-CTG to dynamically poison or attack samples to achieve specific objectives. From an attacker’s perspective, the design and training of B-CTG incorporate knowledge from the IoT domain to ensure the attack’s effectiveness. We conduct experiments under two distinct knowledge assumptions: the main evaluation, which assesses the attack method’s performance when the attacker has limited control over the model training pipeline, and the transferred setting, which further explores the significance of knowledge in predicting attacks in real-world scenarios.Our in-depth analysis focuses on attack performance in specific scenarios rather than a broad examination across multiple scenarios. Results from the main evaluation demonstrate that the proposed attack strategy can achieve high success rates even with low poisoning ratios, though stability remains a concern. Additionally, the inconsistent trends in model performance suggest that designers may struggle to detect the poisoned state of a model based on its performance alone. The transferred setting highlights the critical importance of model and feature knowledge for successful attack predictions, with feature knowledge proving particularly crucial. This insight prompts further investigation into model-agnostic mitigation methods and their effectiveness against the proposed attack strategy, with findings indicating that stability remains a significant concern for both attackers and defenders.

Stealthy Attack Against Distributed State Estimation for Cyber‐Physical Systems

ABSTRACTThis article studies a stealthy attack strategy design for distributed state estimation from the perspective of an attacker in cyber‐physical systems, where both internal and external attack scenarios are considered simultaneously. To enhance robustness against outliers and reduce communication burden, a distributed fusion estimator is developed by fusing the innovation residuals of neighboring smart sensor nodes without any attack. Based on the designed distributed fusion estimator, the evolution of the distributed estimation error covariance is analyzed, and its lower and upper bounds are obtained. Moreover, a stealthy attack framework embedding adjustable parameter is designed to weaken the estimation performance, where the constraints of the adjustable parameter are provided based on the desired attack effect. Finally, a simulation example is provided to manifest the validity of the main results.

Adversarial Attacks in Explainable Machine Learning: A Survey of Threats Against Models and Humans

ABSTRACTReliable deployment of machine learning models such as neural networks continues to be challenging due to several limitations. Some of the main shortcomings are the lack of interpretability and the lack of robustness against adversarial examples or out‐of‐distribution inputs. In this paper, we comprehensively review the possibilities and limits of adversarial attacks for explainable machine learning models. First, we extend the notion of adversarial examples to fit in explainable machine learning scenarios where a human assesses not only the input and the output classification, but also the explanation of the model's decision. Next, we propose a comprehensive framework to study whether (and how) adversarial examples can be generated for explainable models under human assessment. Based on this framework, we provide a structured review of the diverse attack paradigms existing in this domain, identify current gaps and future research directions, and illustrate the main attack paradigms discussed. Furthermore, our framework considers a wide range of relevant yet often ignored factors such as the type of problem, the user expertise or the objective of the explanations, in order to identify the attack strategies that should be adopted in each scenario to successfully deceive the model (and the human). The intention of these contributions is to serve as a basis for a more rigorous and realistic study of adversarial examples in the field of explainable machine learning.

Global Results of Implantable Loop Recorder for Detection of Atrial Fibrillation After Stroke: Reveal LINQ Registry.

We aimed to quantify the incidence of atrial fibrillation (AF) in patients with cryptogenic stroke globally, as well as separately in patients in and outside of Japan, using an implantable loop recorder from a prospective, observational, Reveal LINQ Registry. Patients developing cryptogenic stroke and monitored by implantable loop recorder for searching AF were studied. The primary end point was incidence of AF within 36 months after insertion. Secondary end points were recurrent ischemic stroke/transient ischemic attack and AF-related treatment strategies. A total of 271 patients (61.6±14.3 years, 170 men, 60 from Japan) were enrolled from 12 countries. AF was detected in 28.2% at 36 months. The median time from enrollment to AF detection was 7.9 months. During the first 12 months, the AF detection rate slope was relatively steeper in the Japanese subgroup versus non-Japanese patients. However, by 3 years, the cumulative incidence of AF detection did not differ between groups. Age was the only variable associated with AF detection (hazard ratio, 1.05 [95% CI, 1.02-1.07] per year), trending higher in older age groups. Of the 271 patients, 11 (4.1%) developed recurrent ischemic stroke/transient ischemic attack; AF was detected by implantable loop recorder in only 1 of these patients. Patients with detected AF were more commonly taking oral anticoagulation than those without AF at the last follow-up (64.7% versus 25.3%, P<0.001). The rate of AF detection was similar to other studies in stroke populations monitored by implantable loop recorders, including CRYSTAL-AF (Cryptogenic Stroke and Underlying Atrial Fibrillation), STROKE-AF (Stroke of Unknown Cause and Underlying Atrial Fibrillation) and PER-DIEM (Post-Embolic Rhythm Detection With Implantable Versus External Monitoring). Patients with detected AF more commonly initiated anticoagulation than those without AF.

Creation of an Anomaly Detection System for Blockchain-based Cloud Deployments that Consider Privacy and Delay.

Cloud-based deployments are increasingly vulnerable to many kinds of assaults; hence powerful anomaly detection systems are needed to prevent security breaches. While effective to some degree, current methods like RSSI, GTM, and APG have drawbacks in terms of scalability, precision, and accuracy. In order to improve the forecast accuracy of cloud assaults and optimize blockchain-based cloud installations, this study suggests a novel anomaly detection system that combines multimodal feature analysis, deep learning models, and QoS-aware sidechains. The suggested strategy considerably surpasses traditional approaches in terms of precision, accuracy, recall, and AUC performance by optimizing feature variance across various sample types and utilizing cutting-edge deep learning algorithms. Additionally, the framework shows increased efficiency in terms of throughput, energy usage, and block mining latency, makingcombining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), the system analyses system logs to find anomalous patterns of activity linked to various threats. The architecture guarantees the transparency and integrity of system records through the use of Blockchain technology, while Deep Learning models offer accurate and quick anomaly detection. The advantages of both Deep Learning and Blockchain technology support the choice to merge them. Blockchain technology ensures the accuracy of anomaly detection and prevents tampering with system records by providing a distributed, immutable ledger. On the other hand, deep learning models produce high precision, accuracy, recall, and AUC measures because of their remarkable pattern recognition skills and ability to adjust to shifting attack strategies. Experimental data analyses show the effectiveness of the suggested framework.

Design and implementation of anti-mapping security access technology based on illegal scanning

Abstract In the current field of information security, illegal network scanning activities are prevalent, and such behaviors are usually aimed at detecting security vulnerabilities in network systems and preparing for future attack activities. This study proposes a secure access system based on anti-mapping technology, which aims to effectively block illegal scanning behaviors while ensuring that the normal access of legitimate users is not affected. The system integrates advanced behavioral analysis algorithms that utilize machine learning techniques for deep learning and pattern recognition of network traffic, and is able to accurately distinguish between normal user activities and malicious scanning attempts. At the core of the system is a set of dynamic adaptive identification mechanisms that update the detection algorithms in real time to adapt to emerging scanning techniques and attack strategies by continuously learning from changes in network traffic. In addition, the system employs role-based access control (RBAC) policies to enhance the protection of sensitive resources. The Secure Access Gateway is deployed at the boundary of the network to monitor and filter all ingress traffic, effectively intercepting unauthorized scanning activities by comprehensively evaluating the source, behavior and frequency of traffic. Experimental results show that the proposed two-layer network structure performs well in detecting common threats such as port scanning, DDoS attacks, and SQL injections, with an accuracy rate of over 95%. Especially for complex and covert APT (advanced persistent threat) attacks, the system can significantly reduce the false alarm rate and effectively improve the detection speed. However, when dealing with some highly customized malware, the system's recognition ability still needs to be improved, which indicates that future research needs to focus more on enhancing the ability to learn and adapt to unknown threats.

Robustness of generative AI detection: adversarial attacks on black-box neural text detectors

AbstractThe increased quality and human-likeness of AI generated texts has resulted in a rising demand for neural text detectors, i.e. software that is able to detect whether a text was written by a human or generated by an AI. Such tools are often used in contexts where the use of AI is restricted or completely prohibited, e.g. in educational contexts. It is, therefore, important for the effectiveness of such tools that they are robust towards deliberate attempts to hide the fact that a text was generated by an AI. In this article, we investigate a broad range of adversarial attacks in English texts with six different neural text detectors, including commercial and research tools. While the results show that no detector is completely invulnerable to adversarial attacks, the latest generation of commercial detectors proved to be very robust and not significantly influenced by most of the evaluated attack strategies.

Optimal attack strategy in system identification with binary‐valued observations: An information entropy‐based approach

AbstractWith the advancements in network technology and artificial intelligence, the interaction between network nodes is getting more frequent. The malicious data tampering can be extremely damaging to system stability, therefore the network security is of great importance. In this paper, we address the problem of system identification with binary‐valued observations under data tampering attacks and an information entropy‐based approach to optimal attack strategy from the viewpoint of attacker. Under the constrained attack energy, a method is proposed to obtain the optimal attack strategy using the information entropy as the optimization index. It is interestingly demonstrated that such optimal attack strategy is equivalent to the one by using the estimation error of the identification algorithm as the optimization index. The rationality of the proposed method is verified through numerical simulations.

An Optimal Attack Strategy Based on Adversary-Modified Historical Innovations

An Optimal Attack Strategy Based on Adversary-Modified Historical Innovations

Catch the Cyber Thief: A Multi-Dimensional Asymmetric Network Attack–Defense Game

This paper presents a novel multi-dimensional asymmetric game model for network attack–defense decision-making, called “Catch the Cyber Thief”. The model is built upon the concept of partially observable stochastic games (POSG) and is designed to systematically incorporate multi-dimensional asymmetry into network attack–defense problems. The attack agent is called a “thief” who wants to control a key host by exploring the unfamiliar network environment, and the defense agent is called a “police” who needs to catch the opponent before its goal is accomplished. The results indicate that the asymmetry of network attack and defense is not only influenced by attack and defense strategies but also by spatio-temporal factors such as the attacker’s initial position, network topology, and defense lag time. In addition, we have found that there may exist the “attack rhythm,” which makes “how to maintain a good attack rhythm” and “how to generate a robust defense strategy against different attackers” worth exploring. Compared with existing attack–defense game models, our game model can better generate a direct mapping relationship with real elements, enabling us to understand network attack and defense interactions better, recognize security risks, and design defense strategies that can directly serve real-world decision-making.

Risk Assessment of UAV Cyber Range Based on Bayesian–Nash Equilibrium

In order to analyze the choice of the optimal strategy of cyber security attack and defense in the unmanned aerial vehicles’ (UAVs) cyber range, a game model-based UAV cyber range risk assessment method is constructed. Through the attack and defense tree model, the risk assessment method is calculated. The model of attack and defense game with incomplete information is established and the Bayesian–Nash equilibrium of mixed strategy is calculated. The model and method focus on the mutual influence of the actions of both sides and the dynamic change in the confrontation process. According to the calculation methods of different benefits of different strategies selected in the offensive and defensive game, the risk assessment and calculation of the UAV cyber range are carried out based on the probability distribution of the defender’s benefits and the attacker’s optimal strategy selection. An example is given to prove the feasibility and effectiveness of this method.

Development status and challenges of anti-spoofing technology of GNSS/INS integrated navigation

The threat of spoofing interference has posed a severe challenge to the security application of Global Navigation Satellite System (GNSS). It is particularly urgent and critical to carry out in-depth defense research on spoofing interference. When combined with the inertial navigation system (INS), the GNSS/INS integrated navigation system offers distinct advantages in the field of anti-spoofing technology research, which has garnered significant attention in recent years. To summarize the current research achievements of GNSS/INS integrated navigation anti-spoofing technology, it is necessary to provide an overview of the three core technical aspects of spoofing attack principles and implementation strategies, spoofing detection, and spoofing mitigation. First, the principles and implementation strategies of spoofing interference attacks are introduced, and different classifications of spoofing interference attacks are given. Then, the performance characteristics and technical points of different spoofing detection and spoofing mitigation methods are compared and analyzed, and the shortcomings and challenges in the current development of GNSS/INS anti-spoofing technology are pointed out. Finally, based on the summary and shortcomings of the existing technology, a prospect for the future development of GNSS/INS integrated navigation anti-spoofing technology is discussed.

Activity centrality-based critical node identification in complex systems against cascade failure

Identifying critical nodes in the network has been a concern permanently. Cascading failure would cause catastrophic events, and in the field of cascading failure in complex networks, the structure and dynamics are considered as the key in the process of cascading failure. It is vital to have an applicable centrality to find critical nodes that could control and prevent the cascading failure. In this paper, we propose a steady-state activity centrality to evaluate the importance of each node, and the proposed centrality is related to the degree of each node and the activity of its neighbor nodes. The giant component, the average activity, and the tipping point under different attack strategies are introduced to compare the attack effect of these three centralities including steady-state activity centrality, betweenness centrality and closeness centrality. The results show that the attack effect under the proposed centrality is better than the effect under the other two centralities. In particular, for the network with the SIS and gene regulation dynamic, the attack effect under the steady-state activity centrality driven strategy is obviously better than the effect under the betweenness centrality driven strategy when the network is heterogeneous.

Modeling and detection of false data injection attacks in cyber-physical distribution system with load aggregator interaction

With the increasing number of users and multi-type loads accessing the cyber–physical distribution system (CPDS), the bidirectional interaction between the system becomes more and more frequent. The Load aggregator (LA) also plays an increasingly important role in the interaction process. However, the LA’s high dependence on information and lack of security measures make it vulnerable to attacks, so this paper analyzes the interactive security of the LAs from two points. Considering the game process between the LAs and users from the attacker’s point of view, this paper puts forward an attack strategy aiming cost function of the LAs, establishes a bi-level multi-objective programming attack model of false data injection attacks(FDIAs), and proposes an attack detection method based on the multi-state matching method and improved similar daily data preprocessing generative adversarial network (P-GAN) from the defender’s point of view to defend against the above attack strategy. Furthermore, a hybrid detection mechanism combining event triggering and periodic detection is proposed to ensure response speed and adaptability of detection. The effectiveness of the proposed attack model and the detection method is verified by simulation analysis.