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Journal articles on the topic 'Defense/attack strategies'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Mr., Ajas K. S1, Fares Rahman Mr., Joel Deleep Mr., Joemon Johnson 4. Mr., and Shejina N. M. K. A5 Ms. "PENETRATION TESTING AND DEFENSE STRATEGIES." International Journal of Advances in Engineering & Scientific Research 3, no. 6 (2016): 11–19. https://doi.org/10.5281/zenodo.10774423.

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<strong>Abstract: </strong>   <strong>Objective- </strong>Penetration testing helps to secure networks, and highlights the security issues. In this paper investigate different aspects of penetration testing including tools, attack methodologies, and defense strategies.  More specifically, we performed different penetration tests using a private networks, devices, and virtualized systems and tools. We predominately used tools within the Kali Linux suite. The attacks we performed included: Smartphone penetration testing, hacking phones Bluetooth, traffic sniffing, hacking WPA Protected

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Liu, Jing, Yuchen Zhang, Hao Hu, Jinglei Tan, Qiang Leng, and Chaowen Chang. "Efficient Defense Decision-Making Approach for Multistep Attacks Based on the Attack Graph and Game Theory." Mathematical Problems in Engineering 2020 (August 11, 2020): 1–12. http://dx.doi.org/10.1155/2020/9302619.

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In the multistep attack scenario, each rational attack-defense player tries to maximize his payoff, but the uncertainty about his adversary prevents him from taking the favorable actions. How to select the best strategy from the candidate strategies to maximize the defense payoff becomes the core issue. For this purpose, the paper innovatively designs a game theory model from the point of network survivability in combination with the attribute attack graph. The attack graph is created based on the network connectivity and known vulnerabilities using the MulVAL toolkit, which gives the full vie

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Gong, Xiaopeng, Wanchun Chen, and Zhongyuan Chen. "Intelligent Game Strategies in Target-Missile-Defender Engagement Using Curriculum-Based Deep Reinforcement Learning." Aerospace 10, no. 2 (2023): 133. http://dx.doi.org/10.3390/aerospace10020133.

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Aiming at the attack and defense game problem in the target-missile-defender three-body confrontation scenario, intelligent game strategies based on deep reinforcement learning are proposed, including an attack strategy applicable to attacking missiles and active defense strategy applicable to a target/defender. First, based on the classical three-body adversarial research, the reinforcement learning algorithm is introduced to improve the purposefulness of the algorithm training. The action spaces the reward and punishment conditions of both attack and defense confrontation are considered in t

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Hu, Yongjin, Han Zhang, Yuanbo Guo, Tao Li, and Jun Ma. "A Novel Attack-and-Defense Signaling Game for Optimal Deceptive Defense Strategy Choice." Wireless Communications and Mobile Computing 2020 (October 12, 2020): 1–10. http://dx.doi.org/10.1155/2020/8850356.

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Increasingly, more administrators (defenders) are using defense strategies with deception such as honeypots to improve the IoT network security in response to attacks. Using game theory, the signaling game is leveraged to describe the confrontation between attacks and defenses. However, the traditional approach focuses only on the defender; the analysis from the attacker side is ignored. Moreover, insufficient analysis has been conducted on the optimal defense strategy with deception when the model is established with the signaling game. In our work, the signaling game model is extended to a n

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Chen, Jun, Xin Sun, Wen Tian, and Guangjie Liu. "A Bayesian game approach for node-based attribution defense against asymmetric information attacks in IoT networks." PLOS ONE 20, no. 3 (2025): e0316091. https://doi.org/10.1371/journal.pone.0316091.

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In the rapidly evolving landscape of the Internet of Things (IoT), traditional defense mechanisms struggle to counter sophisticated attribution attacks, especially under asymmetric information conditions. This paper introduces a novel Bayesian game framework—the Node-Based Attribution Attack-Defense Bayesian Game (NAADBG) Model—to address these challenges in IoT networks. The model incorporates a comprehensive set of attacker and defender profiles, capturing the complexities of real-world security scenarios. We develop a refined method for quantifying the payoffs of node-level attack-defense a

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Tan, Hao, Le Wang, Huan Zhang, Junjian Zhang, Muhammad Shafiq, and Zhaoquan Gu. "Adversarial Attack and Defense Strategies of Speaker Recognition Systems: A Survey." Electronics 11, no. 14 (2022): 2183. http://dx.doi.org/10.3390/electronics11142183.

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Speaker recognition is a task that identifies the speaker from multiple audios. Recently, advances in deep learning have considerably boosted the development of speech signal processing techniques. Speaker or speech recognition has been widely adopted in such applications as smart locks, smart vehicle-mounted systems, and financial services. However, deep neural network-based speaker recognition systems (SRSs) are susceptible to adversarial attacks, which fool the system to make wrong decisions by small perturbations, and this has drawn the attention of researchers to the security of SRSs. Unf

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Urbina Escobar, Tomás. "Self-Defense Strategies Against Cyber- Attacks by Non-State Actors." Revista de Relaciones Internacionales, Estrategia y Seguridad 18, no. 2 (2023): 61–72. http://dx.doi.org/10.18359/ries.6639.

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This article aims to analyze and provide a state-of-the-art conceptualization of the notion of self-defense within international law and the cyber realm. In the first section, the paper explores the concepts of self-defense, attack, and the use of force in international law, followed by their application in the cyberspace. The subsequent section, specifically applies the concept of self-defense in the event of a cyber-attack perpetrated by a non-state actors. The conclusion highlights that the cyber realm context introduces the possibility that aggressions from non-state actors can yield conse

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González-Landero, Franks, Iván García-Magariño, Raquel Lacuesta, and Jaime Lloret. "ABS-DDoS: An Agent-Based Simulator about Strategies of Both DDoS Attacks and Their Defenses, to Achieve Efficient Data Forwarding in Sensor Networks and IoT Devices." Wireless Communications and Mobile Computing 2018 (June 24, 2018): 1–11. http://dx.doi.org/10.1155/2018/7264269.

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Sensor networks and Internet of Things (IoT) are useful for many purposes such as military defense, sensing in smart homes, precision agriculture, underwater monitoring in aquaculture, and ambient-assisted living for healthcare. Efficient and secure data forwarding is essential to maintain seamless communications and to provide fast services. However, IoT devices and sensors usually have low processing capabilities and vulnerabilities. For example, attacks such as the Distributed Denial of Service (DDoS) can easily hinder sensor networks and IoT devices. In this context, the current approach p

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Zhu, Yuwen, Lei Yu, Houhua He, and Yitong Meng. "A Defense Strategy Selection Method Based on the Cyberspace Wargame Model." Security and Communication Networks 2021 (October 27, 2021): 1–12. http://dx.doi.org/10.1155/2021/4292670.

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Network defenders always face the problem of how to use limited resources to make the most reasonable decision. The network attack-defense game model is an effective means to solve this problem. However, existing network attack-defense game models usually assume that defenders will no longer change defense strategies after deploying them. However, in an advanced network attack-defense confrontation, defenders usually redeploy defense strategies for different attack situations. Therefore, the existing network attack-defense game models are challenging to accurately describe the advanced network

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10

Bi, Chuanxiang, Shang Shi, and Jian Qu. "Enhancing Autonomous Driving: A Novel Approach of Mixed Attack and Physical Defense Strategies." ASEAN Journal of Scientific and Technological Reports 28, no. 1 (2024): e254093. https://doi.org/10.55164/ajstr.v28i1.254093.

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Adversarial attacks are a significant threat to autonomous driving safety, especially in the physical world where there is a prevalence of "sticker-paste" attacks on traffic signs. However, most of these attacks are single-category attacks with little interference effect. This paper builds an autonomous driving platform and conducts extensive experiments on five single-category attacks. Moreover, we proposed a new physical attack - a mixed attack consisting of different single-category physical attacks. The proposed method outperforms existing methods and can reduce the accuracy of traffic sig

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Abomakhelb, Abdulruhman, Kamarularifin Abd Jalil, Alya Geogiana Buja, Abdulraqeb Alhammadi, and Abdulmajeed M. Alenezi. "A Comprehensive Review of Adversarial Attacks and Defense Strategies in Deep Neural Networks." Technologies 13, no. 5 (2025): 202. https://doi.org/10.3390/technologies13050202.

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Artificial Intelligence (AI) security research is promising and highly valuable in the current decade. In particular, deep neural network (DNN) security is receiving increased attention. Although DNNs have recently emerged as a prominent tool for addressing complex challenges across various machine learning (ML) tasks and DNNs stand out as the most widely employed, as well as holding a significant share in both research and industry, DNNs exhibit vulnerabilities to adversarial attacks where slight but intentional perturbations can deceive DNNs models. Consequently, several studies have propose

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Yang, Jidong, Qiangyun Chi, Wenqiang Xu, and Huaike Yu. "Research on adversarial attack and defense of large language models." Applied and Computational Engineering 93, no. 1 (2024): 105–13. http://dx.doi.org/10.54254/2755-2721/93/20240922.

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Abstract. Large language models (LLMs) have made excellent progress in text and image understanding and generation. However, with the wide range of applications of these models in various industries, the issue of their security, especially the defense against adversarial attacks, has become a focus of research. This study focuses on exploring the adversarial attacks faced by LLMs and their defense strategies, especially the design and optimization of defense mechanisms. Through literature review and case studies, this paper analyzes in detail the white-box and black-box attack patterns against

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Allheeib, Nasser. "Securing Machine Learning Against Data Poisoning Attacks." International Journal of Data Warehousing and Mining 20, no. 1 (2024): 1–21. http://dx.doi.org/10.4018/ijdwm.358335.

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The emergence of intelligent networks has revolutionized the use of machine learning (ML), allowing it to be applied in various domains of human life. This literature review paper provides in-depth analysis of the existing research on data poisoning attacks and examines how intelligent networks can mitigate these threats. Specifically, the author explores how malicious users inject fake training data into adversarial networks, a technique known as a data poisoning attack, which can severely compromise the model's integrity. Through a comparative evaluation of the attack strategies and defense

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Ahn, Jongmin, Hee-Yong Kwon, Bohyun Ahn, et al. "Toward Quantum Secured Distributed Energy Resources: Adoption of Post-Quantum Cryptography (PQC) and Quantum Key Distribution (QKD)." Energies 15, no. 3 (2022): 714. http://dx.doi.org/10.3390/en15030714.

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Quantum computing is a game-changing technology that affects modern cryptography and security systems including distributed energy resources (DERs) systems. Since the new quantum era is coming soon in 5–10 years, it is crucial to prepare and develop quantum-safe DER systems. This paper provides a comprehensive review of vulnerabilities caused by quantum computing attacks, potential defense strategies, and remaining challenges for DER networks. First, new security vulnerabilities and attack models of the cyber-physical DER systems caused by quantum computing attacks are explored. Moreover, this

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Wenyuan Xu, Ke Ma, W. Trappe, and Yanyong Zhang. "Jamming sensor networks: attack and defense strategies." IEEE Network 20, no. 3 (2006): 41–47. http://dx.doi.org/10.1109/mnet.2006.1637931.

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LATES, Ionut, and Catalin BOJA. "Automating Attack and Defense Strategies in Cybersecurity." Informatica Economica 29, no. 1/2025 (2025): 5–15. https://doi.org/10.24818/issn14531305/29.1.2025.01.

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Kim, Jay. "Strategic Defense in Machine Learning: Assessing the Most Optimal Defense Approach to Mitigate Adversarial Cyber Attacks." Journal of Robotics and Automation Research 6, no. 2 (2025): 01–04. https://doi.org/10.33140/jrar.06.02.02.

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In the era of AI proliferation, developing robust defense mechanisms against adversarial cyberattacks is critical. This project focuses on identifying and evaluating the most effective defense strategy to protect AI models from adversarial attacks. To mitigate overfitting, the baseline AI model was constructed with 2 convolutional layers, a dense layer of 256 nodes, pooling, and dropout layers. This foundational model demonstrated exceptional proficiency, achieving a 99.5% accuracy rate on the Modified National Institute of Standards and Technology (MNIST) dataset. The next three defense metho

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Zhang, Chunming. "Impact of Defending Strategy Decision on DDoS Attack." Complexity 2021 (March 15, 2021): 1–11. http://dx.doi.org/10.1155/2021/6694383.

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Distributed denial-of-service (DDoS) attack is a serious threat to cybersecurity. Many strategies used to defend against DDoS attacks have been proposed recently. To study the impact of defense strategy selection on DDoS attack behavior, the current study uses logistic function as basis to propose a dynamic model of DDoS attacks with defending strategy decisions. Thereafter, the attacked threshold of this model is calculated. The existence and stability of attack-free and attacked equilibria are proved. Lastly, some effective strategies to mitigate DDoS attacks are suggested through parameter

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Liu, Yingze, and Yuanbo Guo. "Towards Real-Time Warning and Defense Strategy AI Planning for Cyber Security Systems Aided by Security Ontology." Electronics 11, no. 24 (2022): 4128. http://dx.doi.org/10.3390/electronics11244128.

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Cyber security systems generally have the phenomena of passive defense and low-efficiency early warnings. Aiming at the above problems, this study proposes a real-time warning and plans an AI defense strategy for a cyber security system aided by a security ontology. First, we design a security defense ontology that integrates attack graphs, general purpose and domain-specific knowledge bases, and on this basis, we (1) develop an ontology-driven method of early warnings of real-time attacks, which supports non-intrusive scanning attack detection and (2) combine artificial intelligence planning

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Mishra, Chinmayee, Pullam Bhatla Laxmi Sindhu, Pruthwiraj Mohanty, and Ayush Kumar Samrat. "DDOS Attacks and Analysis of Different Defense Mechanisms." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem40061.

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Denial of Service( DOS) attacks are an immense trouble to internet spots and among the hardest security problems in moment’s Internet. Of particular concern- because of their implicit impact- are the Distributed Denial of Service( DDoS) attacks. With little or no advance advising a DDoS attack can fluently exhaust the computing and communication coffers of its victim within a short period of time. This paper presents the problem of DDoS attacks and develops a bracket of DDoS defence systems. Description of each attack and defence system order is provided ,along with the advantages and disadvan

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Wang, Wenhao, Xingguo Chen, Yuwei Li, and Cheng Zhu. "Catch the Cyber Thief: A Multi-Dimensional Asymmetric Network Attack–Defense Game." Applied Sciences 14, no. 20 (2024): 9234. http://dx.doi.org/10.3390/app14209234.

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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 asym

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Jin, Bao, Xiaodong Zhao, and Dongmei Yuan. "Attack–Defense Confrontation Analysis and Optimal Defense Strategy Selection Using Hybrid Game Theoretic Methods." Symmetry 16, no. 2 (2024): 156. http://dx.doi.org/10.3390/sym16020156.

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False data injection attacks are executed in the electricity markets of smart grid systems for financial benefits. The attackers can maximize their profits through modifying the estimated transmission power and changing the prices of market electricity. As a response, defenders need to minimize expected load losses and generator trips through load and power generation adjustments. The selection of strategies of the attacking and defending sides turns out to be a symmetric game process. This article proposes a hybrid game theory method for analyzing the attack–defense confrontation: firstly, a

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NIKLEKAJ, Malvina, and Elfat MEMAJ. "Analyzing and Mitigating Distributed Denial-of-Service (DDoS) Attacks - A Python-Based Simulation Approach." INGENIOUS 5, no. 1 (2025): 20–36. https://doi.org/10.58944/adif5937.

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The increasing prevalence of Distributed Denial of Service (DDoS) attacks poses a significant threat to the security and availability of online services and networks. These attacks leverage multiple compromised systems to overwhelm a target, rendering it inaccessible to legitimate users. This research presents an in-depth analysis of DDoS attack methodologies, their classification into volumetric, protocol-based, and application-layer attacks, and their real-world implications. To enhance understanding and mitigation strategies, this study introduces a Python-based simulation tool that replica

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Zhang, Yuchen, and Jing Liu. "Optimal Decision-Making Approach for Cyber Security Defense Using Game Theory and Intelligent Learning." Security and Communication Networks 2019 (December 23, 2019): 1–16. http://dx.doi.org/10.1155/2019/3038586.

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Existing approaches of cyber attack-defense analysis based on stochastic game adopts the assumption of complete rationality, but in the actual cyber attack-defense, it is difficult for both sides of attacker and defender to meet the high requirement of complete rationality. For this aim, the influence of bounded rationality on attack-defense stochastic game is analyzed. We construct a stochastic game model. Aiming at the problem of state explosion when the number of network nodes increases, we design the attack-defense graph to compress the state space and extract network states and defense st

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Smith, Oliver, and Anderson Brown. "Comprehensive Review on Advanced Adversarial Attack and Defense Strategies in Deep Neural Network." International Journal of Research and Innovation in Applied Science VIII, no. IV (2023): 156–66. http://dx.doi.org/10.51584/ijrias.2023.8418.

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In adversarial machine learning, attackers add carefully crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions. In this paper, we did comprehensive review of some of the most recent research, advancement and discoveries on adversarial attack, adversarial sampling generation, the potency or effectiveness of each of the existing attack methods, we also did comprehensive review on some of the most recent research, advancement and discoveries on adversarial defense strategies, the effectiveness of each defense met

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Vichi, Leonardo Perin. "CYBERDETERRENCE AS POLICY FOR DEMOCRACY DEFENSE." IA Policy Brief Series 1, no. 1 (2022): 1–3. https://doi.org/10.5281/zenodo.5829466.

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PsyOps aiming attack opponents through misinformation are recurring activities which records can be found since ancient history. Nevertheless, misinformation activities in cyber domain brought new dynamics and methods to this kind of operations. Dealing with governmental misinformation is an ability journalists learn still in the first years in university; however, it has been often detected complex misinformation strategies being used as offensive approaches on strategic communication, aiming undermining and weaken democratic infrastructure in many countries. Two strategies are more recurring

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Baryshnikov, N. V. "STRATEGIES OF EQUAL STATUS INTERCULTURAL COMMUNICATION." MGIMO Review of International Relations, no. 6(33) (December 28, 2013): 90–94. http://dx.doi.org/10.24833/2071-8160-2013-6-33-90-94.

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This article is aimed at arguing a professional type of intercultural communication,at substantiating status equality as a paramount distinctive feature of professional interculturalcommunication and also at presenting some of the strategies associated with it.Thereupon, in the article:– the notion of professional intercultural communication is validated and its integral part – statusequality - is grounded,– original strategies of equal status intercultural communication are presented, some of which include:rabulistic strategies of communicative attack, eristic strategies of communicative self

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Ke, Hu, Jian Xu, Yong Wang, Heyao Chen, and Zepeng Shen. "Adversarial Machine Learning in Cybersecurity: Attacks and Defenses." International Journal of Management Science Research 8, no. 2 (2025): 26–33. https://doi.org/10.53469/ijomsr.2025.08(02).04.

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Adversarial Machine Learning (AML) refers to the research field that involves testing and improving machine learning models by introducing adversarial samples or attack techniques. In the cybersecurity domain, AML has significant potential to help identify and defend against threats such as malware, cyber attacks, and identity fraud. However, AML also faces numerous challenges, including low efficiency in generating adversarial samples, insufficient stealth, and issues with the generality and adaptability of defense methods. There is a dynamic interplay between adversarial attacks and defenses

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Lin, Frank Yeong-Sung, Yu-Shun Wang, and Ming-Yang Huang. "Effective Proactive and Reactive Defense Strategies against Malicious Attacks in a Virtualized Honeynet." Journal of Applied Mathematics 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/518213.

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Virtualization plays an important role in the recent trend of cloud computing. It allows the administrator to manage and allocate hardware resources flexibly. However, it also causes some security issues. This is a critical problem for service providers, who simultaneously strive to defend against malicious attackers while providing legitimate users with high quality service. In this paper, the attack-defense scenario is formulated as a mathematical model where the defender applies both proactive and reactive defense mechanisms against attackers with different attack strategies. In order to si

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Butts, Jonathan, Mason Rice, and Sujeet Shenoi. "An Adversarial Model for Expressing Attacks on Control Protocols." Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 9, no. 3 (2012): 243–55. http://dx.doi.org/10.1177/1548512911449409.

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In this paper we present a model for expressing attacks on control protocols that involve the exchange of messages. Attacks are modeled using the notion of an attacker who can block and/or fabricate messages. These two attack mechanisms cover a variety of scenarios ranging from power grid failures to cyber attacks on oil pipelines. The model provides a method to syntactically express communication systems and attacks, which supports the development of attack and defense strategies. For demonstration purposes, an attack instance is modeled that shows how a targeted messaging attack can result i

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Zhang, Yihong, Guojia Li, Yihui Zhang, Yan Cao, Mingyue Cao, and Chengyao Xue. "BASK: Backdoor Attack for Self-Supervised Encoders with Knowledge Distillation Survivability." Electronics 14, no. 13 (2025): 2724. https://doi.org/10.3390/electronics14132724.

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Backdoor attacks in self-supervised learning pose an increasing threat. Recent studies have shown that knowledge distillation can mitigate these attacks by altering feature representations. In response, we propose BASK, a novel backdoor attack that remains effective after distillation. BASK uses feature weighting and representation alignment strategies to implant persistent backdoors into the encoder’s feature space. This enables transferability to student models. We evaluated BASK on the CIFAR-10 and STL-10 datasets and compared it with existing self-supervised backdoor attacks under four adv

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Wang, Qingya, Yi Wu, Haojun Xuan, and Huishu Wu. "FLARE: A Backdoor Attack to Federated Learning with Refined Evasion." Mathematics 12, no. 23 (2024): 3751. http://dx.doi.org/10.3390/math12233751.

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Federated Learning (FL) is vulnerable to backdoor attacks in which attackers inject malicious behaviors into the global model. To counter these attacks, existing works mainly introduce sophisticated defenses by analyzing model parameters and utilizing robust aggregation strategies. However, we find that FL systems can still be attacked by exploiting their inherent complexity. In this paper, we propose a novel three-stage backdoor attack strategy named FLARE: A Backdoor Attack to Federated Learning with Refined Evasion, which is designed to operate under the radar of conventional defense strate

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R., Ramkumar, Rahul R., and Gowtham Sri. "Anomaly Based Approach for Defending Denial of Service Attack in Web Traffic." COMPUSOFT: An International Journal of Advanced Computer Technology 04, no. 04 (2015): 1657–64. https://doi.org/10.5281/zenodo.14776346.

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Distributed Denial of Service (DDOS) attacks has become a great threat for internet security. This attackis an advanced form of DOS (Denial of Service) attack. This attack changes its whole origin ID and it gives trouble to find it out and it has become a serious threat for internet security. Almost all traditional services such as bank websites, power resources, medical, educational institutions and military are extended to World Wide Web and thus many people widely use internet services. As many users of Internet is mandatory, network security for attacks are also increasing. Current DDoS at

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Huang, Youpeng, and Yong Lv. "Research on Network Attack Early Warning and Defense Strategies for Blockchain Networks." Frontiers in Computing and Intelligent Systems 12, no. 1 (2025): 178–83. https://doi.org/10.54097/1c71e118.

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Blockchain technology has gained significant attention due to its decentralized and secure nature. However, despite its robustness, blockchain networks are not immune to attacks. This research aims to explore the early warning and defense strategies for network attacks in blockchain systems. By analyzing existing vulnerabilities and attack vectors, we propose a comprehensive framework for detecting and mitigating threats. The study emphasizes the importance of proactive measures to enhance the security posture of blockchain networks.

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Jin, Di, Bingdao Feng, Siqi Guo, Xiaobao Wang, Jianguo Wei, and Zhen Wang. "Local-Global Defense against Unsupervised Adversarial Attacks on Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8105–13. http://dx.doi.org/10.1609/aaai.v37i7.25979.

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Unsupervised pre-training algorithms for graph representation learning are vulnerable to adversarial attacks, such as first-order perturbations on graphs, which will have an impact on particular downstream applications. Designing an effective representation learning strategy against white-box attacks remains a crucial open topic. Prior research attempts to improve representation robustness by maximizing mutual information between the representation and the perturbed graph, which is sub-optimal because it does not adapt its defense techniques to the severity of the attack. To address this issue

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Sandeep, Phanireddy. "API Security: Offensive and Defensive Strategies." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 10, no. 4 (2024): 1–5. https://doi.org/10.5281/zenodo.14883156.

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Having realized that APIs are the core of web and mobile apps, securing APIs has become inevitable. This paper aims to discuss the attack and defense techniques for APIs including injection attacks, broken authentication and information leakage problems. The paper addresses reliable measures including authentication methodologies, rate limiting, and encryption for API protection. Considering the above findings of the main forms of offensives and their countermeasures, this paper seeks to offer developers and cybersecurity professionals in the industry insights on improving the API security. Th

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Chen, Pei-Yu, and Frank Yeong-Sung Lin. "Recovery and Resource Allocation Strategies to Maximize Mobile Network Survivability by Using Game Theories and Optimization Techniques." Journal of Applied Mathematics 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/207141.

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With more and more mobile device users, an increasingly important and critical issue is how to efficiently evaluate mobile network survivability. In this paper, a novel metric called Average Degree of Disconnectivity (Average DOD) is proposed, in which the concept of probability is calculated by the contest success function. The DOD metric is used to evaluate the damage degree of the network, where the larger the value of the Average DOD, the more the damage degree of the network. A multiround network attack-defense scenario as a mathematical model is used to support network operators to predi

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Liang, Xinyu. "Security Challenges and Defense Strategies in Blockchain Systems." Applied and Computational Engineering 135, no. 1 (2025): 105–14. https://doi.org/10.54254/2755-2721/2025.21087.

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In the general use of blockchain technology, its decentralization, transparency, and immutability have demonstrated significant value in fields such as finance, logistics, healthcare, and public administration. However, this technology also faces a series of security and performance challenges. Especially in aspects of resisting malicious attacks, there is yet room for refinement. The integrated application of blockchain technology has gradually become an important driving force for new technological innovation and industrial transformation. Various industries have increased investment, resear

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Liu, Xiaohu, Hengwei Zhang, Yuchen Zhang, Lulu Shao, and Jihong Han. "Active Defense Strategy Selection Method Based on Two-Way Signaling Game." Security and Communication Networks 2019 (November 29, 2019): 1–14. http://dx.doi.org/10.1155/2019/1362964.

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Most network security research studies based on signaling games assume that either the attacker or the defender is the sender of the signal and the other party is the receiver of the signal. The attack and defense process is commonly modeled and analyzed from the perspective of one-way signal transmission. Aiming at the reality of two-way signal transmission in network attack and defense confrontation, we propose a method of active defense strategy selection based on a two-way signaling game. In this paper, a two-way signaling game model is constructed to analyze the network attack and defense

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GENÇOĞLU, Muharrem Tuncay. "Mathematical Modeling of Cyber Attack and Defense." Bilgisayar Bilimleri ve Teknolojileri Dergisi 3, no. 1 (2022): 10–16. http://dx.doi.org/10.54047/bibted.997908.

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In this paper, a new game formulation is proposed that combines simulation and game-theoretical approaches to the application of security games in cyberspace. The model presented here builds upon a security economic approach that models the adversary and defender motives and goals in the context of empirically derived countermeasure efficacy metrics. The approach is based on a two-player strategic game to determine optimal strategy selection for both adversary and defender. Besides, not only the solution to the game but also a mathematical and graphical representation of “what if” scenarios in

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Tang, Xun, Pengzhi Yin, Zehao Zhou, and Duan Huang. "Adversarial Perturbation Elimination with GAN Based Defense in Continuous-Variable Quantum Key Distribution Systems." Electronics 12, no. 11 (2023): 2437. http://dx.doi.org/10.3390/electronics12112437.

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Machine learning is being applied to continuous-variable quantum key distribution (CVQKD) systems as defense countermeasures for attack classification. However, recent studies have demonstrated that most of these detection networks are not immune to adversarial attacks. In this paper, we propose to implement typical adversarial attack strategies against the CVQKD system and introduce a generalized defense scheme. Adversarial attacks essentially generate data points located near decision boundaries that are linearized based on iterations of the classifier to lead to misclassification. Using the

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Liu, Yina. "Defense strategies against data poisoning attacks in AI financial risk control models." Advances in Engineering Innovation 16, no. 7 (2025): None. https://doi.org/10.54254/2977-3903/2025.25182.

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Against the backdrop of rapid fintech development, Artificial Intelligence(abbreviated as AI)financial risk control models have been widely applied in financial risk assessment and management due to their efficiency and accuracy. However, data poisoning attacks, as a malicious means targeting model training data, severely threaten the reliability and security of these models. From a professional and technical perspective, this paper deeply analyzes the principles of AI financial risk control models and data poisoning attacks, systematically sorts out the existing problems in the current respon

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Huang, Yuchen. "Research on Network Security Threat Detection and Defense Strategies." Applied and Computational Engineering 120, no. 1 (2024): 82–87. https://doi.org/10.54254/2755-2721/2025.18746.

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With the popularization of the Internet and the rapid development of information technology, network security issues have become increasingly prominent in the context of research. People are facing new network security threats brought about by the development of information technology. Network attacks are becoming increasingly complex, leading to frequent incidents of data breaches. This has also made cybersecurity an important area of competition between countries. The significance of network security lies in protecting personal information security, safeguarding the interests of enterprises,

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N., Naga Lakshmi. "Implementation of DOS Attack Using NS2." International Journal of Emerging Science and Engineering (IJESE) 12, no. 6 (2024): 1–4. https://doi.org/10.35940/ijese.F9859.12060524.

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<strong>Abstract:</strong> This paper presents a comprehensive study on the implementation of a Denial of Service (DOS) attack using NS2, a widely-used network simulator. The project involves the installation and configuration of NS2 and NAM on Ubuntu, the design of a realistic network topology, and the generation of TCP and UDP traffic to simulate a DOS attack. By evaluating the impact of the attack on network performance metrics such as throughput and latency, this study aims to enhance understanding of DOS attacks in simulated environments and propose effective mitigation strategies. The fi

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Ashraf, Syeda Nazia, Raheel Siddiqi, and Humera Farooq. "Auto encoder-based defense mechanism against popular adversarial attacks in deep learning." PLOS ONE 19, no. 10 (2024): e0307363. http://dx.doi.org/10.1371/journal.pone.0307363.

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Convolutional Neural Network (CNN)-based models are prone to adversarial attacks, which present a significant hurdle to their reliability and robustness. The vulnerability of CNN-based models may be exploited by attackers to launch cyber-attacks. An attacker typically adds small, carefully crafted perturbations to original medical images. When a CNN-based model receives the perturbed medical image as input, it misclassifies the image, even though the added perturbation is often imperceptible to the human eye. The emergence of such attacks has raised security concerns regarding the implementati

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Wang, Le, Jianyu Deng, Haonan Tan, et al. "AARF: Autonomous Attack Response Framework for Honeypots to Enhance Interaction Based on Multi-Agent Dynamic Game." Mathematics 12, no. 10 (2024): 1508. http://dx.doi.org/10.3390/math12101508.

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Highly interactive honeypots can form reliable connections by responding to attackers to delay and capture intranet attacks. However, current research focuses on modeling the attacker as part of the environment and defining single-step attack actions by simulation to study the interaction of honeypots. It ignores the iterative nature of the attack and defense game, which is inconsistent with the correlative and sequential nature of actions in real attacks. These limitations lead to insufficient interaction of the honeypot response strategies generated by the study, making it difficult to suppo

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Bassey, Christian, Success Imakuh, and Festus Zindozin. "Cybersecurity in Critical Infrastructure: Defending Against Nation-State Groups." South Asian Research Journal of Engineering and Technology 6, no. 05 (2024): 140–50. http://dx.doi.org/10.36346/sarjet.2024.v06i05.004.

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Cyber-attacks on critical infrastructure can be disastrous and undermine states' national security. Different groups execute these attacks for varying reasons; some may be state-sponsored, and their attack for geopolitical reasons or to achieve strategic national cyber objectives. Regardless of the nation-state actor, it is essential to identify the techniques used and defend critical infrastructure against these attacks. This study evaluated the attack methodology of five nation-state actors based on the MITRE ATT&CK ICS matrix and proposed a multi-layered defense architecture. A virtual

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Navya, E., B. Samson Vitty, J. Aruna Kumari, K.V.L.Supraja, D. Prithvi Raj, and P. Venkatesh. "Ecological Warfare: Natural Plant Defense Mechanism against Insect Pests." Journal of Experimental Agriculture International 47, no. 4 (2025): 139–50. https://doi.org/10.9734/jeai/2025/v47i43364.

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Plants employ a diverse array of morphological, biochemical, and molecular defense strategies to mitigate the impact of herbivore attacks. Biochemical defenses, in particular, are highly dynamic and function through both direct and indirect mechanisms. Defensive compounds may be produced continuously or synthesized in response to herbivory, influencing insect feeding behavior, growth, and survival. Additionally, plants emit volatile organic compounds that attract natural predators of herbivores, enhancing their protective response. Although studies were conducted on plant defense research, a s

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Walton, Claire, Isaac Kaminer, Qi Gong, Abram H. Clark, and Theodoros Tsatsanifos. "Defense against Adversarial Swarms with Parameter Uncertainty." Sensors 22, no. 13 (2022): 4773. http://dx.doi.org/10.3390/s22134773.

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This paper addresses the problem of optimal defense of a high-value unit (HVU) against a large-scale swarm attack. We discuss multiple models for intra-swarm cooperation strategies and provide a framework for combining these cooperative models with HVU tracking and adversarial interaction forces. We show that the problem of defending against a swarm attack can be cast in the framework of uncertain parameter optimal control. We discuss numerical solution methods, then derive a consistency result for the dual problem of this framework, providing a tool for verifying computational results. We als

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Kose, Seima, Yumi Suenaga, and Kazumasa Oida. "Real-time Attack-Scheme Visualization for Complex Exploit Technique Comprehension." International Journal of Machine Learning and Computing 11, no. 2 (2021): 164–69. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1030.

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Recent exploit techniques are highly complex, and it is not easy for cybersecurity learners to understand the attack strategies quickly and clearly. For efficient and comprehensive learning, this paper proposes an attack-scheme visualization system that fulfills three requirements: attack progress visualization in real-time, memory and register-level description, and concise description of the attack schemes. This paper exemplifies two cases: stack buffer overflow and ROP attacks, and demonstrates how the system operates and how users can learn that existing defense technologies are effective

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