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Journal articles on the topic 'Cyber Defense Mechanisms'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Staszczyk, Artur. "European Parliament Position on EU Cyber Security and Defense Policy." Reality of Politics 10, no. 1 (2019): 122–33. http://dx.doi.org/10.15804/rop201908.

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Ensuring cyber security in scope of cyber defense is currently among the top priorities of the EU Common Security and Defense Policy (CSDP). Matters included in scope of cyber defense are a competence of the Member States and cooperation at EU level in this area is governed by decisions of the EU Council based on unanimity. This means that the European Parliament (EP) in the field of cyber defens acts only as an opinion-forming body expressing its position through the adoption of non-legislative resolutions. The aim of the article is to analyze the content of these resolutions and present the

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Kingsley David Onyewuchi Ofoegbu, Olajide Soji Osundare, Chidiebere Somadina Ike, Ololade Gilbert Fakeyede, and Adebimpe Bolatito Ige. "Data-Driven Cyber Threat Intelligence: Leveraging Behavioral Analytics for Proactive Defense Mechanisms." Computer Science & IT Research Journal 4, no. 3 (2023): 502–24. http://dx.doi.org/10.51594/csitrj.v4i3.1501.

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As the cyber threat landscape becomes increasingly sophisticated, traditional defense mechanisms often fall short in anticipating and mitigating advanced attacks. The paper explores the critical role of behavioral analytics in transforming cyber threat intelligence (CTI) into a proactive defense strategy. This study underscores the importance of analyzing user behavior patterns, network activity, and system interactions to detect anomalies that may indicate potential threats. By leveraging large datasets and advanced analytical techniques, organizations can move beyond reactive cybersecurity m

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SWAPNA, S. L., and Vidya SIVALINGAM. "AI Integration and Its Impact on Cyber Defense: A Theoretical Analysis." International Journal of Information Security and Cybercrime 14, no. 1 (2025): 76–87. https://doi.org/10.19107/ijisc.2025.01.05.

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The rapid evolution of cyber threats has made cybersecurity a critical priority for both organizations and individuals. As the complexity and frequency of these threats escalate, traditional defense mechanisms often fall short. Artificial Intelligence (AI) has emerged as a transformative force in the cybersecurity domain, offering innovative solutions to enhance threat detection, prevention, and response. This article explores the integration of AI in cybersecurity, beginning with an overview of its fundamental principles and applications. Key AI-driven solutions, such as machine learning algo

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Airlangga, Gregorius. "Adaptive Cyber-Defense for Unmanned Aerial Vehicles: A Modular Simulation Model with Dynamic Performance Management." Buletin Ilmiah Sarjana Teknik Elektro 5, no. 4 (2023): 505–14. https://doi.org/10.12928/biste.v5i4.9415.

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In light of escalating cyber threats, this study tackles the cybersecurity challenges in UAV systems, underscoring the limitations of static defense mechanisms. Traditional security approaches fall short against the sophisticated and evolving nature of cyber-attacks, particularly for UAVs that depend on real-time autonomy. Addressing this deficiency, we introduce an adaptive modular security system tailored for UAVs, enhancing resilience through real-time defensive adaptability. This system integrates scalable, modular components and employs machine learning techniques—specifically, neural net

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Abdullateef Barakat. "AI-driven threat intelligence: Strengthening cyber defense mechanisms in international cybersecurity frameworks." International Journal of Science and Research Archive 14, no. 3 (2025): 598–615. https://doi.org/10.30574/ijsra.2025.14.3.0722.

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The rapid evolution of cyber threats in an increasingly interconnected world requires advanced solutions beyond traditional cyber security measures. The intelligence of threats given by Artificial Intelligence (AI) emerged as a transformative tool, improving the mechanisms of threat detection, prevention, and response. This study explores the role of AI in strengthening cyber defense in international cyber security structures. In order to analyze AI applications such as machine learning, Deep Education and behavioral analysis, research evaluates its effectiveness in reducing civilized cyber th

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Nobles, Calvin, and Ian Mcandrew. "The Intersectionality of Offensive Cybersecurity and Human Factors: A Position Paper." Scientific Bulletin 28, no. 2 (2023): 215–33. http://dx.doi.org/10.2478/bsaft-2023-0022.

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Abstract This position paper delves into the complex interplay between proactive cyber defense mechanisms and inherent human susceptibilities. The researchers posit that as cyber systems advance in technical robustness, malicious entities progressively exploit human psychological tendencies and behavioral inconsistencies to circumvent security measures. These exploitations are not merely opportunistic but are strategic, leveraging a profound comprehension of human cognitive biases to undermine security infrastructures. The researchers argue for broadening the intellectual horizon in this domai

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Talekar, P. R. "Cyber-Attacks on Blockchain Systems: Vulnerabilities, Implications, and Defense Mechanisms." International Journal of Advance and Applied Research 5, no. 23 (2024): 61–64. https://doi.org/10.5281/zenodo.13604313.

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Abstract: Block chain technology, introduced by Satoshi Nakamoto in 2008, has transformed various industries by offering a decentralized, immutable ledger for secure transactions. Despite its benefits, blockchain systems face significant cyber threats that compromise their integrity and functionality. This paper examines modern cyber-attacks on blockchain, focusing on vulnerabilities in consensus mechanisms like Proof-of-Work and Proof-of-Stake. We explore the impact of 51% attacks, Distributed Denial of Service (DDoS) attacks, smart contract vulnerabilities, and Sybil attacks. By analyzi

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Kommisetty, Phani Durga Nanda Kishore, Bala Maruthi Subba Rao Kuppala, and Hussain Vali Buvvaji. "Transforming Cyber Defense: Anomaly Detection and Predictive Analytics for Automated Threat Response." International Journal of Engineering and Computer Science 11, no. 08 (2022): 25585–600. http://dx.doi.org/10.18535/ijecs/v11i08.4694.

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Currently, cyber defense remains a pre-eminently human-driven endeavor, lacking fundamental capabilities for comprehensive and timely detection, response, and prediction. Here, we present transformative concepts to mature cyber defense toward automated anomaly detection, prediction, and response. Our concepts treat the underlying problem at its most basic and essential level: violation of the predictability of correct actions and correct system and service performance, representing unintended relationships and change. We mathematically generalize prediction to explore relationships between dep

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Pell Reddy, Rajender. "Cyber Warfare: National Security Implications and Strategic Defense Mechanisms." International Journal of Computer Trends and Technology 73, no. 4 (2025): 48–59. https://doi.org/10.14445/22312803/ijctt-v73i4p107.

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Ogugua Chimezie Obi, Onyinyechi Vivian Akagha, Samuel Onimisi Dawodu, Anthony Chigozie Anyanwu `, Shedrack Onwusinkwue, and Islam Ahmad Ibrahim Ahmad `. "COMPREHENSIVE REVIEW ON CYBERSECURITY: MODERN THREATS AND ADVANCED DEFENSE STRATEGIES." Computer Science & IT Research Journal 5, no. 2 (2024): 293–310. http://dx.doi.org/10.51594/csitrj.v5i2.758.

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In the rapidly evolving landscape of cyberspace, the prevalence of sophisticated cyber threats has escalated, posing formidable challenges to individuals, organizations, and nations. This comprehensive review explores the contemporary panorama of cybersecurity, focusing on the latest threats and the advanced defense strategies employed to mitigate them. The analysis encompasses a wide spectrum of cyber threats, including malware, ransomware, phishing attacks, and advanced persistent threats (APTs), shedding light on their evolving tactics, techniques, and procedures. The review delves into the

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Raghu, Ram Chowdary Velevela. "AI-Driven Cyber Security: Enhancing Threat Detection And Defense Mechanisms." Recent Trends in Cyber Criminology Research 1, no. 1 (2025): 10–14. https://doi.org/10.5281/zenodo.15009777.

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<em>As digital infrastructures continue to evolve at an unprecedented pace, the landscape of cyber threats has become increasingly complex and sophisticated. Traditional cybersecurity methodologies, while foundational, are struggling to keep up with the ever-growing volume and complexity of cyberattacks, necessitating the adoption of innovative and adaptive technologies. Artificial Intelligence (AI) has emerged as a transformative force in cybersecurity, offering advanced capabilities that go beyond conventional rule-based security systems. AI-driven cybersecurity solutions leverage machine le

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E. Manev, Petar. "Modern Scientific Proactive Cyber Counterintelligence Strategies for Advanced Persistent Threats Early Warning." Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika 32, no. 3 (2023): 286–97. http://dx.doi.org/10.53656/str2024-3-3-mod.

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The article presents a modern scientific proactive cyber counterintelligence concepts and strategies for applying advanced persistent threats early warning approach. The research reviles some of the main challenges and problems that the cyber security domain faces with respect to evolving and advanced cyber threat actors. The research exposes the main gaps in detection mechanisms of the cyber defense industry and landscape and based on that presents some relevant concepts and strategies. This includes assumptions, challenges, concepts and strategies. Based on actual experience from different c

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Rutvij, Shah, Puthraya Karthik, and Paul Josson. "AI-Driven Threat Intelligence Systems: Predictive Cybersecurity Models for Adaptive IT Defense Mechanisms." Global Journal of Engineering and Technology [GJET] 4, no. 2 (2025): 13–15. https://doi.org/10.5281/zenodo.14964173.

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<em>In the rapidly evolving digital landscape, cyber threats have become increasingly sophisticated, necessitating advanced threat intelligence systems. Artificial Intelligence (AI) has emerged as a pivotal technology in cybersecurity, enabling predictive models that enhance adaptive IT defense mechanisms. This paper explores AI-driven threat intelligence systems, detailing their architecture, methodologies, and applications in mitigating cyber threats. We discuss machine learning (ML) and deep learning (DL) models in predictive cybersecurity, real-time threat detection, and automated response

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Tang, Yanni. "Exploring methods to enhance network security through artificial intelligence." Applied and Computational Engineering 36, no. 1 (2024): 140–49. http://dx.doi.org/10.54254/2755-2721/36/20230436.

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In the dynamic landscape of cybersecurity, traditional rule-based systems find themselves frequently outstripped by the intricacy, variety, and mutable nature of cyber threats. This paper explores the capabilities of Machine Learning (ML) in detecting cyber-attacks, offering a fresh perspective to fortify cyber defense mechanisms. Through its unparalleled strengths in data analysis, pattern discernment, and outcome prediction, Machine Learning emerges as a promising ally in grappling with the multifaceted challenges posed by cyber adversaries. The exploration zeroes in on the potential of util

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Goni, Ashraful, Md Umor Faruk Jahangir, and Rajarshi Roy Chowdhury. "A Study on Cyber security: Analyzing Current Threats, Navigating Complexities, and Implementing Prevention Strategies." International Journal of Research and Scientific Innovation X, no. XII (2024): 507–22. http://dx.doi.org/10.51244/ijrsi.2023.1012039.

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In the intricate landscape of modern cyber threats, encompassing everything from sophisticated malware to targeted phishing tactics, this scholarly discussion emerges as a guiding light, illuminating the paramount significance of vulnerability analysis in contemporary cybersecurity. It meticulously details the systematic process—encompassing identification, scanning, penetration testing, risk assessment, prioritization, remediation, and continuous monitoring—that forms the core of proactive cybersecurity measures. Highlighting the symbiotic relationship between vulnerability analysis and incid

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Singh, Mrs Priyanka. "Encrypted Chat Application." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35388.

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In today's cyber landscape, the protection of digital communication channels stands as a paramount concern. This abstract dissects the cyber resilience strategies underpinning the development of secure chat applications, with a particular focus on user interface design using Jetpack Compose and encryption integration leveraging Kotlin. The examination commences with an in-depth analysis of the cyber defence mechanisms ingrained within chat interfaces crafted through Jetpack Compose, the cutting-edge toolkit tailored for native Android UI construction. By meticulously adhering to Material Desig

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Sun, Rongbo, Yuefei Zhu, Jinlong Fei, and Xingyu Chen. "A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive." Applied Sciences 13, no. 9 (2023): 5367. http://dx.doi.org/10.3390/app13095367.

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Represented by reactive security defense mechanisms, cyber defense possesses a static, reactive, and deterministic nature, with overwhelmingly high costs to defend against ever-changing attackers. To change this situation, researchers have proposed moving target defense (MTD), which introduces the concept of an attack surface to define cyber defense in a brand-new manner, aiming to provide a dynamic, continuous, and proactive defense mechanism. With the increasing use of machine learning in networking, researchers have discovered that MTD techniques based on machine learning can provide omni-b

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Zarreh, Alireza, Mobin Zarreh, HungDa Wan, and Can Saygi. "Optimizing cybersecurity in cyber-physical manufacturing systems: A game-theoretic approach and quantal response equilibrium study." Journal of Future Sustainability 5, no. 3 (2025): 179–94. http://dx.doi.org/10.5267/j.jfs.2025.9.002.

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In the era of Industry 4.0, advanced manufacturing systems are increasingly integrating cyber and physical components, making them susceptible to sophisticated cyber-attacks. Addressing these vulnerabilities is crucial for maintaining the integrity and efficiency of manufacturing processes. This study introduces a comprehensive game-theoretic model to tackle cybersecurity challenges in such systems. The interaction between cyber attackers and defenders is modeled as a strategic game, incorporating a cost function that includes production losses, recovery from attacks, and maintaining of defens

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Dr. Rajitha Kotoju and Md. Abrar Khan. "Cognitive Cyber Threat Intelligence: AI-Driven Behavioural Profiling for Proactive Security." international journal of engineering technology and management sciences 9, Special Issue 1 (2025): 156–59. https://doi.org/10.46647/10.46647/ijetms.2025.v09si01.025.

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The rise of sophisticated cyber threats necessitates a shift from reactive security measures toproactive cyber defense. Cognitive Cyber Threat Intelligence (CCTI) leverages AI-drivenbehavioural profiling to predict and mitigate cyber-attacks before they occur. By analyzing attackerpatterns, threat intelligence data, and real-time system anomalies, CCTI enhances situationalawareness and automates threat detection. This paper explores the integration of machine learning,behavioural analytics, and cognitive computing to develop a dynamic cybersecurity frameworkcapable of adaptive threat intellige

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Olanrewaju Oluwaseun Ajayi, Abiodun Sunday Adebayo, and Naomi Chukwurah. "Addressing security vulnerabilities in autonomous vehicles through resilient frameworks and robust cyber defense systems." Gulf Journal of Advance Business Research 3, no. 3 (2025): 825–50. https://doi.org/10.51594/gjabr.v3i3.114.

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The rapid advancement of autonomous vehicle (AV) technology has introduced significant security vulnerabilities that pose risks to passenger safety, system integrity, and public trust. This paper investigates the critical security challenges facing AVs, including sensor spoofing, GPS jamming, adversarial machine learning attacks, and communication network breaches. Through an extensive review of recent cyber threats, the study highlights the increasing sophistication of cyber-attacks targeting AV ecosystems and the potential consequences of security breaches. To address these vulnerabilities,

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Sung, Kyung-Sang, and Wonshik Na. "Countermeasures Against Military Cyber Threats in Accordance with Internet of Things-Based Defense Intelligence." Journal of Computational and Theoretical Nanoscience 18, no. 5 (2021): 1572–76. http://dx.doi.org/10.1166/jctn.2021.9584.

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The new world paradigm as brought on by the Fourth Industrial Revolution has initiated a change in a nation’s defense system, with cybersecurity now being recognized as a crucial to national security. As the scope of national security expands to include cyberspace, it is now necessary to protect both a nation’s physical and cyber space in order to secure national security and the people’s safety. As such, countries all over the world are seeking to strengthen their defense capacity and prepare reliable cybersecurity strategies by linking the private, public, and defense sectors at the national

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Rosario, Ariel, Timothy Shives, and Mustafa Canan. "Expanding the Cyber Mission Space with the Expansion of Security Cooperation in the Era of Great Power Competition." International Conference on Cyber Warfare and Security 20, no. 1 (2025): 427–36. https://doi.org/10.34190/iccws.20.1.3388.

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As great power competition intensifies, cybersecurity has emerged as both a battleground and an opportunity for cooperation. Malign actors exploit cyber infrastructure to undermine international order while simultaneously presenting themselves as contributors to economic growth. This paper proposes a novel framework for managing cybersecurity challenges by establishing regional Cyber Centers of Excellence (CCoEs), aligned with existing internet governance structures. The research outlines three key contributions: (1) mapping cyberspace governance to align cyber defense responsibilities with ex

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Wan, Ming, Jiawei Li, Ying Liu, Jianming Zhao, and Jiushuang Wang. "Characteristic insights on industrial cyber security and popular defense mechanisms." China Communications 18, no. 1 (2021): 130–50. http://dx.doi.org/10.23919/jcc.2021.01.012.

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Pramod, J. P. "Ethical Intrusion: The Strategic Role of Ethical Hacking in the Modern Cybersecurity Framework." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47045.

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ABSTRACT As cyber threats are increasing in size, complexity, and frequency, the obligation organizations have to safeguard sensitive information and maintain digital trust is significant. This research considers the changing role of ethical hacking—permitted imitation of cyber intrusions—as a first-line-of-defense tool in current-day cybersecurity. It further addresses the increasing concern around system vulnerabilities and data breaches by examining how ethical hacking methodologies can help to proactively identify vulnerabilities before the malicious agent exploits them. We seek to conside

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KAZMIRUK, S., and B. LEONOV. "Legal and organizational provision of cyber protection of lie detection systems against cyber attacks under the conditions of the martial law." INFORMATION AND LAW, no. 3(46) (September 5, 2023): 135–41. http://dx.doi.org/10.37750/2616-6798.2023.3(46).287217.

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The article is devoted to exploring problematic issues regarding the cyber protection of lie detection systems. Aspects of the legal regulation of the use of a polygraph (lie detector) in the security and defense sector of Ukraine are highlighted. The article contains an innovative approach to the cyber protection of computer systems, the detection of hidden and unreliable information, and the improvement of the effective use of the lie detector. A conclusion was drawn on the practicality of developing new and improving existing tools and mechanisms of legal and organizational support for the

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Shelke, Palvi, and Timo Hamalainen. "Analysing Multidimensional Strategies for Cyber Threat Detection in Security Monitoring." European Conference on Cyber Warfare and Security 23, no. 1 (2024): 780–87. http://dx.doi.org/10.34190/eccws.23.1.2123.

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The escalating risk of cyber threats requires continuous advances in security monitoring techniques. This survey paper provides a comprehensive overview of recent research into novel methods for cyber threat detection, encompassing diverse approaches such as machine learning, artificial intelligence, behavioral analysis and anomaly detection. Machine learning plays a central role in cyber threat detection, highlighting the effectiveness of deep neural networks in identifying evolving threats. Their adaptability to changing attack patterns is emphasized, underlining their importance for real-ti

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Rushika, Umesh Dhobale, and Tulshiram Gavali Shweta. "Blockchain for Secure Defense Communication Networks." Recent Trends in Control and Converter 8, no. 2 (2025): 6–21. https://doi.org/10.5281/zenodo.15348865.

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<em>In modern defense communication networks, ensuring security, integrity, and resilience against cyber threats is of paramount importance. Traditional centralized network architectures are prone to cyberattacks, data breaches, and unauthorized access, posing significant risks to national security. Blockchain technology, with its decentralized, immutable, and cryptographically secure nature, presents a revolutionary solution for safeguarding military communication infrastructures. By leveraging distributed ledger mechanisms, blockchain enhances data integrity, prevents unauthorized modificati

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Dwarampudi, Aiswarya, and Manas Kumar Yogi. "A Robust Machine Learning Model for Cyber Incident Classification and Prioritization." Journal of Trends in Computer Science and Smart Technology 6, no. 1 (2024): 51–66. http://dx.doi.org/10.36548/jtcsst.2024.1.004.

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Cyber incident classification and prioritization are crucial tasks in cybersecurity, enabling rapid response and resource allocation to mitigate potential threats effectively. This study presents a robust machine learning model designed for accurate classification and prioritization of cyber incidents, aiming to enhance cyber defense mechanisms. The proposed model integrates diverse machine learning algorithms, including Random Forest, Support Vector Machines, and Gradient Boosting, leveraging their complementary strengths to improve predictive performance and robustness. Extensive experimenta

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Ammar, Alzaydi, Abedalrhman Kahtan, Nurhaliza Siti, and Ismail Mohd. "Enhancing Cyber Defense Mechanisms for Genomic Data in Personalized Healthcare Systems." Applied Science and Biotechnology Journal for Advanced Research 3, no. 5 (2024): 20–30. https://doi.org/10.5281/zenodo.13852606.

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In the era of personalized medicine, genomic data emerges as a cornerstone for tailored healthcare solutions, offering unprecedented opportunities for disease prediction and prevention. However, this sensitive data is increasingly vulnerable to cyber threats that compromise patient privacy and system integrity. Addressing this critical issue, our research introduces a novel cybersecurity framework specifically designed to protect genomic information within healthcare systems. We develop and implement advanced cryptographic methods, real-time intrusion detection systems, and secure data sharing

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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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Abdullahi, Sani M., and Sanja Lazarova-Molnar. "On the adoption and deployment of secure and privacy-preserving IIoT in smart manufacturing: a comprehensive guide with recent advances." International Journal of Information Security 24 (January 3, 2025): 53. https://doi.org/10.1007/s10207-024-00951-8.

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The adoption of the Internet of Things (IoT) in smart manufacturing has recently seen a boost in economic and technological advancement. This is attributed to improved operational efficiency resulting from streamlined interoperability, decreased downtime, and real-time processing as compared to conventional manufacturing industries. It also brings with it a massive deployment of intelligent systems and devices from both the Information Technology (IT) and Operational Technology (OT) realms within the Industrial IoT (IIoT) infrastructure, thus creating a heterogeneous interconnectivity. However

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Ugochukwu, Ikechukwu Okoli, Chimezie Obi Ogugua, Okechukwu Adewusi Adebunmi, and Abrahams Oluwaseun. "Machine learning in cybersecurity: A review of threat detection and defense mechanisms." World Journal of Advanced Research and Reviews 21, no. 1 (2024): 2286–95. https://doi.org/10.5281/zenodo.13367448.

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The cybersecurity concerns get increasingly intricate as the digital world progresses. In light of the increasing complexity of cyber threats, it is imperative to develop and implement advanced and flexible security strategies. Machine Learning (ML) has become a potent tool in strengthening cybersecurity, providing the capacity to scrutinise extensive information, recognise trends, and improve threat detection and defence methods. This paper examines the significance of ML in the field of cybersecurity, with a special emphasis on the identification of threats and the implementation of protecti

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Ugochukwu Ikechukwu Okoli, Ogugua Chimezie Obi, Adebunmi Okechukwu Adewusi, and Temitayo Oluwaseun Abrahams. "Machine learning in cybersecurity: A review of threat detection and defense mechanisms." World Journal of Advanced Research and Reviews 21, no. 1 (2024): 2286–95. http://dx.doi.org/10.30574/wjarr.2024.21.1.0315.

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The cybersecurity concerns get increasingly intricate as the digital world progresses. In light of the increasing complexity of cyber threats, it is imperative to develop and implement advanced and flexible security strategies. Machine Learning (ML) has become a potent tool in strengthening cybersecurity, providing the capacity to scrutinise extensive information, recognise trends, and improve threat detection and defence methods. This paper examines the significance of ML in the field of cybersecurity, with a special emphasis on the identification of threats and the implementation of protecti

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Gulhane, Vaishnavi, and Dr R. S. Bansode. "Enhancing Cyber Defense: A Comprehensive Study of DNS Security." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–17. http://dx.doi.org/10.55041/ijsrem36765.

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As the internet continues to evolve, robust security measures are becoming increasingly vital at every stage. One crucial component, the Domain Name System (DNS), plays a pivotal role in helping users access websites. However, its lack of inherent security mechanisms makes it vulnerable to exploitation. Unsecured DNS can be manipulated, leading to threats like DNS tunneling, hijacking, and cache poisoning. To address these vulnerabilities, DNSSEC (Domain Name System Security Extension) offers a critical layer of security for a safer DNS system. Keywords: DNS, DNSSEC, Tunneling, Cache poisoning

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Almelehy, Bashaer, Adnan Anwar, Ghalia Nassreddine, and Mohammed Maayah. "Web Application Security: An Analytical Study of Cyber Threats and Defense Mechanisms." Journal of Cyber Security and Risk Auditing 2025, no. 3 (2025): 43–62. https://doi.org/10.63180/jcsra.thestap.2025.3.5.

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The use of internet technologies offers numerous advantages and has significantly transformed our daily lives, becoming a primary means of communication. Additionally, many businesses have shifted their services to digital platforms by leveraging web application technologies. As a result, vast amounts of data are exchanged between users and web applications—much of which contains sensitive and critical information. This makes them prime targets for cyber-attacks, including data theft and the unauthorized disclosure of confidential information. According to the Open Web Application Security Pro

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Lypka, Yurii. "Paradigm shifts in cybersecurity: a convergence of artificial intelligence, quantum computing, and neuromorphic systems." Modeling, Control and Information Technologies, no. 7 (December 7, 2024): 190–91. https://doi.org/10.31713/mcit.2024.055.

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The convergence of artificial intelligence (AI), quantum computing, and neuromorphic systems represents a significant paradigm shift in the field of cybersecurity. This transformation is driven by the increasing complexity of cyber threats and the need for more sophisticated defense mechanisms. As cyber threats evolve, traditional security measures are often inadequate, necessitating the integration of advanced technologies such as AI and quantum computing to enhance cybersecurity frameworks.

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Darshini, S., A. Thrisha, M. Navya Sree, G. Varshitha, and G. Thippanna. "Dark Sides of Cyber Attacks: Protection from the Assorted Cyber Attacks." Journal of Cyber Security, Privacy Issues and Challenges 4, no. 1 (2025): 14–21. https://doi.org/10.46610/jcspic.2025.v04i01.002.

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Cyber attacks have increased due to the quick development of digital communications, revealing a dark side where bad actors take advantage of system flaws to do serious damage. These assaults, which target people, businesses, and even governments, can take many different forms, such as ransomware, phishing, data breaches, and Advanced Persistent Threats (APTs). These attacks are becoming more complicated and frequent, which puts sensitive data, financial assets, and national security in danger. This paper investigates the diverse nature of cyber attacks, highlighting the motivations, methods,

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Mohammed, Ghazi Ghader, and Zeedon Zaheer. "NeuroCyberGuard: Developing a Robust Cybersecurity Defense System through Deep Neural Learning-Based Mathematical Modeling." Journal of Smart Internet of Things 2022, no. 1 (2022): 133–45. http://dx.doi.org/10.2478/jsiot-2022-0009.

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Abstract Due to the increasing sophistication and frequency of cyber threats, cyber security has become a pressing issue in today’s interconnected society. The traditional security methods have proven insufficient to tackle the evolving threat landscape, leading to the need for more advanced and effective defence mechanisms. The emerging field of deep neural learning has shown promise in cybersecurity, as it can learn complex patterns and behaviors in data and provide more accurate and efficient detection and prediction capabilities. This research aims to develop a robust cybersecurity defense

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Gamilla, Anazel P., Thelma D. Palaoag, and Marlon A. Naagas. "Probing the depths: assessing the efficacy of the two-tier deception-driven security model." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 3 (2024): 1631. http://dx.doi.org/10.11591/ijeecs.v36.i3.pp1631-1639.

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In the age characterized by relentless cyber threats, the need for innovative and proactive security measures has never been more important. Deception is defined as the deliberate structure of tricks, traps, and false information to mislead and discourage threats, while providing timely warning signals and useful information to defenders. The two-tier deception-driven security model's implementation focuses on applying deception security techniques to deceive potential attackers and protect network resources, with an emphasis on a proactive defense approach. The study emphasized the deployment

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Anazel, P. Gamilla Thelma D. Palaoag Marlon A. Naagas. "Probing the depths: assessing the efficacy of the two-tier deception-driven security model." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 3 (2024): 1631–39. https://doi.org/10.11591/ijeecs.v36.i3.pp1631-1639.

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Abstract:

In the age characterized by relentless cyber threats, the need for innovative and proactive security measures has never been more important. Deception is defined as the deliberate structure of tricks, traps, and false information to mislead and discourage threats, while providing timely warning signals and useful information to defenders. The two-tier deception-driven security model's implementation focuses on applying deception security techniques to deceive potential attackers and protect network resources, with an emphasis on a proactive defense approach. The study emphasized the deployment

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41

Azmi, Nor Nashrah, Fiza Abdul Rahim, Noor Hafizah Hassan, and Nur Azfahani Ahmad. "SWOT ANALYSIS OF CYBERSECURITY VULNERABILITIES AND STRATEGIES IN RENEWABLE ENERGY SYSTEM: A CASE STUDY ON EUROPEAN WIND POWER SYSTEM." Malaysian Journal of Sustainable Environment 12, no. 2 (2025): 103–26. https://doi.org/10.24191/myse.v12i2.7072.

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As smart cities increasingly incorporate renewable energy systems to achieve sustainability objectives, ensuring strong cyber security measures is crucial. The susceptibility of these infrastructures, such as smart grids and solar panels, to diverse cyber threats poses significant risks to energy supply and data integrity. This paper examines cyber resilience in the context of the renewable energy industry, using a SWOT analysis of a European wind energy case study to assess the sector's cybersecurity posture and identify key vulnerabilities. This paper aims to identify the industry's current

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Dr., Arjun Singh Chauhan. "MODERN CYBER REGULATIONS AND TECH CRIMES IN THE 21ST CENTURY." Indian Journal of Law and Society I, no. 8 (2024): 39–53. https://doi.org/10.5281/zenodo.10644551.

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<strong>ABSTRACT</strong> <em>In an era dominated by digital inter connectedness, the significance of robust cyber security measures cannot be overstated. This abstract provides an overview of a comprehensive study aimed at advancing cyber security practices through the integration of cutting-edge technologies and proactive threat intelligence strategies. The research delves into the current landscape of cyber security, highlighting the escalating sophistication of cyber threats and the evolving tactics employed by malicious actors. Recognizing the imperative need for adaptive defense mechanis

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Hasan, Kamrul, Forhad Hossain, Al Amin, Yadab Sutradhar, Israt Jahan Jeny, and Shakik Mahmud. "Enhancing Proactive Cyber Defense: A Theoretical Framework for AI-Driven Predictive Cyber Threat Intelligence." Journal of Technologies Information and Communication 5, no. 1 (2025): 33122. https://doi.org/10.55267/rtic/16176.

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The rapid evolution of cyber threats and the dynamic nature of the threat landscape have necessitated the development of proactive and predictive defense mechanisms. This research proposes an AI-driven framework for predictive cyber threat intelligence aimed at enhancing organizational cybersecurity by identifying and mitigating threats before they materialize. The framework integrates diverse data sources, including network logs, endpoint data, and threat intelligence feeds, to generate actionable insights using advanced machine learning algorithms such as anomaly detection, pattern recogniti

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Kachan, Vadym. "Study of the cyber defense methods’ effectiveness against the TCP SYN Flood attack of the Transport Layer." Problemi telekomunìkacìj, no. 2(31) (February 16, 2023): 51–68. http://dx.doi.org/10.30837/pt.2022.2.04.

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The work analyzes threats at seven layers of the Open Systems Interconnection reference model. Special attention is paid to the critical Transport Layer, one of the most desirable layers for an adversary to attack, and cyber defense techniques at this layer. The importance of protection against «Denial of Service» attacks at the Transport Layer has been analyzed and substantiated. The importance of implementing cyber defense against the Synchronize (SYN) Flood attack has been established. This attack was executed with the set parameters, and the consequences of its impact on the system were ev

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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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Babajide Tolulope Familoni. "CYBERSECURITY CHALLENGES IN THE AGE OF AI: THEORETICAL APPROACHES AND PRACTICAL SOLUTIONS." Computer Science & IT Research Journal 5, no. 3 (2024): 703–24. http://dx.doi.org/10.51594/csitrj.v5i3.930.

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In the ever-evolving landscape of cybersecurity, the proliferation of artificial intelligence (AI) technologies introduces both promising advancements and daunting challenges. This paper explores the theoretical underpinnings and practical implications of addressing cybersecurity challenges in the age of AI. With the integration of AI into various facets of digital infrastructure, including threat detection, authentication, and response mechanisms, cyber threats have become increasingly sophisticated and difficult to mitigate. Theoretical approaches delve into understanding the intricate inter

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Wang, Zhen, Yongjie Wang, Xinli Xiong, Qiankun Ren, and Jun Huang. "A Novel Framework for Enhancing Decision-Making in Autonomous Cyber Defense Through Graph Embedding." Entropy 27, no. 6 (2025): 622. https://doi.org/10.3390/e27060622.

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Faced with challenges posed by sophisticated cyber attacks and dynamic characteristics of cyberspace, the autonomous cyber defense (ACD) technology has shown its effectiveness. However, traditional decision-making methods for ACD are unable to effectively characterize the network topology and internode dependencies, which makes it difficult for defenders to identify key nodes and critical attack paths. Therefore, this paper proposes an enhanced decision-making method combining graph embedding with reinforcement learning algorithms. By constructing a game model for cyber confrontations, this pa

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Dr., Amit Sharma. "Advanced Cyber Defense: Machine Learning Techniques with TensorFlow." Career Point International Journal of Research (CPIJR) 1, no. 3 (2024): 123–30. https://doi.org/10.5281/zenodo.13821739.

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The rise of cyber threats has necessitated the development of advanced defense mechanisms to protect digital infrastructures. Traditional cybersecurity techniques, while effective in many scenarios, are often inadequate to address the growing complexity of modern attacks. Machine learning (ML) has emerged as a promising solution for enhancing cybersecurity, allowing for more accurate threat detection, prediction, and response. TensorFlow, a widely-used open-source ML framework, offers a robust platform for deploying sophisticated ML models aimed at mitigating cyber threats. This paper investig

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Li, Tony Yuan. "Asymmetry in the Digital Age: Cyber Deterrence Strategies for Small States." Journal of Strategic Security 17, no. 4 (2024): 71–88. https://doi.org/10.5038/1944-0472.17.4.2268.

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In an era where cyberwarfare poses an invisible but potent threat to national security, traditional deterrence theories fall short in providing nations with a viable strategy to defend themselves. This is especially the case for small states, where limited national resources and cyber capabilities prevent them from effectively deterring larger powers. This paper investigates whether small states can achieve cyber deterrence, and the means to do so. This research challenges the preconception that small states are at a disadvantage in cyber contestation, and instead proposes that their small siz

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Choudhary, Rahul, Rajkumar Choudhary, and Karuna Soni. "MACHINE LEARNING ALGORITHMS FOR CYBER ATTACKS AND FRAUD DETECTION." International Journal of Technical Research & Science 9, no. 05 (2024): 17–20. http://dx.doi.org/10.30780/ijtrs.v09.i05.004.

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Cyber-attacks and fraud pose significant risks to individuals, organizations, and nations. The consequences of such malicious activities range from financial losses and reputational damage to national security threats. As cyber attackers continuously evolve their techniques, traditional defense mechanisms often fall short in providing adequate protection. Consequently, there is a pressing need for advanced, adaptive, and efficient solutions to detect and mitigate these threats. The rise of cyber-attacks and fraud has become a significant concern for both individuals and organizations. As a res

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