Jun 2026· Journal of Computational Science and Technology· Vol 3, pp. 22-44· 0 citations
TL;DR
The findings indicate that AI-driven cybersecurity systems improve real-time threat detection, anomaly identification, automated monitoring, and predictive security analysis and identify critical challenges related to ethical governance, privacy protection, computational complexity, and adversarial attacks in AI-based cybersecurity systems.
Abstract
This study explores the role of artificial intelligence in strengthening cybersecurity threat detection frameworks for next-generation network environments. The rapid expansion of cloud computing, Internet of Things ecosystems, and distributed digital infrastructures has significantly increased cybersecurity risks and operational vulnerabilities. Traditional cybersecurity systems often struggle to detect sophisticated and evolving threats due to their dependence on static detection mechanisms. Using a qualitative research approach and content analysis method, this study examines recent developments in artificial intelligence, machine learning algorithms, and intelligent cybersecurity frameworks. The findings indicate that AI-driven cybersecurity systems improve real-time threat detection, anomaly identification, automated monitoring, and predictive security analysis. Machine learning technologies such as Random Forest, Support Vector Machine, and deep learning models demonstrate strong potential for enhancing intrusion detection accuracy and reducing false positive rates. The study also identifies critical challenges related to ethical governance, privacy protection, computational complexity, and adversarial attacks in AI-based cybersecurity systems
This study examines the application of artificial intelligence-powered intrusion detection systems that leverage deep learning architectures and anomaly detection methodologies to identify malicious activities within dynamic network environments and concludes that the convergence of deep learning methodologies and anomaly detection techniques provides a robust foundation for next-generation intrusion detection systems.
M. A. Gandhi, Dinesh Baban Kute, U. Hemavathi· International journal of com...· 0 citations
The findings indicate that AI-powered cyber defense significantly enhances threat detection, reduces response time, and improves overall cyber resilience compared to traditional security models, highlighting its critical role in next-generation cybersecurity infrastructures.
Chinedu Eze· International Journal of App...· 0 citations
The rapid digital transformation of critical infrastructure has significantly increased its exposure to complex and continuously evolving cyber threats, creating an urgent need for intelligent and adaptive cybersecurity solutions. Conventional security mechanisms, such as signature-based and rule-based intrusion detection systems, often struggle to identify novel attack patterns and provide timely responses to emerging threats. To address these limitations, this study proposes an artificial intelligence (AI)-driven framework for cyber threat detection and automated response that strengthens the security, resilience, and operational reliability of critical infrastructure environments. The experimental evaluation demonstrates that AI-based techniques substantially outperform traditional cybersecurity methods in terms of detection performance. Conventional rule-based systems achieve an average detection accuracy of approximately 68%, whereas machine learning and deep learning models improve the accuracy to nearly 80% and 88%, respectively. The proposed AI-driven framework delivers the highest performance, achieving an overall detection accuracy of approximately 94%. This superior performance highlights its capability to accurately identify both previously known attacks and sophisticated zero-day threats. Beyond detection accuracy, the study evaluates response time, which plays a crucial role in limiting the impact of cyber incidents. The findings reveal that the proposed AI-enabled response mechanism reduces the average response time to approximately 35 seconds, compared with around 150 seconds for manual response processes and 90 seconds for conventional rule-based automation. Such improvements enable faster threat containment, minimize operational disruption, and enhance the resilience of critical infrastructure systems. The framework also demonstrates notable improvements in reducing false positive alerts. The AI-driven approach achieves a false positive rate of approximately 5%, significantly lower than the 20% observed in signature-based systems and the 12% reported for anomaly-based detection methods. By minimizing false alarms, the proposed framework improves the efficiency of security operations, reduces alert fatigue among cybersecurity analysts, and enables security teams to prioritize genuine threats more effectively.
Reily Kaium, Lizi Alasa, K. Robert et al.· The Eastasouth Journal of In...· 0 citations
It is concluded that AI has become an indispensable component of modern cybersecurity strategies and will play a critical role in safeguarding digital infrastructure against emerging cyber threats.
Shaurya Gupta· Innovative Research Thoughts· 0 citations
The rapid growth of interconnected digital infrastructures, cloud computing environments, Internet of Things devices, and enterprise networking systems has significantly increased the frequency, complexity, and sophistication of cyberattacks targeting organizational information assets. Traditional cybersecurity mechanisms based primarily on signature detection and static rule-based monitoring are becoming increasingly ineffective against modern attack strategies such as zero-day exploits, advanced persistent threats, insider attacks, ransomware campaigns, and polymorphic malware. In this context, adaptive threat intelligence frameworks integrated with behavior-based analytics have emerged as a promising approach for enhancing real-time cyberattack detection and proactive security response capabilities. This research investigates the design and implementation of an adaptive threat intelligence framework capable of identifying malicious activities through continuous behavioral analysis, anomaly detection, and dynamic threat assessment techniques. The study focuses on how behavioral analytics can improve cybersecurity resilience by monitoring user activities, network communication patterns, system interactions, application behavior, and endpoint activities to identify deviations from established normal operational baselines. Unlike traditional detection approaches that depend heavily on predefined signatures, behavior-based analytics enables the identification of previously unknown threats and evolving attack vectors through machine learning algorithms, predictive analytics, and intelligent pattern recognition models. The proposed framework integrates adaptive learning mechanisms that continuously update threat intelligence repositories based on real-time attack behaviors, thereby improving detection accuracy and minimizing response delays. The research further examines the role of artificial intelligence, big data analytics, and automated incident response systems in strengthening cyber defense infrastructures across enterprise environments. In addition to operational advantages, the study critically evaluates challenges associated with implementing adaptive threat intelligence systems, including false-positive generation, data privacy concerns, computational complexity, adversarial machine learning attacks, scalability limitations, and integration difficulties within heterogeneous network architectures. The research methodology incorporates quantitative analysis, simulated attack scenarios, case study evaluations, and expert assessments to measure the effectiveness of behavior-based threat detection techniques in identifying malicious activities across dynamic cybersecurity environments. Findings from the study indicate that adaptive threat intelligence frameworks significantly enhance threat visibility, accelerate incident response, reduce detection latency, and improve organizational preparedness against sophisticated cyber threats when compared to conventional security monitoring systems. The research also emphasizes the importance of continuous learning models, human oversight, ethical cybersecurity governance, and secure data management practices to ensure sustainable and reliable implementation of intelligent threat detection systems. The study concludes that behavior-based adaptive cybersecurity frameworks represent a critical advancement in modern cyber defense strategies by enabling organizations to detect, analyze, and respond to emerging cyber threats in real time while maintaining operational continuity, information security, and digital infrastructure resilience in increasingly hostile cyber environments.
S. Tamilselvi· Journal of Intelligent Decis...· 0 citations
Artificial intelligence (AI) has emerged as a transformative force in cybersecurity, offering capabilities that extend far beyond the static, rule-based defenses of the past. Machine learning, deep learning, and natural language processing techniques are increasingly embedded in intrusion detection systems, threat intelligence platforms, and automated incident response tools, enabling organizations to identify and neutralize threats with greater speed and precision. However, the same interconnectedness that drives digital transformation—spanning IoT ecosystems, cloud infrastructures, and 5G networks—has also expanded the attack surface available to malicious actors, giving rise to increasingly sophisticated, adaptive, and often AI-enabled threats such as adversarial machine learning attacks, deepfake-driven social engineering, and automated supply chain exploits. This paper examines the dual role of AI as both a defensive asset and a potential vector of risk within modern cybersecurity ecosystems. Drawing on a review of existing AI-driven security solutions, comparative analysis of AI-based versus traditional defense mechanisms, and case study evaluation, the study assesses the effectiveness, limitations, and ethical implications of AI integration in cyber defense. Findings indicate that while AI substantially improves threat detection accuracy and response times, challenges related to explainability, adversarial vulnerability, and regulatory oversight remain significant barriers to widespread adoption. The paper concludes with practical recommendations for organizations and policymakers seeking to harness AI's defensive potential while mitigating its associated risks, emphasizing the need for explainable AI frameworks, human-AI collaboration, and adaptive governance structures in an increasingly interconnected digital age.
Nicolas Guzman Camacho· Journal of Artificial Intell...· 0 citations