International Journal For Multidisciplinary Research

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Enhancement of Cybersecurity in Information Technology Using AI-Powered Working Intrusion Detection Working Systems

Author(s) Dr. Shiva Reddy Kashireddy
Country India
Abstract This research addresses the inadequacy of traditional cybersecurity mechanisms in combating the growing sophistication of modern cyber threats. The purpose of this study is to develop an artificial intelligence (AI)-powered intrusion detection system (IDS) that enhances threat detection capabilities, minimizes false positives, and reduces computational overhead. The increasing reliance on interconnected networks has amplified vulnerabilities, while existing IDS
solutions struggle with high false-positive rates, inability to detect zero-day attacks, and inefficiencies in processing large-scale data. A comprehensive review of the literature highlights these limitations, establishing the need for innovative approaches that integrate advanced AI techniques. The research proposes a hybrid framework that combines supervised machine learning for detecting known attack patterns and unsupervised learning for anomaly detection, enabling the system to adapt to rapidly evolving threats in real time.
The study’s findings underscore the transformative potential of AI in cybersecurity. The system provides a proactive and adaptive approach to threat detection, moving beyond reactive methods to offer a more secure digital environment. The research also explores broader implications, proposing future enhancements such as integrating blockchain technology to ensure data integrity and privacy, as well as incorporating explainable AI (XAI) to improve
transparency. These advancements lay the groundwork for fully autonomous cybersecurity systems capable of dynamic self-learning and real-time threat mitigation, bridging the gap between theoretical innovation and practical application. This work establishes a foundation for organizations across industries to enhance their defenses against evolving cyber threats.
Keywords This research addresses the inadequacy of traditional cybersecurity mechanisms in combating the growing sophistication of modern cyber threats. The purpose of this study is to develop an artificial intelligence (AI)-powered intrusion detection system (IDS) that enhances threat detection capabilities, minimizes false positives, and reduces computational overhead. The increasing reliance on interconnected networks has amplified vulnerabilities, while existing IDS solutions struggle with high false-positive rates, inability to detect zero-day attacks, and inefficiencies in processing large-scale data. A comprehensive review of the literature highlights these limitations, establishing the need for innovative approaches that integrate advanced AI techniques. The research proposes a hybrid framework that combines supervised machine learning for detecting known attack patterns and unsupervised learning for anomaly detection, enabling the system to adapt to rapidly evolving threats in real time. The study’s findings underscore the transformative potential of AI in cybersecurity. The system provides a proactive and adaptive approach to threat detection, moving beyond reactive methods to offer a more secure digital environment. The research also explores broader implications, proposing future enhancements such as integrating blockchain technology to ensure data integrity and privacy, as well as incorporating explainable AI (XAI) to improve transparency. These advancements lay the groundwork for fully autonomous cybersecurity systems capable of dynamic self-learning and real-time threat mitigation, bridging the gap between theoretical innovation and practical application. This work establishes a foundation for organizations across industries to enhance their defenses against evolving cyber threats.
Field Engineering
Published In Volume 8, Issue 1, January-February 2026
Published On 2026-01-07
DOI https://doi.org/10.36948/ijfmr.2026.v08i01.63042
Short DOI https://doi.org/hbh55m
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