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Aripin Rambe
Universitas Battuta
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Journal : International Journal for Advanced Research

 1 
Enhancing Cybersecurity Resilience with AI-Powered Threat Detection Systems Sattar Rasul; Aripin Rambe; Roy Nuary Singarimbun
International Journal of Advanced Research Vol. 1 No. 3: October 2024
Publisher : Outline Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61730/6fch4r18

Abstract

Cybersecurity is a major concern across sectors given the increasing complexity of digital threats. This study evaluates the application of an AI-powered threat detection system to improve an organization’s cybersecurity resilience. By leveraging technologies such as Machine Learning (ML) and Deep Learning (DL), the system is able to detect new threat patterns and respond in real-time. The study shows that the AI-powered system has an accuracy rate of up to 95% in detecting threats, reducing the average response time from 4 hours to less than 30 minutes, and reducing false positives by 40%. The results also revealed that AI can detect 87% of new, unregistered threats. However, the adoption of this technology faces challenges, such as high implementation costs, reliance on quality data, and the risk of AI-based adversarial attacks. The study recommends mitigation strategies, including adversarial-based training, careful data management, and investment in AI infrastructure. The study concludes that the application of AI provides an adaptive and effective solution to improve cybersecurity resilience despite the challenges that must be overcome.
Co-Authors Baginda Harahap Dewi Wahyuni Dinur Syahputra Dinur Syahputra Eka Hayana Hasibuan Eka Hayana Hasibuan Fithrie Soufitri Juliansyah Putra Tanjung Leni Indrayani Melky Suhery Simamora Muhammad Konginta Lubis Muhathir, Muhathir Roy Nuary Singarimbun Roy Nuary Singarimbun ruziq, fahmi Sattar Rasul