AI and Deep Learning in Cybersecurity: Efficacy, Challenges, and Future Prospects
Keywords:
Cybersecurity, Artificial Intelligence, Deep Learning, Threat Detection, Adversarial Attacks, Anomaly Detection, Predictive Analytics, Machine Learning, Network SecurityAbstract
Artificial Intelligence (AI) and Deep Learning (DL) have revolutionized cybersecurity by enhancing threat detection, automating responses, and improving adaptive security mechanisms. With the exponential growth of cyber threats, conventional security measures struggle to cope with sophisticated attacks. AI-driven security solutions, particularly those employing Deep Learning, offer unparalleled advantages in anomaly detection, real-time monitoring, and predictive analytics. However, these advancements also present challenges such as adversarial attacks, data privacy concerns, and computational costs. This paper provides an in-depth analysis of AI and DL in cybersecurity, evaluates their effectiveness through experimental findings, discusses associated challenges, and explores potential future developments in the field. The findings indicate that AI and DL significantly enhance cybersecurity measures, but robust frameworks and defensive mechanisms are necessary to address their limitations.
