AI-powered threat detection systems: Pioneering the future of cybersecurity
Pallavi Nayak, Anchal Anchal, Krishna Dheeravath, Pallati Narsimhulu, J. Somasekar, Raja Praveen K N, Vikram Neerugatti · 2025
As cyber threats growing more sophisticated, conventional security approaches are finding it difficult to match pace. This paper provides an extensive examination of threat detection systems that utilise artificial intelligence (AI), machine learning (ML) and deep learning (DL) to identify, forecast, and address threats as they occur. We assessed the effectiveness of convolutional neural networks (CNN), random forest, and support vector machine (SVM) using datasets such as CICIDS 2017 and UNSW-NB15. The findings indicate that systems based on AI attained a detection accuracy exceeding 95%, and they significantly lowered the incidence of false positives in comparison to conventional approaches. We examine difficulties like data privacy, computational requirements, and false positives, as well as potential future developments such as blockchain integration and quantum-resistant algorithms.