• Asha M
• Ramesh K

The increasing interconnectivity of modern digital infrastructures and the proliferation of Internet of Things (IoT) devices have led to an exponential rise in cyber-attacks, posing severe challenges to data integrity, privacy, and network reliability. Conventional signature-based Intrusion Detection Systems (IDS) are often ineffective against emerging and unknown attack patterns. To address this limitation, Artificial Intelligence (AI) and Machine Learning (ML) techniques have emerged as powerful tools for intelligent, automated, and adaptive cybersecurity solutions. This paper investigates the role of AI and ML algorithms in detecting and classifying cyber-attacks using both traditional and deep learning approaches. Various models—including Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—are trained and evaluated on benchmark datasets such as NSL-KDD and CICIDS2017. The comparative analysis demonstrates that hybrid deep learning architectures, specifically CNN-LSTM ensembles, outperform conventional classifiers with detection accuracies exceeding 99%, low false-positive rates, and improved real-time adaptability. The study highlights the transformative potential of AI-driven frameworks in enhancing the resilience, scalability, and intelligence of modern cyber-security systems.

Artificial Intelligence (AI); Machine Learning (ML); Cybersecurity; Intrusion Detection System (IDS); Anomaly Detection; Deep Learning; Internet of Things (IoT); Network Security; CNN-LSTM; Classification

"AI and ML Approaches for Cyber-Attack Detection and Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 2, page no.i337-i342, February 2025 , Available :http://www.jetir.org/papers/JETIR2502835.pdf

"AI and ML Approaches for Cyber-Attack Detection and Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 2, page no. ppi337-i342, February 2025 , Available at : http://www.jetir.org/papers/JETIR2502835.pdf