• Vaibhav suresh chorge
• Miss. N.N. Adulkar
• Riya Rohidas Rewale
• Yash Bhagawan Pawar
• Prajakta Rajaram Rathod

Cloud computing provides scalable and cost-effective services, but its dynamic nature introduces significant security risks such as malware, Distributed Denial of Service (DDoS), and unauthorized access. Traditional intrusion detection systems (IDS) are insufficient in detecting advanced threats in cloud environments. Artificial Intelligence (AI), particularly machine learning and deep learning, has emerged as a powerful tool to enhance IDS by enabling adaptive, intelligent, and real-time detection. This paper reviews AI-driven IDS approaches, highlights their characteristics, discusses applications, explains methodologies, and outlines challenges and opportunities for future research. The rapid adoption of cloud computing has transformed the way organizations store, process, and manage data, but it has also introduced significant security challenges. Traditional Intrusion Detection Systems (IDS) often struggle to cope with the dynamic, distributed, and large-scale nature of cloud environments, leaving them vulnerable to sophisticated cyber-attacks. To address these challenges, Artificial Intelligence (AI)-driven IDS have emerged as a promising solution for enhancing cloud security. By leveraging machine learning, deep learning, and data-driven analytics, AI-based IDS can effectively identify anomalies, detect zero-day attacks, and adapt to evolving threat landscapes in real time. This research explores the design, methodologies, and deployment of AIdriven IDS within cloud networks, emphasizing their capability to reduce false positives, improve scalability, and enable automated threat response. Furthermore, it highlights the challenges associated with data privacy, model interpretability, and computational overhead, while discussing potential directions for future advancements, such as federated learning and lightweight AI models. The study concludes that AI-driven IDS offer a robust, adaptive, and intelligent framework for securing next-generation cloud infrastructures.

Cloud Computing, Intrusion Detection System (IDS), Artificial Intelligence, Machine Learning, Deep Learning, Cybersecurity, Network Security

"AI-Driven Intrusion Detection Systems for Securing Cloud Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 9, page no.f22-f29, September-2025, Available :http://www.jetir.org/papers/JETIR2509504.pdf

"AI-Driven Intrusion Detection Systems for Securing Cloud Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 9, page no. ppf22-f29, September-2025, Available at : http://www.jetir.org/papers/JETIR2509504.pdf