• Kalpana Verma

This global network, made up of hundreds of millions of computers running different hardware and software combinations, allows people to interact and do business. Because computers are connected to one another, this makes it easier for hackers to misuse resources and launch Internet attacks. The creation of a security-focused strategy that might be adaptable and agile in view of the rising frequency of cyberattacks faces substantial obstacles. To identify online dangers, one needs an intrusion detection system (IDS). An intrusion detection system, or IDS, is necessary to keep a network secure. We must also create a strong IDS for the cloud platform because it is constantly growing and permeating more aspects of our daily life. However, using standard intrusion detection systems in the cloud may provide challenges. The additional detection overhead introduced by the pre-defined IDS design may lead a cloud segment to become overloaded. within the framework of a flexible architecture networked system. We demonstrate how to fully utilize available resources without overtaxing any particular cloud server by using a neural network-based intrusion detection system. Even more efficiently, the suggested IDS employs machine learning with neural networks to detect new threats.

Artificial Intelligence, Intrusion Detection System, Machine learning Algorithm.

"AI-Based Intrusion Detection System in Cloud Computing.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.i254-i259, April-2024, Available :http://www.jetir.org/papers/JETIR2404832.pdf

"AI-Based Intrusion Detection System in Cloud Computing.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppi254-i259, April-2024, Available at : http://www.jetir.org/papers/JETIR2404832.pdf