Intrusion Detection System Using AI and Machine Learning Algorithm
Tashfeen, M.T.A. (2024). Intrusion Detection System Using AI and Machine Learning Algorithm. In I.U. Khan, M. Ouaissa, M. Ouaissa, Z.A.E. Houda, & M.F. Ijaz (Eds.), Cyber Security for Next-Generation Computing Technologies (pp. 120-140). CRC Press. https://doi.org/10.1201/9781003404361-7
The more efficient method for reducing the burden of analysts is robust automatic threat identification and mitigation, which scans the computing and network operations and alerts analysts if any questionable behavior is found in the communications. It continually watches the system and reacts to the attack surface. From stage to stage, this reaction function changes. In this case, unusual activity is discovered through the use of artificial intelligence, which serves as a digital analyst while working with network intrusion detection systems to protect against the risk landscape and take necessary action with the analyst's approval. In the process's last stage, packet analysis is used to search for network attacks and classify supervised and unsupervised data. The unsupervised information is processed or transformed into supervised data with the assistance of analyst input, and the engine (simulated analyst algorithm) is then automatically updated. It employs a proactive machine learning method for the program to improve over time and grow stronger, and more efficient as a result; it can fight against identical or comparable strikes.