• Archana C
• Aadhilakshmi A
• Keerthana G
• Nayagan S

Recently, technology has been blooming rapidly, and the requirement for security is much more important. And so for the security of the network enterprises, we are using the intrusion detection system. An intrusion detection system is a device or software app that overlooks the inbound and outbound network traffic, continuously analyses for activity changes and patterns, and alerts an administrator. In this paper, we have analysed various methods to detect the threat in real time. Hence, we used a variety of algorithms to detect the intrusion and prevent the attack. With the help of a variety of algorithms, it not only predicts the attack but also finds what type it is. The design of attack prediction tools has always been dominated by statistical methodologies. The supervised machine learning technique (SMLT) helps in analysing the datasets for the collection of information, which includes variable identification, univariate, bivariate, and multivariate analysis, missing values, etc. The most effective machine learning algorithm for predicting the types of cyberattacks has been determined through a comparison study of different algorithms. The outcomes may be compared to the highest levels of accuracy, precision, recall, F1 score, sensitivity, and specificity. Finally, the best accuracy algorithm is used to predict the attack and its type.

Intrusion detection, Attack prediction, Supervised Machine Learning Technique (SMLT), Machine learning

"Intrusion Prediction and Detection using Supervised Machine Learning Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.i233-i238, May-2023, Available :http://www.jetir.org/papers/JETIR2305861.pdf

"Intrusion Prediction and Detection using Supervised Machine Learning Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppi233-i238, May-2023, Available at : http://www.jetir.org/papers/JETIR2305861.pdf