Implementation of the NSL-KDD Dataset to Study the Naive Bayes Algorithm for Intrusion Detection Systems

Abstract

Artificial intelligence has turn out to be a vital part of our ecosystem. Major sectors of the market are considering artificial intelligence for business and some sectors are using the technology in boom for their businesses. Advances in business through machine learning technology are reaching miles. Intrusion Detection systems should be robust if more platforms will be using AI technology all over the globe. The research in this work is predicated on the NSL- KDD dataset for detection of malicious activity. The dataset is evaluated using the Naive Bayes algorithm. A machine learning technique that can both learn and adapt to previously unknown patterns has been used in an attempt to develop an intrusion detection system. Unsupervised feature learning has made use of various classifications as required to train and test the model. The NSL-KDD dataset is then classified using a logistic classifier. Accuracy, precision, and recall metrics have been used to measure the system's performance, and the findings are quite positive for possible future modifications and uses.