Intrusion Detection System Using Supervised Machine Learning

The whole world is joining the Internet because our world is moving toward digitalization. Internet/networks are significant today on the planet; information security has turned into a pivotal area of study. As the number of users of the Internet is increasing, it has become a challenge to provide that network security. Nowadays, the improvement of organization security is subsequently featured. Assurance of the network permits the accidental impedance to a structure to arrange and stay away from it. Intrusion detection system (IDS) is one of the most fundamental security devices for the overwhelming majority of security concerns existing in the present digital network world. IDS is developed to inspect the framework applications and organization traffic to uncover dubious exercises and issue an admonition in the event that it is found. To create a more perfect system, countless strategies are accessible in AI for intrusion detection. The main purpose of this paper is to perform anomaly-based intrusion detection systems and apply different machine learning algorithms techniques to the dataset and then compare and estimate their performances. In this paper, we use the KDD’99 cup dataset (KDD cup 1999 data. Retrieved from http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html . Accessed on 7 Nov 2021) and Pearson’s Correlation method to select the important features from the dataset and remove those features that are useless in finding the accuracy. The preprocessed dataset was tried with the models (Decision Tree, Support Vector Machine and Logistic Regression) to get the noticeable outcomes, which prompts expanding the expectation precision. Machine learning methods, namely Decision Tree, Support Vector Machine, Logistic Regression, are used. The examination provides a predictive computational methodology to boost intrusion detection in the Network Traffic Data along with applying different approaches for the appraisal of the best accuracy from machine learning. The aftereffects of various order algorithms analyzed finally by utilizing the proposed dataset have been introduced in the paper for their functionality.