Intrusion Detection In Computer Networks Using Machine Learning Algorithms

With the dawn of the COVID-19 age, the communication industry has witnessed a large spike in users as home networks, workplaces and even conferences have gone online. This has led to a rise in the number of victims of cyber network attacks due to lack of ample security measures being taken in most network environments. Hence the introduction of Intrusion Detection Systems (IDSs) is proven to provide an increased security level. Machine Learning (ML) algorithms have been put into extensive use in tasks of intrusion detection. An ML technique that adds to the performance of standard IDS is the Support Vector Machine (SVM) algorithm, owing to their decent generalization nature and the capability to surpass the barriers of dimensionality. The objective of the project is to determine and compare the performance and accuracy of several ML algorithms like k-means clustering, SVM and KNN. The data set used to derive these results is "kddcup99", which contains 41 features. Data preprocessing is the first step towards achieving this goal, by performing feature extraction, followed by calculating the variance of features. This facilitates the filtering of relevant features from the non-linear dataset. Final objective is to separate the dataset into dissimilar classes based on the attack type faced by the network.