Improving intrusion detection system using an extreme learning machine algorithm

Abstract

An Intrusion Detection System (IDS) is a system, that checks the network or data for abnormal actions and when such activity is discovered it issues an alert. Numerous IDS techniques are in use these days but one major problem with all of them is their performance. Various works have been done on this issue using support vector machine and multilayer perceptron. Supervised learning models such as support vector machines with related learning algorithms are used to analyze the data which is used for regression analysis and also classification. The IDS is used in analyzing big data as there is huge traffic which has to be analyzed to check for suspicious activities, and also be successful in doing so. Hence, an efficient and fast classification algorithm is required. Machine learning techniques such as neural networks and extreme machine learning are used. Both of these techniques are highly regarded and are considered one of the best techniques. Extreme learning machines are feed forward neural networks which have one hidden layer and no back propagation used for classification. Once the intrusion is detected using IDS through ELM then we are also going to detect the type of intrusion using the Random Forest Technique (Multi class classification) efficiently with a higher rate of accuracy and precision. The NSL_KDD dataset which is very well-known used for the training as well as testing of these IDS algorithms. This work determines that compared to artificial neural network and logistic regression extreme learning machines provide a much better rate of intrusion detection, which is 93.96% and is also proven to be more efficient in terms of execution time of 38 seconds.