Intrusion Detection Systems Based on Machine Learning Algorithms

Abstract

Networks are important today in the world and data security has become a crucial area of study. An IDS monitors the status of the software and hardware of the network. Curing problems for current IDSs remain they improve detection precision, decrease false alarm rates and track unknown attacks after decades of advancement. Many researchers have focused on the development of IDSs using machine learning approaches to solve the above-described problems. With the high precision of computer teachings, the basic distinctions between usual and irregular data can be recognized automatically. Unknown threats may also be detected because of their generalizability via machine learning system. This paper suggests a taxonomy of IDS, which uses the primary dimension of data objects to classify and sum up IDS literatures based on and dependent on deep learning. We assume this kind of taxonomy is sufficient for researchers in cyber security. We selected three algorithms from machine learning (Bayes Net, Random Forest, Neural Network) and two algorithms of deep learning (RNN, LSTM), and we tested them on KDD cup 99 and evaluated accuracy algorithms, and we used a program WEKA To calculate the accuracy.