Intrusion Detection System using Random Forest

Abstract

Abstract—   Abstract. Nowadays, it is very hard to prevent security breaches using current technologies. Thus, the result is that Intrusion Detection becomes an important problem in the security of network and computer forensics. To ensure that communication of information is safe, various systems for detecting intrusions are developed that may have several restrictions in intrusion detection. The rules of encoding intrusion are very time taking and also conditional upon the idea of similar intrusions. Probing, Remote to User (R2L), Denial of Service (DoS), and User to Root(U2R) attacks are those some similar kinds of network attacks that may affect enormous systems in the daily world. Detecting attacks and preventing computers through these is a leading topic for research among researchers in this era. In this paper, we will implement and will highlight Intrusion Detection on the attacks mentioned above. Threats to these types are different and are potentially devastating. Till now, various methodologies have been proposed by the researchers for the “Intrusion Detection System”, some of which are machine learning, Pattern matching, DNA sequence, and data mining are used as expertise to learn about the attack and its different types and even encountered some matching attacks types when came across us in the future. Here, we are using one of the machine learning algorithms “Random Forest” along with one data-mining classification method called Decision Tree. Here, we have classified the problem based on features that have been selected as a parameter for evaluation. We have also used model evaluation and selection methods like accuracy, f-score, precision, recall along with the Confusion Matrix which is a tool used for evaluating performance. And as a result, we have shown the accuracy of our method for each type of attack.