CSEIT183160 | Received : 07 Jan 2018 | Accepted : 20 Jan 2018 | January-February-2018 [(3) 1 : 328-338 ] International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2018 IJSRCSEIT | Volume 3 | Issue 1 | ISSN : 2456-3307 328 A Machine Learning Approach for Intrusion Detection using Ensemble Technique - A Survey Shraddha Khonde *1 , V. Ulagamuthalvi* 2 * 1 Research Scholar, Department of CSE, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India *2 Professor, Department of CSE, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India ABSTRACT An Intrusion detection system is a machine or software that monitors the traffic in a network and on detection of a malicious packet, informs the user or a specific acting unit which can take further action and avoid the malicious packet from entering the network. In network intrusion, there may be multiple computing nodes attacked by intruders. The evidences of intrusions have to gather from all such attacked nodes. An intruder may move between multiple nodes in the network to conceal the origin of attack, or misuse some compromised hosts to launch the attack on other nodes. To detect such intrusion activities spread over the whole network, we present a new intrusion detection system (IDS) that classifies data with three different classifiers and an Ensemble technique that selects the majority of the three classifiers to assign the packet in the network as anomaly or normal. In this paper, we discuss a different ways to implement intelligent IDS, which classifies the normal traffic in a network with abnormal or attacked ones. This paper explains the method that used to generate such a system and the various classifiers used in the generation process. The dataset used to train classifiers can be NSL - KDD, KDD Cup 1999, KDD99 dataset. The IDS proposed here can serve many applications in the field of Military Systems, Banks and Social Networking websites where data is very sensitive. The paper also explains related work done in this field and briefly explains every classifier, the network attacks and the dataset. General Terms: Network Security, Intrusion Detection, IDS, Artificial Intelligence, Machine Learning, Ensemble, SVM. Random Forest, Decision Tree, Collaborative IDS, Distributed IDS. Keywords: IDS, Intrusion Detection System, Artificial Intelligence, AI, Majority Voting, Ensemble Learning, Random Forest, SVM, DT, Collaborative IDS and Distributed IDS. I. INTRODUCTION Intrusion has become a growing concern today. With the advent of new technologies each day and widespread of computers (from personal computers to embedded systems), security has become a very important issue. To name a few Attacks like Ransom ware, DoS, DDoS, U2R, R2L have become a great deal of concern to every computer in the network. Such attacks compromise the security of the computer and obtain access to sensitive data. Hence, Security of any network is a high priority issue that taken care. Various Intrusion Detection Systems (IDS) exist which help identify threats in the system but only an intelligent system will correctly yield them with maximum accuracy. With Data Mining, Machine Learning and Artificial Intelligence becoming pervasive in the computer world, it sets its foot into the area of Network Security as well. Hence, we could make full use of it and create a system that