Anomaly based Intrusion Detection System using Machine Learning

Abstract: Attacks on the computer infrastructures are becoming an increasingly serious issue. The problem is ubiquitous and we need a reliable system to prevent it. An anomaly detection-based network intrusion detection system is vital to any security framework within a computer network. The existing Intrusion detection system have a high detection rate but they also have mendacious alert rates. With the use of Machine Learning, we can implement an efficient and reliable model for Intrusion detection and stop some of the hazardous attacks in the network. This paper focuses on detailed study on NSL- KDD dataset after extracting some of the relevant records and then several experiments have been performed and evaluated to assess various machine learning classifiers based on dataset. The implemented experiments demonstrated that the Random forest classifier has achieved the highest average accuracy and has outperformed the other models in various evaluations. Keywords: Intrusion Detection System, Anomaly Detection, Machine Learning, Random Forest, Network Security

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An Intelligent Network Intrusion Detection System Based on Multi-Modal Support Vector Machines

International Journal of Information Security and Privacy ◽

10.4018/ijisp.2013100104 ◽

2013 ◽

Vol 7 (4) ◽

pp. 37-52

Author(s):

Srinivasa K G

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Machine Learning Techniques ◽

Support Vector ◽

Intelligent Network ◽

Statistical Machine Learning ◽

High Detection Rate ◽

Network Intrusion

Increase in the number of network based transactions for both personal and professional use has made network security gain a significant and indispensable status. The possible attacks that an Intrusion Detection System (IDS) has to tackle can be of an existing type or of an entirely new type. The challenge for researchers is to develop an intelligent IDS which can detect new attacks as efficiently as they detect known ones. Intrusion Detection Systems are rendered intelligent by employing machine learning techniques. In this paper we present a statistical machine learning approach to the IDS using the Support Vector Machine (SVM). Unike conventional SVMs this paper describes a milti model approach which makes use of an extra layer over the existing SVM. The network traffic is modeled into connections based on protocols at various network layers. These connection statistics are given as input to SVM which in turn plots each input vector. The new attacks are identified by plotting them with respect to the trained system. The experimental results demonstrate the lower execution time of the proposed system with high detection rate and low false positive number. The 1999 DARPA IDS dataset is used as the evaluation dataset for both training and testing. The proposed system, SVM NIDS is bench marked with SNORT (Roesch, M. 1999), an open source IDS.

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Machine Learning Techniques for Network Intrusion Detection System (NIDS): A Survey

International Journal of Emerging Trends in Engineering Research ◽

10.30534/ijeter/2021/059122021 ◽

2021 ◽

Vol 9 (12) ◽

pp. 1477-1483

Keyword(s):

Machine Learning ◽

Network Security ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Computer Network ◽

Detection System ◽

Vital Role ◽

Cyber Attacks ◽

Machine Learning Techniques ◽

Network Intrusion

In computer network, security of the network is a major issue and intrusion is the most common threats to security. Cyber attacks detection is becoming more enlightened challenge in detecting these threats accurately. In network security, intrusion detection system (IDS) has played a vital role to detect intrusion. In recent years, numerous methods have been proposed for intrusion detection to detect these security threats. This survey paper study examines recent work in the topic of network security, machine learning based techniques as well as a discussion of the many datasets that are commonly used to evaluate IDS. It also explains how researchers employ Machine Learning Based Techniques to detect intrusions

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