The utilization of the Internet has grown tremendously resulting in more critical data are being transmitted and handled online.Hence, these occurring changes have led to draw the conclusion that thenumber of attacks on the important information over the internet is increasing
yearly.Intrusion is one of the main threat to the internet.Various techniques and approaches have been developed to address the limitations of intrusion detection system such as low accuracy, high false alarm rate, and time consuming. This research proposed a hybrid machine learning technique for network intrusion detection based on combination of K-means clustering and support vector machine classification.The aim of this research is to reduce the rate of false positive alarm, false negative alarm rate and to improve the detection rate.The NSL-KDD dataset has been used in the proposed technique.In order to improve classification performance, some steps have been taken on the dataset.The classification has been performed by using support vector machine. After training and testing the proposed hybrid machine learning technique, the results have shown that the proposed technique has achieved a positive detection rate and reduce the false alarm rate.

Hybrid machine learning technique for intrusion detection system

Recently, with wide use of computer systems, internet, and rapid growth of computer networks, the problem of intrusion detection in network security has become an important issue of concern. In this regard, various intrusion detection systems have been developed for using misuse detection and anomaly detection methodologies. These systems try to improve detection rates of variation in attack types and reduce the false positive rate. In this paper, a new intrusion detection method has been introduced using MinMax K-means clustering algorithm, which overcomes the shortage of sensitivity to initial centers in K-means algorithm, and increases the quality of clustering. The experiments on the NSL-KDD data set indicate that the proposed method is more efficient than that based on K-means clustering algorithm. Also, the method has higher detection rate and lower false positive detection rate.

Intrusion detection based on MinMax K-means clustering

With the vulgarization of Internet, the easy access to its resources and the rapid growth in the number of computers and networks, the security of information systems has become a crucial topic of research and development especially in the field of intrusion detection. Techniques such as machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a malicious activity is taking place on a network. This paper presents a new intrusion detection system (IDS) based on information gain criterion to select relevant features from network traffic records and a new version of support vector domain description to classify the extracted features and to detect new intrusions. Experimental evaluation on NSL-KDD, a filtered version of the original KDD99 has shown that the proposed IDS can achieve good performance in terms of intrusions detection and recognition.

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New Intrusion Detection System Based on Support Vector Domain Description with Information Gain Metric.

Intrusion Detection System (IDS) is becoming a vital component of any network in today's world of Internet. IDS are an effective way to detect different kinds of attacks in an interconnected network thereby securing the network. An effective Intrusion Detection System requires high accuracy and detection rate as well as low false alarm rate. This paper focuses on a hybrid approach for intrusion detection system (IDS) based on data mining techniques. The main research method is clustering analysis with the aim to improve the detection rate and decrease the false alarm rate. Most of the previously proposed methods suffer from the drawback of k-means method with low detection rate and high false alarm rate. This paper presents a hybrid data mining approach encompassing feature selection, filtering, clustering, divide and merge and clustering ensemble. A method for calculating the number of the cluster centroid and choosing the appropriate initial cluster centroid is proposed in this paper. The IDS with clustering ensemble is introduced for the effective identification of attacks to achieve high accuracy and detection rate as well as low false alarm rate.

An efficient approach for Intrusion Detection using data mining methods

Network Intrusion Detection Systems (NIDSs) have always been designed to enhance and improve the network security issue by detecting, identifying, assessing and reporting any unauthorized and illegal network connections and activities. The purpose of this research is to improve on the existing Anomaly Based Intrusion Detection (ABID) method using K-Means clustering technique as to maximize the detection rate and accuracy while minimizing the false alarm. The problem with outliers may disturb the K-Means clustering process as it might be avoided in the clustering process from mixing with the normal data that make the NIDSs become less accurate. Thus this research aims to improve the performance of the ABID systems that balance the loss of information or ignored data in clustering. An integrated machine learning algorithm using K-Means Clustering with discretization technique and Naive Bayes Classifier (KMC-D+NBC) is proposed against ISCX 2012 Intrusion Detection Evaluation Dataset. The outcome depicts that the proposed method generates better detection rate and accuracy up to 99.3% and 99.5% respectively and reduces the false alarm to 1.2% with better efficiency of 0.03 seconds time taken to build model.

Oving K-Means Clustering using discretization technique in Network Intrusion Detection System

Intrusion Detection System (IDS) is a vital component of any network in today's world of Internet. IDS are an effective way to detect different kinds of attacks in interconnected network. An effective Intrusion Detection System requires high accuracy and detection rate as well as low false alarm rate. Different Data Mining techniques such as clustering and classification are proving to be useful for analyzing and dealing with large amount of network traffic. This paper presents various data mining techniques applied on intrusion detection systems for the effective identification of both known and unknown patterns of attacks, to develop secure information systems.

An Overview of Intrusion Detection Based on Data Mining Techniques

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An Overview of Intrusion Detection Based on Data Mining Techniques