Informatics Publishing Limited

Rishi Sayal ¹, S. Megha Chandrika ¹

Affiliations
1 Guru Nanak Engineering College, IN

Abstract

Data mining techniques make it possible to search large amounts of data for characteristic rules and patterns. If applied to network monitoring data recorded on a host or in a network, they can be used to detect intrusions, attacks and/or anomalies. In this paper, we present "Supervised & Unsupervised learning" a method to cascade K-means clustering and the Id3 decision tree learning methods to classifying anomalous and normal activities in a computer network. The K-means clustering method first partitions the training instances into two clusters using Euclidean distance similarity. On each cluster, representing a density region of normal or anomaly instances, we build an ID3 decision tree. The decision tree on each cluster refines the decision boundaries by learning the subgroups within the cluster. Our work studies the best algorithm by using classifyinganomalous and normal activities in a computer networks with supervised & unsupervised algorithms that have not been used before. We analyses the algorithm that have the best efficiency or the best learning and describes the proposed system of K-means&ID3 Decision Tree.

Keywords

Data Mining, Intrusion, Anomaly Detection, Anomalies, K-Means, Decision Tree.

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Rishi Sayal ¹, P. Harsha ²

Affiliations
1 CSE Dept., Guru Nanak Engineering College, IN
2 Guru Nanak Engineering College, Affiliated to JNTU, Hyderabad, IN

Abstract

Intrusions pose a serious security problem in the network environment. Security of computer systems is very important to their utility and acceptance. Security in the network environment is maintained by monitoring the data. Security of a network also depends on the amount of data to be monitored. Unfortunately the available amount of network audit data instances is usually large that usually demands human labeling which is a tedious and time consuming task as well as proven to be expensive. To solve similar kinds of problems such as mentioned above, like the human interventions or high level of data biasing intrusion detection systems implement a suite of techniques that use data mining machine learning systems to identify the attacks against computers and network infrastructures. The suites of IDS techniques are widely discussed through out the paper.

Keywords

Data Mining, Intrusion, Intrusion Detection Systems, Machine Learning Systems.

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