Intrusion detection system (IDS) is one of the emerging techniques for information security. Security mechanisms for an information system should be designed to prevent unauthorized access of system resources and data. Many intelligent learning techniques of machine learning are applied to the large volumes of data for the construction of an efficient intrusion detection system (IDS). This paper presents an overview of intrusion detection system and a hybrid technique for intrusion detection based on Bayesian algorithm and Genetic algorithm. Bayesian algorithm classifies the dataset into various categories to identify the normal/ attacked packets where as genetic algorithm is used to generate a new data by applying mutation operation on the existing dataset to produce a new dataset. Thus this algorithm classifies KDD99 benchmark intrusion detection dataset to identify different types of attacks with high detection accuracy. The experimental result also shows that the accuracy of detecting attacks is fairly good.

In today's world data analytics is gaining popularity due to user's motivation towards online data storage. This storage is not organized because of content types and data handling schemes complexity. User aims to retrieve data in lesser time with logical outcomes as desired can be achieved by applying data mining. Clustering in data mining is one of the known categorization approach used for formation of groups of similar elements having certain properties in common with other elements. This formation sometime creates noisy result in terms of formatted clusters. It depends on various factors such as distance measures, proximity values, objective functions, categorical or numerical attribute types etc. Over the last few years various schemes are suggested by different authors for improving the performance of tradition clustering algorithms. Among them, one is ensemble based clustering. Ensemble uses the mechanism for criteria selection from newly formed clusters with a defined portioning and joining methods to generate a single result instead of multiple solutions. The generation results are affected by various environmental parameters such as number of cluster, partitioning types, proximity values, objective function etc. This paper propose a novel SMCA based ensemble clustering algorithm for improvements over the existing issues defined in the paper. At the primary level of work and analytical evaluations, it shows the promising results in near future.