Multidimensional attacks classification based on genetic algorithm and SVM

In the wide growth of information technology, security has one challenging phase for computer and networks. Attacks on the web are increasing day-by-day. Intrusion detection system is used to detect several types of malicious attacks that can compromise the security of a computer system. Data mining techniques are used to monitor and analyze large amount of network data & classify these network data into abnormal and normal data. Various data mining techniques like classification and clustering are applied to build Intrusion detection system. An effective Intrusion detection system needs high detection rate, low false alarm rate and high accuracy. This presents IDS uses the KDD Cup 99 dataset and completely different Data mining techniques are used on IDS for the effective detection of the abnormal and normal activities in network, that helps to develop secure information system. The multidimensional feature representation method is an important pattern classifier that facilitates correct classifications. Then, this new and multidimensional feature descriptor is used to represent each data sample for intrusion detection and SVM(Support Vector Machine) classifier are used for correct classification of normal data and attacks. It also provides the bad data filtering from a given dataset.