A tree growth based forward feature selection algorithm for intrusion detection system on convolutional neural network

With the rapid advancement of networking technologies, security system has become increasingly important to academics from several sectors. Intrusion detection (ID) provides a valuable protection by reducing human resources required keep an eye on intruders, improving efficiency detecting various attacks in networks. Machine learning and deep are two key areas that have recently received lot attention, with focus precision classifiers. Using defense anvance research project agency (DARPA”98) datasets, number developed intrusion systems. This paper discusses approaches different researchers, including scale-hybrid-IDS-AlertNet (SHIA), forward feature selection algorithm (FFSA), modified- mutual information (MMIFS), neural network (DNN), holes remain be filled, highlighting where these procedures can improved, also addressed proposed approach improved convolutional (IDCNN) is compared existing approach.