Published Paper Details:

There is drastic increase in needs of networking and data sharing in today�s world. Such globalization of increased information technology and development there exists need of network security. Firewalls may provide some level of security but they never alert administrator for upcoming attacks. In order to find such abnormal behavior of network packets there is need of reliable detection system for improvement of efficiency and accuracy. As in today�s developing network environment there is threat of new type of attacks daily in the network. So, the network administration system is also needed to be updated regularly for up gradation of security level. One of the network packet monitoring system is Intrusion detection systems (IDS).There are many techniques in the literature for developing these defense systems. However, it is also important to examine the improvement of the datasets used to train and test these security systems. Enhanced datasets extend the detection capabilities of offline and online intrusion detection models. Standard datasets such as KDD 99 and NSL-KDD are obsolete and do not contain data on current attacks such as denial of service. Therefore, they are not suitable for evaluation. This article presents an in-depth analysis of IDS records and presents the challenges of IDS. This article also provides an overview of the deep learning approach that can be used to develop a better network intrusion detection system.