Title :
An Effective Back Propagation Neural Network Architecture for the Development of an Efficient Anomaly Based Intrusion Detection System
Author :
Sen, Nilanjan ; Sen, Rinku ; Chattopadhyay, Manojit
Author_Institution :
Dept. of Comput. Applic., Pailan Coll. of Manage. & Technol., Kolkata, India
Abstract :
The problem of intrusion is gradually becoming nightmare for several organizations. To protect the valuable data of their clients, organizations implement security systems to detect and prevent security breaches. But since the intruders are using sophisticated techniques to penetrate the systems, even the highly reputed secured systems have become vulnerable now. To deal with the current scenario, advanced level of researches are required to be carried out to invent more sophisticated Intrusion Detection System (IDS). Among various methodologies, researchers consider Back Propagation Neural Network (BPNN) as a very effective and popular tool for developing an IDS. In this paper, we have proposed an efficient BPNN architecture for the development of an anomaly based IDS with high accuracy and detection rate. The KDD´99 data set is used in this context to develop the architecture.
Keywords :
backpropagation; computer network security; neural nets; BPNN architecture; anomaly based IDS; anomaly based intrusion detection system; back propagation neural network architecture; client data; robust supervised learning neural network; security breach detection; security breach prevention; security system; security vulnerability; valuable data protection; Accuracy; Artificial intelligence; Artificial neural networks; Intrusion detection; Testing; Training; Anomaly detection; Artificial neural network; BPNN; Intrusion Detection System; KDD ´99 data set; Network security;
Conference_Titel :
Computational Intelligence and Communication Networks (CICN), 2014 International Conference on
Print_ISBN :
978-1-4799-6928-9
DOI :
10.1109/CICN.2014.221
