Subject: An Intrusion Detection Approach Based on Improved Deep Belief Network
Lecturer: Prof. Kuan-Ching Li, University of California(Irvine)
Time: 1:30 p.m.-3:00p.m. , Oct. 26, 2019
Place: Room 235, CIE
Abstract:
With the advances and development of network technology, network attacks and intrusion methods have become increasingly complex and diverse. At present, these existing intrusion detection technologies have overfitting, low classification accuracy and high false positive rate (FPR). In this paper, an intrusion detection approach based on improved Deep Belief Network (DBN) is proposed, where the dataset is processed by Probabilistic Mass Function (PMF) encoding and Min-Max normalization method to simplify the data preprocessing. And, a combined sparse penalty term based on Kullback-Leibler (KL) divergence and non-mean Gaussian distribution is introduced in the likelihood function of the unsupervised training phase of DBN. The sparse distribution of the dataset is obtained by sparse constraints, avoiding the problem of feature homogeneity and overfitting. By using the NSL-KDD and UNSW-NB15 datasets, the experimental results show that the proposed approach has significant improvement in classification accuracy, and FPR.
Boigraphy:
Kuan-Ching Li is a Professor of Computer Science and Engineering at University of California(Irvine), the United States. He received guest and distinguished chair professorships from universities in China and other countries, and a recipient of awards and funding support from several agencies and industrial companies. He has been actively involved in many conferences and workshops in program/general/steering conference chairman positions and has organized numerous conferences related to high-performance computing and computational science and engineering. Besides the publication of research papers, he is co-author/co-editor of several technical professional books published by CRC Press, Springer, McGraw-Hill and IGI Global. He is a Fellow of IET,a life member of TACC, a senior member of the IEEE and a member of the AAAS, and Editor-in-Chief of International Journal of Computational Science and Engineering (IJCSE), International Journal of Embedded Systems (IJES), and International Journal of High-Performance Computing and Networking (IJHPCN), published by Inderscience. His research interests include GPU/many-core computing, Big Data and Cloud.