The Experts below are selected from a list of 52962 Experts worldwide ranked by ideXlab platform
Xu Rongsheng - One of the best experts on this subject based on the ideXlab platform.
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Intrusion Detection System Based on Machine Learning
Computer Engineering, 2006Co-Authors: Wang Xuren, Xu RongshengAbstract:Intrusion Detection System has some defects,such as signatures being generated manually,updating difficulty and doing nothing in front of large data set.This paper discusses Intrusion Detection System with machine learning techniques.By making usage of Gene algorithm and Bayes classifiers,the defects mentioned above can be reduced to some extent and some tests have been done to show machine learning magic capability in Intrusion Detection System.
Gao Yuan - One of the best experts on this subject based on the ideXlab platform.
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Intrusion Detection System about Wireless LAN
Computers & Security, 2010Co-Authors: Gao YuanAbstract:Based on the analysis of WLAN security,it gives us an idea of how to build a wireless Intrusion Detection System model of distributed linkage mechanism combined with the knowledge of Intrusion Detection System,which is simply analyzed in the article.
Wang Xuren - One of the best experts on this subject based on the ideXlab platform.
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Intrusion Detection System Based on Machine Learning
Computer Engineering, 2006Co-Authors: Wang Xuren, Xu RongshengAbstract:Intrusion Detection System has some defects,such as signatures being generated manually,updating difficulty and doing nothing in front of large data set.This paper discusses Intrusion Detection System with machine learning techniques.By making usage of Gene algorithm and Bayes classifiers,the defects mentioned above can be reduced to some extent and some tests have been done to show machine learning magic capability in Intrusion Detection System.
Chiawei Liu - One of the best experts on this subject based on the ideXlab platform.
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an integrated Intrusion Detection System for cluster based wireless sensor networks
Expert Systems With Applications, 2011Co-Authors: Shunsheng Wang, Kuoqin Yan, Shuching Wang, Chiawei LiuAbstract:A Wireless Sensor Network (WSN) consists of many low-cost, small devices. Usually, as they are deployed to an open and unprotected region, they are vulnerable to various types of attacks. In this research, a mechanism of Intrusion Detection System (IDS) created in a Cluster-based Wireless Sensor Network (CWSN) is proposed. The proposed IDS is an Integrated Intrusion Detection System (IIDS). It can provide the System to resist Intrusions, and process in real-time by analyzing the attacks. The IIDS includes three individual IDSs: Intelligent Hybrid Intrusion Detection System (IHIDS), Hybrid Intrusion Detection System (HIDS) and misuse Intrusion Detection System. These are designed for the sink, cluster head and sensor node according to different capabilities and the probabilities of attacks these suffer from. The proposed IIDS consists of an anomaly and a misuse Detection module. The goal is to raise the Detection rate and lower the false positive rate through misuse Detection and anomaly Detection. Finally, a decision-making module is used to integrate the detected results and report the types of attacks.
Il Kon Kim - One of the best experts on this subject based on the ideXlab platform.
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Improved Kernel Based Intrusion Detection System
Lecture Notes in Computer Science, 2006Co-Authors: Byung-joo Kim, Il Kon KimAbstract:Computer security has become a critical issue with the rapid development of business and other transaction Systems over the Internet. The application of artificial intelligence, machine learning and data mining techniques to Intrusion Detection Systems has been increasing recently. But most research is focused on improving the classification performance of a classifier. Selecting important features from input data leads to simplification of the problem, and faster and more accurate Detection rates. Thus selecting important features is an important issue in Intrusion Detection. Another issue in Intrusion Detection is that most of the Intrusion Detection Systems are performed by off-line and it is not a suitable method for a real-time Intrusion Detection System. In this paper, we develop the real-time Intrusion Detection System, which combines an on-line feature extraction method with the on-line Least Squares Support Vector Machine classifier. Applying the proposed System to KDD CUP 99 data, experimental results show that it has a remarkable feature feature extraction, classification performance and reducing Detection time compared to existing off-line Intrusion Detection System.
