The Experts below are selected from a list of 17541 Experts worldwide ranked by ideXlab platform
Xie Lixia - One of the best experts on this subject based on the ideXlab platform.
-
Application of Network Intrusion Deception Technology——Honeypot System
Computer Engineering, 2020Co-Authors: Xie LixiaAbstract:This paper presents a kind of Network Intrusion deception technology–honeypot system and it’s application to cybersecurity,describes it’s architecture and functionalities.This paper also presents some deployment schemes and application features about honeypot system on enterprise Networks.Test results demonstrate that honeypot system can decoy,slower and block Network Intrusion attacks to sensitive hosts effectively.
-
application of Network Intrusion deception technology honeypot system
Computer Engineering, 2006Co-Authors: Xie LixiaAbstract:This paper presents a kind of Network Intrusion deception technology–honeypot system and it’s application to cybersecurity,describes it’s architecture and functionalities.This paper also presents some deployment schemes and application features about honeypot system on enterprise Networks.Test results demonstrate that honeypot system can decoy,slower and block Network Intrusion attacks to sensitive hosts effectively.
Willy Susilo - One of the best experts on this subject based on the ideXlab platform.
-
Interactive three-dimensional visualization of Network Intrusion detection data for machine learning
Future Generation Computer Systems, 2020Co-Authors: Wei Zong, Yang-wai Chow, Willy SusiloAbstract:Abstract The threat of cyber-attacks is on the rise in the digital world today. As such, effective cybersecurity solutions are becoming increasingly important for detecting and combating cyber-attacks. The use of machine learning techniques for Network Intrusion detection is a growing area of research, as these techniques can potentially provide a means for automating the detection of attacks and abnormal traffic patterns in real-time. However, misclassification is a common problem in machine learning for Intrusion detection, and the improvement of machine learning models is hindered by a lack of insight into the reasons behind such misclassification. This paper presents an interactive method of visualizing Network Intrusion detection data in three-dimensions. The objective is to facilitate the understanding of Network Intrusion detection data using a visual representation to reflect the geometric relationship between various categories of Network traffic. This interactive visual representation can potentially provide useful insight to aid the understanding of machine learning results. To demonstrate the usefulness of the proposed visualization approach, this paper presents results of experiments on commonly used Network Intrusion detection datasets.
-
CW - A 3D Approach for the Visualization of Network Intrusion Detection Data
2018 International Conference on Cyberworlds (CW), 2018Co-Authors: Wei Zong, Yang-wai Chow, Willy SusiloAbstract:With the increasing threat of cyber attacks, machine learning techniques have been researched extensively in the area of Network Intrusion detection. Such techniques can potentially provide a means for the real-time automated detection of attacks and abnormal traffic patterns. However, misclassification is a common problem in machine learning techniques for Intrusion detection, and a lack of insight into why such misclassification occurs impedes the improvement of machine learning models. This paper presents an approach to visualizing Network Intrusion detection data in 3D. The purpose of this is to facilitate the understanding of Network Intrusion detection datasets using a visual representation to reflect the geometric relationship between various categories of Network traffic. This can potentially provide useful insight to aid the design of machine learning techniques. This paper demonstrates the usefulness of the proposed 3D visualization approach by presenting results of experiments on commonly used Network Intrusion detection datasets.
Serhat Peker - One of the best experts on this subject based on the ideXlab platform.
-
A Comparison of Neural Network Approaches for Network Intrusion Detection
Artificial Intelligence and Applied Mathematics in Engineering Problems, 2020Co-Authors: Serhat PekerAbstract:Nowadays, Network Intrusion detection is an important area of research in computer Network security, and the use of artificial neural Networks (ANNs) have become increasingly popular in this field. Despite this, the research concerning comparison of artificial neural Network architectures in the Network Intrusion detection is a relatively insufficient. To make up for this lack, this study aims to examine the neural Network architectures in Network Intrusion detection to determine which architecture performs best, and to examine the effects of the architectural components, such as optimization functions, activation functions, learning momentum on the performance. For this purpose, 6480 neural Networks were generated, their performances were evaluated by conducting a series of experiments on KDD99 dataset, and the results were reported. This study will be a useful reference to researchers and practitioners hoping to use ANNs in Network Intrusion detection.
-
The Use of Artificial Neural Networks in Network Intrusion Detection: A Systematic Review
2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 2018Co-Authors: Mehmet Uğur Öney, Serhat PekerAbstract:Network Intrusion detection is an important research field and artificial neural Networks have become increasingly popular in this subject. Despite this, there is a lack of systematic literature review on that issue. In this manner, the aim of this study to examine the studies concerning the application artificial neural Network approaches in Network Intrusion detection to determine the general trends. For this purpose, the articles published within the last decade from 2008 to 2018 were systematically reviewed and 43 articles were retrieved from commonly used databases by using a search strategy. Then, these selected papers were classified by the publication type, the year of publication, the type of the neural Network architectures they employed, and the dataset they used. The results indicate that there is a rising trend in the usage of ANN approaches in the Network Intrusion detection with the gaining popularity of deep neural Networks in recent years. Moreover, the KDD'99 dataset is the most commonly used dataset in the studies of Network Intrusion detection using ANNs. We hope that this paper provides a roadmap to guide future research on Network Intrusion detection using ANNs.
Wei Zong - One of the best experts on this subject based on the ideXlab platform.
-
Interactive three-dimensional visualization of Network Intrusion detection data for machine learning
Future Generation Computer Systems, 2020Co-Authors: Wei Zong, Yang-wai Chow, Willy SusiloAbstract:Abstract The threat of cyber-attacks is on the rise in the digital world today. As such, effective cybersecurity solutions are becoming increasingly important for detecting and combating cyber-attacks. The use of machine learning techniques for Network Intrusion detection is a growing area of research, as these techniques can potentially provide a means for automating the detection of attacks and abnormal traffic patterns in real-time. However, misclassification is a common problem in machine learning for Intrusion detection, and the improvement of machine learning models is hindered by a lack of insight into the reasons behind such misclassification. This paper presents an interactive method of visualizing Network Intrusion detection data in three-dimensions. The objective is to facilitate the understanding of Network Intrusion detection data using a visual representation to reflect the geometric relationship between various categories of Network traffic. This interactive visual representation can potentially provide useful insight to aid the understanding of machine learning results. To demonstrate the usefulness of the proposed visualization approach, this paper presents results of experiments on commonly used Network Intrusion detection datasets.
-
CW - A 3D Approach for the Visualization of Network Intrusion Detection Data
2018 International Conference on Cyberworlds (CW), 2018Co-Authors: Wei Zong, Yang-wai Chow, Willy SusiloAbstract:With the increasing threat of cyber attacks, machine learning techniques have been researched extensively in the area of Network Intrusion detection. Such techniques can potentially provide a means for the real-time automated detection of attacks and abnormal traffic patterns. However, misclassification is a common problem in machine learning techniques for Intrusion detection, and a lack of insight into why such misclassification occurs impedes the improvement of machine learning models. This paper presents an approach to visualizing Network Intrusion detection data in 3D. The purpose of this is to facilitate the understanding of Network Intrusion detection datasets using a visual representation to reflect the geometric relationship between various categories of Network traffic. This can potentially provide useful insight to aid the design of machine learning techniques. This paper demonstrates the usefulness of the proposed 3D visualization approach by presenting results of experiments on commonly used Network Intrusion detection datasets.
Telugu Praveen Kumar - One of the best experts on this subject based on the ideXlab platform.
-
Network Intrusion detection system using string matching
2010Co-Authors: Siddharth Saha, Telugu Praveen KumarAbstract:Network Intrusion detection system is a retrofit approach for providing a sense of security in existing computers and data Networks, while allowing them to operate in their current open mode. The goal of a Network Intrusion detection system is to identify, preferably in real time, unauthorized use, misuse and abuse of computer systems by insiders as well as from outside perpetrators. At the heart of every Network Intrusion detection system is packet inspection which employs nothing but string matching. This string matching is the bottleneck of performance for the whole Network Intrusion detection system. Thus, the need to increase the performance of string matching cannot be more exemplified. In this project, we have studied some of the standard string matching algorithms and implemented them. We have then compared the performance of the various algorithms with varying input sizes. The main focus of the project was the Aho-Corasick algorithm. In addition to using the default implementation of suffix trees, we have used a dense hash set and a sparse hash set implementation- which are libraries from the Google code repository- and we show that the performance for these implementations are better. They give noticeable enhancement in performance when the input size increases.
