The Study and Simulation on Campus Network Intrusion Detection System

2012 ◽  
Vol 490-495 ◽  
pp. 2657-2661
Author(s):  
Ping Xie ◽  
Wei Wang
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Structural Characteristics ◽  
Functional Independence ◽  
Detection Methods ◽  
Agent Technology ◽  
Campus Network ◽  
Model Framework ◽  
Network Intrusion

In this paper, the current intrusion detection systems are analyzed in the full study of the development trend of domestic and foreign country. According to the campus network can be divided into functional independence of the structural characteristics of the subnet, while taking full advantage of agent technology in the intrusion detection system technology, we have referenced to the agent technology and a variety of detection methods for the analysis and comparison, and have analyzed the existing distributed intrusion detection system ,we propose a monitoring and management center with a multi-agent intrusion detection model framework. This model uses a distributed architecture that combines network-and host-based intrusion detection method for intrusion detection.

Application of Representation Learning based Chronological Modeling for Network Intrusion Detection

2022 ◽  
Vol 16 (1) ◽  
pp. 0-0
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Representation Learning ◽  
Detection Methods ◽  
Network Intrusion Detection ◽  
Moving Averages ◽  
Malicious Activity ◽  
Network Intrusion ◽  
Individual Observation

An autoencoder has the potential to overcome the limitations of current intrusion detection methods by recognizing benign user activity rather than differentiating between benign and malicious activity. However, the line separating them is quite blurry with a significant overlap. The first part of this study aims to investigate the rationale behind this overlap. The results suggest that although a subset of traffic cannot be separated without labels, timestamps have the potential to be leveraged for identification of activity that does not conform to the normal or expected behavior of the network. The second part aims to eliminate dependence on visual-inspections by exploring automation. The trend of errors for HTTP traffic was modeled chronologically using resampled data and moving averages. This model successfully identified attacks that had orchestrated over HTTP within their respective time slots. These results support the hypothesis that it is technically feasible to build an anomaly-based intrusion detection system where each individual observation need not be categorized.

scholarly journals Design of a Network Intrusion Detection System Using Complex Deep Neuronal Networks

2021 ◽  
Vol 13 (3) ◽  
Author(s):  
Mohammed Abdulhammed Al-Shabi
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Neuronal Networks ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Network Intrusion ◽  
Network Intrusion Detection System

Recent years have witnessed a tremendous development in various scientific and industrial fields. As a result, different types of networks are widely introduced which are vulnerable to intrusion. In view of the same, numerous studies have been devoted to detecting all types of intrusion and protect the networks from these penetrations. In this paper, a novel network intrusion detection system has been designed to detect cyber-attacks using complex deep neuronal networks. The developed system is trained and tested on the standard dataset KDDCUP99 via pycharm program. Relevant to existing intrusion detection methods with similar deep neuronal networks and traditional machine learning algorithms, the proposed detection system achieves better results in terms of detection accuracy.

scholarly journals Sistem Pendeteksian Penyusupan Jaringan Komputer dengan Active Response Menggunakan Metode Hybrid Intrusion Detection

2020 ◽  
Author(s):  
afdhal
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Current System ◽  
Internet Technology ◽  
Network Activity ◽  
Detection Methods ◽  
Network Intrusion Detection ◽  
Active Response ◽  
Network Intrusion

ABSTRACTCurrent network intrusion detection systems are generally able to detect various types of attacks but are unable to take further action. In addition the current system does not have interactivity with the administrator when the administrator is not administering the system. This is an ineffective matter especially when the system is in critical condition. This research will be designed and implemented a network intrusion detection system that has the ability to detect suspicious network activity, take further countermeasures.The progress of internet technology increase the need of security data. The progress of tools which have intrusion ability, also influence these needed. The methods of Intrusion Detection System (IDS) implementation and methods of analyze intrusion have excess and lack, which mutually completes. There are a lot of IDS now, but just an IDS open source based is snort. Method of snort implementation is network based restricted. This FinalTask’s system used Hybrid Intrusion Detection System, Signatures and Anomaly Detection Methods. The indicator which used to detect intrusion are IP Address and Port Number. This system use TCP, UDP and ICMP protocols. This system also, is completed by active response, like blocking access for intruder. This System Implementation with Java Programming Language for engine perform and Java Server Pages (JSP) to develop user interface, The database which used is MYSQL. There are two of development test; Link system test and intrusion test. The link system test show the connect each interface. Intrusion is executed by host detection which used DoS HTTP tools and network detection which used Ping of Death’s scripts. The intrusion testing conclusions are; can be detected, analyze and active response for intrusion..

The Network Security System Research Based on Intrusion Detection

2014 ◽  
Vol 596 ◽  
pp. 888-891 ◽  
Author(s):  
Jie Qian ◽  
Yan Ping Wang ◽  
Han Xi Li
Keyword(s):  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Rapid Development ◽  
System Structure ◽  
Detection Methods ◽  
Current Status ◽  
Security Model ◽  
Network Intrusion

With the rapid development of Internet and the network information resources can be Shared height, information system security face severe challenges. Firstly, this article from the current status of the network security, combined the new changes of the network security situation, getting the conclusion that the use of the necessity of intrusion detection system. And then it made a comprehensive overview on network security model put forward that was to achieve the purpose of security, which need to establish a reasonable network security model. By introducing the common types of network intrusion and network security technology, it lead to an intrusion detection system, and the concept of intrusion detection system, system structure and detection methods in detail in this paper.

Proposed Hybrid Classifier to Improve Network Intrusion Detection System using Data Mining Techniques

2020 ◽  
Vol 38 (1B) ◽  
pp. 6-14
Author(s):  
ٍٍSarah M. Shareef ◽  
Soukaena H. Hashim
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
Intrusion Detection System ◽  
Detection System ◽  
Multinomial Logistic Regression ◽  
Network Intrusion Detection ◽  
Limiting Factor ◽  
Network Intrusion ◽  
Network Intrusion Detection System ◽  
Normal Behavior

Network intrusion detection system (NIDS) is a software system which plays an important role to protect network system and can be used to monitor network activities to detect different kinds of attacks from normal behavior in network traffics. A false alarm is one of the most identified problems in relation to the intrusion detection system which can be a limiting factor for the performance and accuracy of the intrusion detection system. The proposed system involves mining techniques at two sequential levels, which are: at the first level Naïve Bayes algorithm is used to detect abnormal activity from normal behavior. The second level is the multinomial logistic regression algorithm of which is used to classify abnormal activity into main four attack types in addition to a normal class. To evaluate the proposed system, the KDDCUP99 dataset of the intrusion detection system was used and K-fold cross-validation was performed. The experimental results show that the performance of the proposed system is improved with less false alarm rate.

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