Network Attacks and Intrusion Detection System: A Brief

2019 ◽  
Author(s):  
Neha V Sharma ◽  
Kavita ◽  
Gaurav Agarwal
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Network Attacks ◽  
System A

Solutions of Single Point of Failure in Intrusion Detection System

2011 ◽  
Vol 128-129 ◽  
pp. 285-288 ◽  
Author(s):  
Yan Jing Cai ◽  
Xian Yi Cheng ◽  
Yan Pan
Keyword(s):  
Intrusion Detection ◽  
Mobile Agent ◽  
Intrusion Detection System ◽  
Detection System ◽  
Single Point ◽  
System A ◽  
Security Of Network ◽  
New System

In this paper, Mobile Agent (MA) and a number of intrusion detection system described. Considering the shortcoming of the current intrusion detection system, a new system called the intrusion detection system based on MA was described. Using the autonomy of MA, Intrusion Detection System based on MA avoids single-point failure, and robusts the system. As a result, the security of network has been increased.

NETWORK TRAFFIC ANOMALIES DETECTION USING AN ENSEMBLE OF CLASSIFIERS

2020 ◽  
pp. 38-46
Author(s):  
S. A. Sakulin ◽  
A. N. Alfimtsev ◽  
K. N. Kvitchenko ◽  
L. Ya. Dobkach ◽  
Yu. A. Kalgin
Keyword(s):  
Intrusion Detection ◽  
Network Traffic ◽  
Intrusion Detection System ◽  
Detection System ◽  
Hybrid Approach ◽  
Stochastic Gradient Descent ◽  
Type I ◽  
Network Attacks ◽  
Detection Systems ◽  
Accuracy Increase

Network technologies have been steadily developing and their application has been expanding. One of the aspects of the development is a modification of the current network attacks and the appearance of new ones. The anomalies that can be detected in network traffic conform with such attacks. Development of new and improvement of the current approaches to detect anomalies in network traffic have become an urgent task. The article suggests a hybrid approach to detect anomalies on the basis of the combined signature approach and computationally effective classifiers of machine learning: logistic regression, stochastic gradient descent and decision tree with accuracy increase due to weighted voting. The choice of the classifiers is explained by the admissible complexity of the algorithms that allows detection of network traffic events for the time close to real. Signature analysis is carried out with the help of the Zeek IDS (Intrusion Detection System) signature base. Learning is fulfilled by preliminary prepared (by excluding extra recordings and parameters) CICIDS2017 (Canadian Institute for Cybersecurity Intrusion Detection System) signature set by cross validation. The set is roughly divided into ten parts that allows us to increase the accuracy. Experimental evaluation of the developed approach comparing with individual classifiers and with other approaches by such criteria as part of type I and II errors, accuracy and level of detection, has proved the approach suitable to be applied in network attacks detection systems. It is possible to introduce the developed approach into both existing and new anomaly detection systems.

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