An investigation of classification algorithms for intrusion detection system — A quantitative approach

2017 ◽  
Author(s):  
Josy Elsa Varghese ◽  
Balachandra Muniyal
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Quantitative Approach ◽  
Classification Algorithms ◽  
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.

scholarly journals Evaluation of DDoS attacks Detection in a New Intrusion Dataset Based on Classification Algorithms

2019 ◽  
Vol 1 (3) ◽  
pp. 49-55 ◽  
Author(s):  
Amer A. Abdulrahman ◽  
Mahmood K. Ibrahem
Keyword(s):  
Intrusion Detection ◽  
Computer Security ◽  
System Architecture ◽  
Intrusion Detection System ◽  
Network Flows ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Strategic Decision ◽  
Classification Algorithms ◽  
Ddos Attack

Intrusion detection system is an imperative role in increasing security and decreasing the harm of the computer security system and information system when using of network. It observes different events in a network or system to decide occurring an intrusion or not and it is used to make strategic decision, security purposes and analyzing directions. This paper describes host based intrusion detection system architecture for DDoS attack, which intelligently detects the intrusion periodically and dynamically by evaluating the intruder group respective to the present node with its neighbors. We analyze a dependable dataset named CICIDS 2017 that contains benign and DDoS attack network flows, which meets certifiable criteria and is openly accessible. It evaluates the performance of a complete arrangement of machine learning algorithms and network traffic features to indicate the best features for detecting the assured attack classes. Our goal is storing the address of destination IP that is utilized to detect an intruder by method of misuse detection.

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