scholarly journals Comparison of Intrusion Detection System Hybrid Approach in Computer Networks with Previous Methods

2019 ◽  
Vol 11 (3) ◽  
pp. 464-475
Author(s):  
Mehdi Khodamoradi
Keyword(s):  
Intrusion Detection ◽  
Computer Networks ◽  
Intrusion Detection System ◽  
Detection System ◽  
Hybrid Approach

Implementasi Iptables Firewall dan Intrusion Detection System Untuk Mencegah Serangan DDoS Pada Linux Server

2021 ◽  
pp. 19-23
Author(s):  
Theodorus Kristian Widianto ◽  
Wiwin Sulistyo
Keyword(s):  
Intrusion Detection ◽  
Computer Networks ◽  
Intrusion Detection System ◽  
Detection System ◽  
Ddos Attacks ◽  
Server Side ◽  
Ddos Attack ◽  
Internet Users ◽  
Data Theft ◽  
Server Computer

Security on computer networks is currently a matter that must be considered especially for internet users because many risks must be borne if this is negligent of attention. Data theft, system destruction, and so on are threats to users, especially on the server-side. DDoS is a method of attack that is quite popular and is often used to bring down servers. This method runs by consuming resources on the server computer so that it can no longer serve requests from the user side. With this problem, security is needed to prevent the DDoS attack, one of which is using iptables that has been provided by Linux. Implementing iptables can prevent or stop external DDoS attacks aimed at the server.

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.

scholarly journals Security Enrichment in Intrusion Detection System Using Classifier Ensemble

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Uma R. Salunkhe ◽  
Suresh N. Mali
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Detection System ◽  
Hybrid Approach ◽  
Classifier Ensemble ◽  
Intrusion Detection Systems ◽  
Detection Rates ◽  
Detection Systems

In the era of Internet and with increasing number of people as its end users, a large number of attack categories are introduced daily. Hence, effective detection of various attacks with the help of Intrusion Detection Systems is an emerging trend in research these days. Existing studies show effectiveness of machine learning approaches in handling Intrusion Detection Systems. In this work, we aim to enhance detection rate of Intrusion Detection System by using machine learning technique. We propose a novel classifier ensemble based IDS that is constructed using hybrid approach which combines data level and feature level approach. Classifier ensembles combine the opinions of different experts and improve the intrusion detection rate. Experimental results show the improved detection rates of our system compared to reference technique.

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