scholarly journals Network Intrusion Detection System Based on the Combination of Multiobjective Particle Swarm Algorithm-Based Feature Selection and Fast-Learning Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Sajad Einy ◽  
Cemil Oz ◽  
Yahya Dorostkar Navaei
Keyword(s):  
Intrusion Detection ◽  
Particle Swarm ◽  
Feature Subset Selection ◽  
Particle Swarm Algorithm ◽  
Feature Subset ◽  
Malicious Nodes ◽  
Learning Network ◽  
Fast Learning ◽  
Multiobjective Particle Swarm Optimization ◽  
Fast Learning Network

Given the growth of wireless networks and the increase of the advantages and applications of communication networks, especially mobile ad hoc networks (MANETs), this type of network has attracted the attention of users and researchers more than before. The benefit of these types of networks in various kinds of networks and environments is that MANET does not require to hardware infrastructure to communicate and send and receive data packets within the network. It is one of the main reasons for using these MANET in various fields. On the other hand, the increased popularity of these networks has led to many challenges, one of the most important of which is network security. In this regard, a lack of regulatory and security infrastructure in MANETs has caused some problems in sending and receiving data, where intrusion in the network has been recognized as one of the most important issues. In MANETs, wireless notes act as a link between the source and destination nodes and play the role of relays and routers in the network. Therefore, malicious node penetration and the destruction of information packages become feasible. Today, intrusion detection systems (IDSs) are used as a solution to deal with the problem through remote monitoring of the performance and behaviors of nodes existing in wireless sensor networks. In addition to detecting malicious nodes in the network, IDSs can predict the behavior of malicious nodes in the future in most cases. Therefore, the present study introduced a network IDS (NIDS) entitled MOPSO-FLN by using a combination of multiobjective particle swarm optimization algorithm- (MOPSO-) based feature subset selection (FSS) and fast-learning network (FLN). In this work, we used the KDD Cup99 and dataset to select features, train the network, and test the model. According to the simulation results, this method was able to improve the performance of the IDS in terms of evaluation criteria, compared to other previous methods, by creating a balance between the objectives of the number of representative features and training errors based on the evolutionary power of MOPSO.

scholarly journals A New Intrusion Detection System Based on Fast Learning Network and Particle Swarm Optimization

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 20255-20261 ◽  
Author(s):  
Mohammed Hasan Ali ◽  
Bahaa Abbas Dawood Al Mohammed ◽  
Alyani Ismail ◽  
Mohamad Fadli Zolkipli
Keyword(s):  
Particle Swarm Optimization ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Learning Network ◽  
Fast Learning ◽  
Fast Learning Network

scholarly journals Propose a new Firefly-Fast Learning Network model based Intrusion-Detection System

2019 ◽  
Vol 8 (12S2) ◽  
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Hybrid Models ◽  
Intrusion Detection Systems ◽  
Learning Network ◽  
Detection Systems ◽  
Model Based ◽  
Fast Learning ◽  
Fast Learning Network

Currently, effective Intrusion-detection systems (IDS) still represent one of the important security tools. However, hybrid models based on the IDS achieve better results compared with intrusion detection based on a single algorithm. But even so, the hybrid models based on traditional algorithms still face different limitations. This work is focused on providing two main goals; firstly, analysis based on the main methods and limitations of the most-recent hybrid model-based on intrusion detection, secondly, to propose a novel hybrid IDS model called FA-FLN based on the Firefly algorithm and Fast Learning Network.

Improved Intrusion Detection Algorithm based on TLBO and GA Algorithms

2021 ◽  
Vol 18 (2) ◽  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Optimization Algorithm ◽  
Feature Subset Selection ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Feature Subset ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Optimization algorithms are widely used for the identification of intrusion. This is attributable to the increasing number of audit data features and the decreasing performance of human-based smart Intrusion Detection Systems (IDS) regarding classification accuracy and training time. In this paper, an improved method for intrusion detection for binary classification was presented and discussed in detail. The proposed method combined the New Teaching-Learning-Based Optimization Algorithm (NTLBO), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) NTLBO algorithm with supervised machine learning techniques for Feature Subset Selection (FSS). The process of selecting the least number of features without any effect on the result accuracy in FSS was considered a multi-objective optimization problem. The NTLBO was proposed in this paper as an FSS mechanism; its algorithm-specific, parameter-less concept (which requires no parameter tuning during an optimization) was explored. The experiments were performed on the prominent intrusion machine-learning datasets (KDDCUP’99 and CICIDS 2017), where significant enhancements were observed with the suggested NTLBO algorithm as compared to the classical Teaching-Learning-Based Optimization algorithm (TLBO), NTLBO presented better results than TLBO and many existing works. The results showed that NTLBO reached 100% accuracy for KDDCUP’99 dataset and 97% for CICIDS dataset

Sign in / Sign up

Export Citation Format

Share Document