scholarly journals Intrusion Detection System Using Self Organizing Map Algorithms

2014 ◽  
Vol 3 (9) ◽  
pp. 585-588
Author(s):  
Faezeh Mozneb khodaie ◽  
Mohammad Ali Jabraeil Jamali ◽  
Ali Farzan
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Self Organizing Map ◽  
Self Organizing

A Self Organizing Map Intrusion Detection System for RPL Protocol Attacks

2019 ◽  
Vol 11 (1) ◽  
pp. 30-43 ◽  
Author(s):  
Elie Kfoury ◽  
Julien Saab ◽  
Paul Younes ◽  
Roger Achkar
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Early Stage ◽  
Sensor Data ◽  
Self Organizing Map ◽  
Lossy Networks ◽  
Mesh Topology ◽  
Som Neural Network ◽  
Self Organizing

Routing over low power and lossy networks (RPL) is a standardized routing protocol for constrained Wireless Sensor Network (WSN) environments. The main node's constraints include processing capability, power, memory, and energy. RPL protocol describes how WSN nodes create a mesh topology, enabling them to route sensor data. Unfortunately, various attacks exist on the RPL protocol that can disrupt the topology and consume nodes' energy. In this article, the authors propose an intrusion detection system (IDS) based on self-organizing map (SOM) neural network to cluster the WSN routing attacks, and hence notify the system administrator at an early stage, reducing the risk of interrupting the network and consuming nodes' power. Results showed that the proposed SOM architecture is able to cluster routing packets into three different types of attacks, as well as clean data.

scholarly journals Intrusion Detection System Using Back Propagation Algorithm and Compare its Performance with Self Organizing Map

2016 ◽  
Vol 1 ◽  
pp. 127 ◽  
Author(s):  
Subarna Shakya ◽  
Bisho Raj Kaphle
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Back Propagation ◽  
Attack Behavior ◽  
Main Function ◽  
Self Organizing Map ◽  
Back Propagation Algorithm ◽  
Network Intrusion ◽  
Self Organizing

<p>In recent years, internet and computers have been utilized by many people all over the world in several fields. On the other hand, network intrusion and information safety problems are ramifications of using internet. In this thesis it propose a new learning methodology towards developing a novel intrusion detection system (IDS) by back propagation neural networks (BPN) and self organizing map (SOM) and compare the performance between them. The main function of Intrusion Detection System is to protect the resources from threats. It analyzes and predicts the behaviors of users, and then these behaviors will be considered an attack or a normal behavior. The proposed method can significantly reduce the training time required. Additionally, the training results are good. It provides a powerful tool to help supervisors and unsupervisors analyze, model and understand the complex attack behavior of electronic crime.</p><p><em>Journal of Advanced College of Engineering and Management, Vol. 1, 2015</em>, pp. 127-138</p>

scholarly journals Anomaly-based intrusion detector system using restricted growing self organizing map

2019 ◽  
Vol 13 (3) ◽  
pp. 919
Author(s):  
Tomi Yahya Christyawan ◽  
Ahmad Afif Supianto ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Big Data ◽  
Learning Process ◽  
Intrusion Detection System ◽  
Detection System ◽  
Rapid Development ◽  
Network Technology ◽  
Self Organizing Map ◽  
Reference Vector ◽  
Data Problem ◽  
Self Organizing

<p><span>The rapid development of internet and network technology followed by malicious threats and attacks on networks and computers. Intrusion detection system (IDS) was developed to solve that problems. The development of IDS using machine learning is needed for classifying the attacks. One method of the classification is Self-Organizing Map (SOM). SOM able to perform classification and visualization in learning process to gain new knowledge. However, the SOM has less efficient in learning process when applied in Big Data. This study proposes Restricted Growing SOM method with clustering reference vector (RGSOM-CRV) and Parallel RGSOM-CRV to improve SOM efficiency in classification with accuracy consideration to solve Big Data problem. Growing process in RGSOM is restricted by maximum nodes and growing threshold, the reupdate weight process will update unused reference vector when map size already maximum, these two processes solve the consuming time of regular GSOM. From the results of this research against KDD Cup 1999 dataset, proposed method Parallel RGSOM-CRV able to give 91.86% accuracy, 20.58% false alarm rate, 95.32% recall or detection rate, and precision is 94.35% and time consuming is outperform than regular Growing SOM. This proposed method is very promising to handle big data problems compared with other methods.</span></p>

An Efficient Hybrid Self-Learning Intrusion Detection System Based on Neural Networks

2019 ◽  
Vol 18 (01) ◽  
pp. 1950001 ◽  
Author(s):  
Shahriar Mohammadi ◽  
Fatemeh Amiri
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Imperialist Competitive Algorithm ◽  
Hybrid Architecture ◽  
Self Organizing Map ◽  
First Time ◽  
Self Learning

An intrusion detection system (IDS) is an immunizing system that identifies the hostile activities in a network, and alerts the network administrator in case of detecting suspicious behaviors. Signature-based systems are the most common methods for intrusion detection, but however, they are not able to detect new attacks on the network. The main problem of these systems is to keep up to date the database of already containing known attack signatures. Neural networks have a high ability to learn and are generalizable. This study present as follow: A new intrusion detection system that is a hybrid of self-organizing map algorithm (SOM), radial basis function (RBF) and perceptron networks is proposed to solve this problem. For the first time, The Imperialist Competitive Algorithm is used to calculate the parameters of the Perceptron neural network. The proposed approach uses a hybrid architecture that tries to increase the quality of warnings. Signature-based systems using this method can detect new attacks as a self-learner. The results indicated better performance of the proposed hybrid algorithm compared to earlier methods.

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