Machine learning classification model for Network based Intrusion Detection System

2016 ◽  
Author(s):  
Sanjay Kumar ◽  
Ari Viinikainen ◽  
Timo Hamalainen
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Classification Model ◽  
Machine Learning Classification

scholarly journals Modified Gray Wolf Feature Selection and Machine Learning Classification for Wireless Sensor Network Intrusion Detection

2021 ◽  
Vol 3 (2) ◽  
pp. 118-127
Author(s):  
Subarna Shakya
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
False Alarm ◽  
Intrusion Detection System ◽  
Detection System ◽  
Wireless Sensor ◽  
Gray Wolf ◽  
Machine Learning Classification ◽  
Network Intrusion ◽  
Proposed Model

The ability of wireless sensor networks (WSN) and their functions are degraded or eliminated by means of intrusion. To overcome this issue, this paper presents a combination of machine learning and modified grey wolf optimization (MLGWO) algorithm for developing an improved intrusion detection system (IDS). The best number of wolves are found by running tests with multiple wolves in the model. In the WSN environment, the false alarm rates are reduced along with the reduction in processing time while improving the rate of detection and the accuracy of intrusion detection with a decrease in the number of resultant features. In order to evaluate the performance of the proposed model and to compare it with the existing techniques, the NSL KDD’99 dataset is used. In terms of detection rate, false alarm rate, execution time, total features and accuracy the evaluation and comparison is performed. From the evaluation results, it is evident that higher the number of wolves, the performance of the MLGWO model is enhanced.

scholarly journals Performance and Computation Time Enhancement of Various Machine Learning Techniques for NSL-KDD Dataset

2020 ◽  
Vol 8 (6) ◽  
pp. 4726-4730
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Missing Values ◽  
Detection System ◽  
Computation Time ◽  
Machine Learning Techniques ◽  
Huge Number ◽  
Machine Learning Classification ◽  
Learning Techniques

To develop an effective intrusion detection system we definitely need a standardize dataset with a huge number of correct instances without missing values. This would significantly help the system to train and test for real-time use. Previously for research purpose, KDD-CUP’99 dataset has been used, but later on, it has been seen that it is not so useful for training the model as it consists a lot of missing and abundant values. All this issue have been tackled in NSL dataset. To analyze the capabilities of the dataset for intrusion detection system we have analyzed various machine learning classification algorithm to classify the attack over any network. This paper has explored many facts about the dataset and the computation time.

A MACHINE LEARNING BASED IOT FOR PROVIDING AN INTRUSION DETECTION SYSTEM FOR SECURITY

2021 ◽  
pp. 103741
Author(s):  
Dhanke Jyoti Atul ◽  
Dr. R. Kamalraj ◽  
Dr. G. Ramesh ◽  
K. Sakthidasan Sankaran ◽  
Sudhir Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System
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