Intrusion detection system using fuzzy genetic algorithm

2015 ◽  
Author(s):  
Yogita Danane ◽  
Thaksen Parvat
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Fuzzy Genetic Algorithm ◽  
Fuzzy Genetic

scholarly journals A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1046 ◽  
Author(s):  
Omar Almomani
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Selection Model ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Proposed Model ◽  
Positive Rate

The network intrusion detection system (NIDS) aims to identify virulent action in a network. It aims to do that through investigating the traffic network behavior. The approaches of data mining and machine learning (ML) are extensively used in the NIDS to discover anomalies. Regarding feature selection, it plays a significant role in improving the performance of NIDSs. That is because anomaly detection employs a great number of features that require much time. Therefore, the feature selection approach affects the time needed to investigate the traffic behavior and improve the accuracy level. The researcher of the present study aimed to propose a feature selection model for NIDSs. This model is based on the particle swarm optimization (PSO), grey wolf optimizer (GWO), firefly optimization (FFA) and genetic algorithm (GA). The proposed model aims at improving the performance of NIDSs. The proposed model deploys wrapper-based methods with the GA, PSO, GWO and FFA algorithms for selecting features using Anaconda Python Open Source, and deploys filtering-based methods for the mutual information (MI) of the GA, PSO, GWO and FFA algorithms that produced 13 sets of rules. The features derived from the proposed model are evaluated based on the support vector machine (SVM) and J48 ML classifiers and the UNSW-NB15 dataset. Based on the experiment, Rule 13 (R13) reduces the features into 30 features. Rule 12 (R12) reduces the features into 13 features. Rule 13 and Rule 12 offer the best results in terms of F-measure, accuracy and sensitivity. The genetic algorithm (GA) shows good results in terms of True Positive Rate (TPR) and False Negative Rate (FNR). As for Rules 11, 9 and 8, they show good results in terms of False Positive Rate (FPR), while PSO shows good results in terms of precision and True Negative Rate (TNR). It was found that the intrusion detection system with fewer features will increase accuracy. The proposed feature selection model for NIDS is rule-based pattern recognition to discover computer network attack which is in the scope of Symmetry journal.

Knowledge-Based Automatic Fault Detection in Flight Control System

2007 ◽  
Author(s):  
C. H. Lo ◽  
Eric H. K. Fung ◽  
Y. K. Wong
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Control System ◽  
Fault Detection ◽  
Flight Control ◽  
Detection System ◽  
Flight Control System ◽  
Actuator Failure ◽  
Fuzzy Genetic Algorithm ◽  
Fuzzy Genetic

There are various possible failures, like, actuator, sensor, or structural, which can occur on a sophisticated modern aircraft. In certain situations the need for an automatic fault detection system provides additional information about the status of the aircraft to assist pilots to compensate for failures. In this paper, we develop an intelligent technique based on fuzzy-genetic algorithm for automatically detecting failures in flight control system. The fuzzy-genetic algorithm is proposed to construct the automatic fault detection system for monitoring aircraft behaviors. Fuzzy system is employed to estimates the times and types of actuator failure. Genetic algorithms are used to generate an optimal fuzzy rule set based on the training data. The optimization capability of genetic algorithms provides and efficient and effective way to generate optimal fuzzy rules. Different types of actuator failure can be detected by the fuzzy-genetic algorithm based automatic fault detection system after tuning its rule table. Simulations with different actuator failures of the non-linear F-16 aircraft model are conducted to appraise the performance of the proposed automatic fault detection system.

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