Implementation of Intrusion Detection System using Adaptive Neuro-Fuzzy Inference System for 5G wireless communication network

Intrusion Detection System (IDS) is the nearly all imperative constituent of computer network security. IDSs are designed to comprehend intrusion attempts in incoming network traffic shrewdly. It deals with big volume of data containing immaterial and outmoded features, which lead to delay in training as well as testing procedures. Therefore, to minimize the false alarm and computation complexity, the features selection technique for intrusion detection has been implemented. In this paper PCA (Principal Component Analysis) and Fuzzy Inference System (FIS) have been used on kdd99 dataset to develop FC-NIDS model. PCA is used to select the attacked features to minimize the computational work, while FIS is used to develop a fuzzy inference system for accuracy in prophecy using MATLAB. The results of the experiment are tested on UCI data sets as a standard bench-mark. It has been found efficient for true prediction of intrusion as well as to reduce the false alarm rate. The proposed fuzzy logic controller IDS (FC-NIDS), is passable to covenant with signature and anomaly based attacks to get enhanced intrusion detection, decreases false alarm and to optimize complexity.

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Attack modeling and intrusion detection system for 5G wireless communication network

International Journal of Communication Systems ◽

10.1002/dac.3237 ◽

2016 ◽

Vol 30 (10) ◽

pp. e3237 ◽

Cited By ~ 8

Author(s):

Akhil Gupta ◽

Rakesh Kumar Jha ◽

Sanjeev Jain

Keyword(s):

Communication Network ◽

Wireless Communication ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Attack Modeling ◽

Wireless Communication Network

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Computation of Risk Severity of the Malicious Node using Adaptive Neuro Fuzzy Inference System (ANFIS)

Asian Journal of Engineering and Applied Technology ◽

10.51983/ajeat-2019.8.1.1067 ◽

2019 ◽

Vol 8 (1) ◽

pp. 9-14

Author(s):

R. Dharmarajan ◽

V. Thiagarasu

Keyword(s):

Intrusion Detection ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Detection System ◽

Malicious Nodes ◽

Inference System ◽

Detection Systems ◽

Network Intrusion ◽

Neuro Fuzzy ◽

Network Intrusion Detection Systems

The Intrusion Detection System (IDS) can be employed broadly for safety network. Intrusion Detection Systems (IDSs) are commonly positioned alongside with other protecting safety mechanisms, such as authentication and access control, as a subsequent line of defence that guards data structures. In this paper, Adaptive Neuro Fuzzy Inference System has utilized to predict the risk severity of the malicious nodes found the previous classification phase.

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Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection

Sensors ◽

10.3390/s19040943 ◽

2019 ◽

Vol 19 (4) ◽

pp. 943 ◽

Cited By ~ 12

Author(s):

Sadegh Arefnezhad ◽

Sajjad Samiee ◽

Arno Eichberger ◽

Ali Nahvi

Keyword(s):

Feature Selection ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Detection System ◽

Steering Wheel ◽

Support Vector ◽

Inference System ◽

Drowsiness Detection ◽

Driver Drowsiness ◽

Neuro Fuzzy

This paper presents a novel feature selection method to design a non-invasive driver drowsiness detection system based on steering wheel data. The proposed feature selector can select the most related features to the drowsiness level to improve the classification accuracy. This method is based on the combination of the filter and wrapper feature selection algorithms using adaptive neuro-fuzzy inference system (ANFIS). In this method firstly, four different filter indexes are applied on extracted features from steering wheel data. After that, output values of each filter index are imported as inputs to a fuzzy inference system to determine the importance degree of each feature and select the most important features. Then, the selected features are imported to a support vector machine (SVM) for binary classification to classify the driving conditions in two classes of drowsy and awake. Finally, the classifier accuracy is exploited to adjust parameters of an adaptive fuzzy system using a particle swarm optimization (PSO) algorithm. The experimental data were collected from about 20.5 h of driving in the simulator. The results show that the drowsiness detection system is working with a high accuracy and also confirm that this method is more accurate than the recent available algorithms.

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