Designing of Fuzzy Logic-Based Intrusion Detection System (FIDS) for Detection of Blackhole Attack in AODV for MANETs

2021 ◽  
pp. 113-128
Author(s):  
Ruchi Makani ◽  
B. V. R. Reddy
Keyword(s):  
Fuzzy Logic ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Blackhole Attack

scholarly journals Intelligent Intrusion Detection System Through Combined and Optimized Machine Learning

2018 ◽  
Vol 17 (02) ◽  
pp. 1850007 ◽  
Author(s):  
Syed Ali Raza Shah ◽  
Biju Issac ◽  
Seibu Mary Jacob
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Fuzzy Logic ◽  
Intrusion Detection ◽  
Open Source Software ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Rule Based

In this paper, an existing rule-based intrusion detection system (IDS) is made more intelligent through the application of machine learning. Snort was chosen as it is an open source software and though it was performing well, it showed false positives (FPs). To find the best performing machine learning algorithms (MLAs) to use with Snort so as to improve its detection, we tested some algorithms on three available datasets. Support vector machine (SVM) was chosen along with fuzzy logic and decision tree based on their accuracy. Combined versions of algorithms through ensemble SVM along with other variants were tried on the generated traffic of normal and malicious packets at 10[Formula: see text]Gbps. Optimized versions of the SVM along with firefly and ant colony optimization (ACO) were also tried, and the accuracy improved remarkably. Thus, the application of combined and optimized MLAs to Snort at 10[Formula: see text]Gbps worked quite well.

Network Traffic Intrusion Detection System Using Fuzzy Logic and Neural Network

2017 ◽  
Vol 8 (1) ◽  
pp. 1-17
Author(s):  
Mrudul Dixit ◽  
Rajashwini Ukarande
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
True Positive Rate ◽  
Attack Detection ◽  
Good Precision ◽  
True Negative ◽  
Fuzzy Neural

Intrusion Detection System (IDS) are actively used to identify any unusual activities in a network. To improve the effectiveness of IDS, security experts have embedded their extensive knowledge with the use of fuzzy logic, neuro-fuzzy, neural network and other such AI techniques. This article presents an intrusion detection system in network based on fuzzy logic and neural network. The proposed system is evaluated using the KDD Cup 99 dataset. The fuzzy system detects the intrusion behavior of the network using the defined set of rules. Whereas neural network trains the network based on the input and uses the trained system to predict the output. The evaluation depicts the effectiveness of the selected method in terms of selection of attributes which gives high True Positive Rate and True Negative Rate, with good precision in attack detection.

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