GOAMLP: Network Intrusion Detection With Multilayer Perceptron and Grasshopper Optimization Algorithm

In this paper, an intrusion detection system is introduced that uses data mining and machine learning concepts to detect network intrusion patterns. In the proposed method, an artificial neural network (ANN) is used as a learning technique in intrusion detection. The metaheuristic algorithm with the...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.215202-215213
Main Authors: Moghanian, Shadi, Saravi, Farshid Bagheri, Javidi, Giti, Sheybani, Ehsan O.
Format: Article
Language:English
Subjects:
Algorithms
artificial neural network
Artificial neural networks
Classification algorithms
Computer hacking
Data mining
Error detection
Genetic algorithms
Heuristic methods
Intrusion detection systems
Learning theory
Machine learning
Machine learning algorithms
Mathematical model
multilayer perceptron
Multilayer perceptrons
Network intrusion detection
Neural networks
Optimization
Optimization algorithms
Service introduction
swarm-based algorithm
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Summary:In this paper, an intrusion detection system is introduced that uses data mining and machine learning concepts to detect network intrusion patterns. In the proposed method, an artificial neural network (ANN) is used as a learning technique in intrusion detection. The metaheuristic algorithm with the swarm-based approach is used to reduce intrusion detection errors. In the proposed method, the Grasshopper Optimization Algorithm (GOA) is used for better and more accurate learning of ANNs to reduce intrusion detection error rate. The role of the GOAMLP algorithm is to minimize the intrusion detection error in the neural network by selecting useful parameters such as weight and bias. Our implementation in MATLAB software and using the KDD and UNSW datasets show that the proposed method detects abnormal, malicious traffic and attacks with high accuracy. The GOAMLP method outperforms and is more accurate than the existing state-of-the-art techniques such as RF, XGBoost, and embedded learning of ANN with BOA, HHO, and BWO algorithms in network intrusion detection.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3040740