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

• Published in: IEEE access Vol. 8; pp. 215202 - 215213
• Main Authors: Moghanian, Shadi, Saravi, Farshid Bagheri, Javidi, Giti, Sheybani, Ehsan O.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3040740

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.