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Ferrag, M.A., Maglaras, L., Moschoyiannis, S. and Janicke, H. (2020) Deep Learning for Cyber Security Intrusion Detection: Approaches, Datasets, and Comparative Study. Journal of Information Security and Applications, 50, Article ID: 102419.
https://doi.org/10.1016/j.jisa.2019.102419

has been cited by the following article:

• TITLE: Performance Evaluation of a Genetic Neuro-Fuzzy Intrusion Detection System across Multiple Datasets

  AUTHORS: Mohammad Hamdan, Mohammed Assora, Mustapha Dakkak

  KEYWORDS: Network Security, Intrusion Detection Systems, Neural Networks, Fuzzy Logic, Genetic Algorithm

  JOURNAL NAME: Journal of Information Security, Vol.17 No.3, June 9, 2026

  ABSTRACT: The paper introduces an IDS that combines a genetic-algorithm feature selector with an Adaptive Neuro-Fuzzy Inference System classifier. A genetic algorithm, one of the most prominent heuristic optimization methods, is utilized to select a set of optimal features to serve as inputs to the IDS. The performance of this hybrid approach is rigorously compared with the widely adopted open-source Snort system using several standard benchmark datasets, including KDDCup99, NSL-KDD, UNSW-NB15, Bot-IoT, and CSE-CIC-IDS2018. The primary objective is to create a system capable of learning and detecting previously unknown attacks by harnessing the strengths of neural networks and fuzzy logic, thereby minimizing erroneous classifications—whether considering benign data as malicious or vice versa. The model is trained and tested on five public datasets and benchmarked against Snort. Across all datasets the GA-ANFIS variant attains higher accuracy (≈99%) and markedly lower false-positive rates (