International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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FFO-ANN: Firefly-Optimized Artificial Neural Network for High-Accuracy Intrusion Detection

Author(s) Gavaskar Vincent
Country India
Abstract Intrusion Detection Systems (IDS) play a critical role in protecting modern network infrastructures against a range of evolving threats. This work develops a sophisticated deep learning-based intrusion detection system that uses Firefly optimized Artificial Neural Network (FFO-ANN) for accurate attack classification. First, raw traffic data is pre-processed, this includes classifying category variables as data encoding and numerical variables as z-score normalization, to normalize feature scaling so that no one feature is to be more prominent than the others. For feature selection, Exploratory Data Analysis (EDA) is conducted to extract relevant information. The FFO-ANN model is then able to classify the best possible accuracy, dynamically adapting to network patterns (training without the computational complexity), but is still able to increase accuracy. The FFO-ANN model produced more realistic intrusion detection capability, with an improvement in accuracy of 0.964, precision of 0.963, recall of 0.962, FI-Score of 0.9625 and specificity of 0.9655, compared to traditional models developed through python. The results provided evidence that the proposed model is a practical application for an intrusion detection system for cybersecurity.
Keywords Intrusion Detection System, Data preprocessing, FFO-ANN, Exploratory Data Analysis
Field Engineering
Published In Volume 8, Issue 1, January-February 2026
Published On 2026-01-12
DOI https://doi.org/10.36948/ijfmr.2026.v08i01.65725
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E-ISSN 2582-2160
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