Enhancing Intrusion Detection Using MAG: A Shallow Multilayer Perceptron Tuned with Adam and Grey Wolf Optimization

Enhancing Intrusion Detection Using MAG: A Shallow Multilayer Perceptron Tuned with Adam and Grey Wolf Optimization | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhancing Intrusion Detection Using MAG: A Shallow Multilayer Perceptron Tuned with Adam and Grey Wolf Optimization Iman Farhadian Dehkordi, Kooroush Manochehri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9463244/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study introduces MAG, a lightweight intrusion detection system (IDS) that couples a single-hidden-layer multilayer perceptron (MLP) with the Adam optimizer and a Grey Wolf Optimizer (GWO) for hyperparameter tuning. A Classification and Regression Tree (CART) stage in WEKA first derives compact five-feature subsets for the KDDCup99 and NSL-KDD benchmarks, enabling multiclass and binary evaluation with reduced input dimensionality. The leakage-free pipeline combines target encoding, capped Synthetic Minority Over-sampling Technique (SMOTE) resampling to address class imbalance, and z-score normalization within a repeated nested cross-validation (CV) protocol. Across 25 runs, MAG consistently outperforms plain MLP and its Adam- and GWO-augmented variants in macro-averaged F1 score (F1), while using only 122 parameters and achieving sub-millisecond CPU inference with a negligible memory footprint. A nonparametric Friedman test over four dataset–task combinations confirms that MAG attains a significantly better overall ranking than the competing models, supporting its suitability for cost-aware IDS deployment. Intrusion Detection Systems (IDS) Multilayer Perceptron (MLP) Grey Wolf Optimizer (GWO) Feature Selection KDDCup99 NSL-KDD Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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