BrainShield: A Neuromorphic Intrusion Detection System Inspired by the Drosophila melanogaster Whole-Brain Connectome
Year: 2026
Type: article
Abstract: —This paper presents BrainShield, an Intrusion De- tection System whose architecture is derived from the whole- brain connectome of Drosophila melanogaster. The system models 9,987 Izhikevich spiking neurons across 64 populations in 12 neuropil regions. We propose Top-4 Attentive Aggregation, a scoring mechanism that selects the four most anomalous features per analysis window to resist signal dilution in concentrated attacks. The system was evaluated on raw PCAP traffic from three days of the CIC-IDS2017 dataset (over 22 million packets), achieving 99.9% precision on volumetric attacks and successfully flagging brute-force campaigns that conventional threshold-based detectors missed. Analytical energy estimates suggest the topology could run at roughly 0.6 nJ per inference window on Intel Loihi 2. Index Terms—Neuromorphic Computing, Spiking Neural Networks, Intrusion Detection, Drosophila Connectome, Intel Loihi 2, Adversarial Robustness, Energy-Efficient AI
Author: Ruwayd, Mohammed Saddam
Institution: University of Baghdad
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7135064068
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