Abstract—We present BrainShield, the first operational In-trusion Detection System (IDS) fully grounded in the whole-brain neuronal connectivity map (connectome) of Drosophila melanogaster. Unlike conventional Deep Neural Networks (DNNs)that require massive labeled datasets and megawatts of power,BrainShield leverages biologically evolved architectural priorsto perform sparse, high-dimensional pattern recognition. Thesystem employs 9,987 Izhikevich spiking neurons across 64anatomically accurate populations spanning 12 neuropil regions,utilizing neurotransmitter-specific synaptic connectivity deriveddirectly from the FlyWire and Hemibrain connectome datasets.To solve the problem of false-negative signal dilution in highlyconcentrated threat vectors (such as SYN Floods), we introduce Top-4 Attentive Aggregation—a novel anomaly scoring mecha-nism mathematically proven to have O(1) computational com-plexity. To prove viability in production environments, the system was implemented as a high-speed Linux daemon with zero-overhead BPF packet capturing, automated Intrusion Prevention System (IPS) integrations, and a live web dashboard. A rigorousexperimental evaluation was conducted on the raw CIC-IDS2017 Friday-WorkingHours.pcap (8.4GB, 6 million packets). Brain-Shield processed the raw packet streams at ∼76,000 pkts/sec on a standard CPU, correctly classifying 53,878 attack windowswith 99.9% precision and 100% recall of dominant DDoS andPortScan bursts. In simulated environments, it achieves a 100%F1-score across 5 benchmark attack categories while maintaining100% adversarial evasion detection. Furthermore, a hardwareenergy profiling study estimates an energy consumption of 0.004μJ per inference window on Intel Loihi 2 silicon—representing a10,590,000× energy reduction over GPU-accelerated LSTMs, andenabling data-center-wide 128-segment monitoring on a single6mW neuromorphic chip.
Mohammed Saddam Ruwayd (2026) studied this question.