Fullmoonfever
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Nice results....bit better that Loihi head to head in the same experiments.
Paper being presented at:
2025 IEEE 68th International Midwest Symposium on Circuits and Systems (MWSCAS)
August 10-13, 2025, Lansing, MI, USA
Paper being presented at:
2025 IEEE 68th International Midwest Symposium on Circuits and Systems (MWSCAS)
August 10-13, 2025, Lansing, MI, USA
| 13:30-13:45, Paper TueLecB04.1 | |
| Ultra-Efficient Network Intrusion Detection Implemented on Spiking Neural Network Hardware (I) | |
| Islam, Rashedul | University of Dayton |
| Yakopcic, Chris | University of Dayton |
| Rahman, Nayim | University of Dayton |
| Alam, Shahanur | University of Dayton |
| Taha, Tarek | University of Dayton |
| Keywords: Neuromorphic System Algorithms and Applications, Machine Learning at the Edge, Other Neural and Neuromorphic Circuits and Systems Topics Abstract: Network intrusion detection is crucial for securing data transmission against cyber threats. Traditional anomaly detection systems use computationally intensive models, with CPUs and GPUs consuming excessive power during training and testing. Such systems are impractical for battery-operated devices and IoT sensors, which require low-power solutions. As energy efficiency becomes a key concern, analyzing network intrusion datasets on low-power hardware is vital. This paper implements a low-power anomaly detection system on Intel’s Loihi and Brainchip’s Akida neuromorphic processors. The model was trained on a CPU, with weights deployed on the processors. Three experiments—binary classification, attack class classification, and attack type classification—are conducted. We achieved approximately 98.1% accuracy on Akida and 94% on Loihi in all experiments while consuming just 3 to 6 microjoules per inference. Also, a comparative analysis with the Raspberry Pi 3 and Asus Tinker Board is performed. To the best of our knowledge, this is the first performance analysis of low power anomaly detection based on spiking neural network hardware. |