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Intrusion detection systems (IDS) are commonly utilized to prevent cyber-attacks. With the wide proliferation of network connected devices, running IDS algorithms on all devices (including mobile devices) within a network can help bolster security. However, the cost of running IDS algorithms on all networked devices can be high in terms of power and physical resources (especially battery operated ones). Several recent studies have proposed mapping applications to neural network form and then running these on specialized neural network accelerators [1, 2] to reduce power consumption. Neural accelerators can result in power reduction from about 2 times to several thousand times compared to RISC processors . Hence utilizing these neural network accelerators can enabling the deployment of IDS algorithms across all devices in a network.
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- Low Power Neuromorphic Architectures to Enable Pervasive Deployment of Intrusion Detection Systems
Tarek M. Taha
Mark R. McLean
- Chapter 10
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