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- W2897792541 abstract "Custom low power hardware for network intrusion detection is in great demand, as this would allow for efficient security in battery-powered network devices. This paper presents a memristor crossbar based Multilayer Perceptron (MLP) capable of performing network intrusion detection. The MLP is trained based on the Knowledge Discovery and Datamining (KDD) dataset. Once the network is trained, the weight values are programmed into a simulated memristor crossbar hardware system to show that custom circuitry can be designed to perform on-chip intrusion detection. We show the proposed memristor system is capable of achieving greater than 99% intrusion detection accuracy if the memristors utilized are able to store at least 4 unique values (essentially acting as 2-bit devices). Furthermore, we present a number of studies that quantify the amount of classification accuracy lost due to analog processing and amplification in the signal path. This circuit is capable of tolerating significant amplifier gain error (up to 30%) before classification accuracy drops below 99%." @default.
- W2897792541 created "2018-10-26" @default.
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- W2897792541 date "2018-07-01" @default.
- W2897792541 modified "2023-10-14" @default.
- W2897792541 title "Analysis and Design of Memristor Crossbar Based Neuromorphic Intrusion Detection Hardware" @default.
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- W2897792541 doi "https://doi.org/10.1109/ijcnn.2018.8489252" @default.
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