Memristor Based Autoencoder for Unsupervised Real-Time Network Intrusion and Anomaly Detection
| Autor: | Yassine Jaoudi, B. Rasitha Fernando, Md. Shahanur Alam, Tarek M. Taha, Guru Subramanyam, Raqibul Hasan, Chris Yakopcic |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Network security
business.industry Computer science 020206 networking & telecommunications Pattern recognition 02 engineering and technology Memristor Intrusion detection system Autoencoder Networking hardware law.invention Electric power system Neuromorphic engineering law 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Anomaly detection Artificial intelligence business |
| Zdroj: | ICONS |
| DOI: | 10.1145/3354265.3354267 |
| Popis: | Custom low power hardware for real-time network security and anomaly detection are in great demand, as these would allow for efficient security in battery-powered network devices. This paper presents a memristor based system for real-time intrusion detection, as well as an anomaly detection based on autoencoders. Intrusion detection is based on a single autoencoder, and the overall detection accuracy of this system is 92.91% with a malicious packet detection accuracy of 98.89%. The system described in this paper is also capable of using two autoencoders to perform anomaly detection using real-time online learning. Using this system, we show that anomalous data is flagged by the system, but over time the system stops flagging a particular datatype if its presence is abundant. Utilizing memristors in these designs allows us to present extreme low power systems for intrusion and anomaly detection, while sacrificing little accuracy. |
| Databáze: | OpenAIRE |
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