Name
Title | ||
|---|---|---|
Memristor Based Autoencoder for Unsupervised Real-Time Network Intrusion and Anomaly Detection |
Abstract | ||
|---|---|---|
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.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3354265.3354267 | Proceedings of the International Conference on Neuromorphic Systems |
Keywords | Field | DocType |
Autoencoder, Memristor, NSL-KDD, Neuromorphic | Anomaly detection,Memristor,Autoencoder,Intrusion,Pattern recognition,Computer science,Real time networks,Control engineering,Artificial intelligence | Conference |
ISBN | Citations | PageRank |
978-1-4503-7680-8 | 2 | 0.37 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Md. Shahanur Alam | 1 | 2 | 0.71 |
| B. Rasitha Fernando | 2 | 2 | 0.37 |
| Yassine Jaoudi | 3 | 2 | 0.71 |
| Chris Yakopcic | 4 | 140 | 13.10 |
| Raqibul Hasan | 5 | 76 | 8.74 |
| Tarek M. Taha | 6 | 280 | 32.89 |
| Guru Subramanyam | 7 | 2 | 0.37 |