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LSTM for Anomaly-Based Network Intrusion Detection
Conference proceeding

LSTM for Anomaly-Based Network Intrusion Detection

Sara A. Althubiti, Eric Marcell Jones, Kaushik Roy and IEEE
2018 28TH INTERNATIONAL TELECOMMUNICATION NETWORKS AND APPLICATIONS CONFERENCE (ITNAC), pp.293-295
01/01/2018

Abstract

Science & Technology Technology Telecommunications
Due to the massive amount of the network traffic, attackers have a great chance to cause a huge damage to the network system or its users. Intrusion detection plays an important role in ensuring security for the system by detecting the attacks and the malicious activities. In this paper, we utilize CIDDS dataset and apply a deep learning approach, Long-Short-Term Memory (LSTM), to implement intrusion detection system. This research achieves a reasonable accuracy of 0.85.

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