LSTM for Anomaly-Based Network Intrusion Detection

被引：0
|
作者：
Althubiti, Sara A. [1 ]
Jones, Eric Marcell, Jr. [1 ]
Roy, Kaushik [1 ]
机构：
[1] North Carolina A&T State Univ, Dept Comp Sci, Greensboro, NC 27411 USA
来源：
2018 28TH INTERNATIONAL TELECOMMUNICATION NETWORKS AND APPLICATIONS CONFERENCE (ITNAC) | 2018年
基金：
美国国家科学基金会;
关键词：
intrusion detection system; anomaly detection; long short-term memory;
D O I：
暂无
中图分类号：
TN [电子技术、通信技术];
学科分类号：
0809 ;
摘要：
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.
引用
收藏
页码：293 / 295
页数：3
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