Convolutional Neural Networks with LSTM for Intrusion Detection

Mostofa Ahsan, Kendall E. Nygard

2020EPiC series in computing54 citationsDOI Open Access PDF

Abstract

A variety of attacks are regularly attempted at network infrastructure. With the increasing development of artificial intelligence algorithms, it has become effective to prevent network intrusion for more than two decades. Deep learning methods can achieve high accuracy with a low false alarm rate to detect network intrusions. A novel approach using a hybrid algorithm of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) is introduced in this paper to provide improved intrusion detection. This bidirectional algorithm showed the highest known accuracy of 99.70% on a standard dataset known as NSL KDD. The performance of this algorithm is measured using precision, false positive, F1 score, and recall which found promising for deployment on live network infrastructure.

Topics & Concepts

Computer scienceConvolutional neural networkIntrusion detection systemArtificial intelligenceDeep learningSoftware deploymentMachine learningRecurrent neural networkFalse positive rateLong short term memoryArtificial neural networkData miningPattern recognition (psychology)Operating systemNetwork Security and Intrusion DetectionAnomaly Detection Techniques and ApplicationsInternet Traffic Analysis and Secure E-voting