A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS

TL;DR

This paper introduces a combined LSTM and SVDD model for anomaly-based NIDS, leveraging temporal sequence learning and data description to improve detection of network intrusions, especially DoS and Probe attacks.

Contribution

It proposes a novel joint training model combining LSTM and SVDD for enhanced anomaly detection in network traffic.

Findings

Achieved 98.0% detection rate for DoS attacks.

Achieved 99.8% detection rate for Probe attacks.

Outperformed existing models on KDD99 dataset.

Abstract

Through continuous observation and modeling of normal behavior in networks, Anomaly-based Network Intrusion Detection System (A-NIDS) offers a way to find possible threats via deviation from the normal model. The analysis of network traffic based on the time series model has the advantage of exploiting the relationship between packages within network traffic and observing trends of behaviors over a period of time. It will generate new sequences with good features that support anomaly detection in network traffic and provide the ability to detect new attacks. Besides, an anomaly detection technique, which focuses on the normal data and aims to build a description of it, will be an effective technique for anomaly detection in imbalanced data. In this paper, we propose a combination model of Long Short Term Memory (LSTM) architecture for processing time series and a data description Support Vector Data Description (SVDD) for anomaly detection in A-NIDS to obtain the advantages of them. This model helps parameters in LSTM and SVDD are jointly trained with the joint optimization method. Our experimental results with KDD99 dataset show that the proposed combined model obtains high performance in intrusion detection, especially DoS and Probe attacks with 98.0% and 99.8%, respectively.