Network Intrusion Detection Based on Novel Feature Selection Model and Various Recurrent Neural Networks
Source abstract
Abstract
The recent increase in hacks and computer network attacks around the world has intensified the need to develop better intrusion detection and prevention systems. The intrusion detection system (IDS) plays a vital role in detecting anomalies and attacks on the network which have become larger and more pervasive in nature. However, most anomaly-based intrusion detection systems are plagued by high false positives. Furthermore, Remote-to-Local (R2L) and User-to-Root (U2R) are two kinds of attack which have low predicted accuracy scores in advance IDS methods. Therefore, this paper proposes a novel IDS framework to overcome these IDS problems. The proposed framework including three main parts. The first part is to build SFSDT model which is the feature selection model. SFSDT is to generate the best feature subset from the original feature set. This model is a hybrid Sequence Forward Selection (SFS) algorithm and Decision Tree (DT) model. The second part is to build various IDS models to train on the best-selected feature subset. The various Recurrent Neural Networks (RNN) are traditional RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Two IDS datasets are used for the learned models in experiments including NSL-KDD in 2010 and ISCX in 2012. The final part is to evaluate the proposed model by comparing the proposed models to other IDS models. The experimental results show the proposed models achieve significantly improved accuracy detection rate as well as attack types classification. Furthermore, this approach can reduce the computation time by memory profilers measurement.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection · Internet Traffic Analysis and Secure E-voting · Advanced Malware Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Engineering, Pusan National University, Busan 609-735, Korea
Information Technology Faculty, Hung Yen University of Technology and Education, Hung Yen 16000, Vietnam
References (46)
Show source-provided references
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