Abstract
In a network architecture, an intrusion detection system (IDS) is one of the most commonly used approaches to secure the integrity and availability of critical assets in protected systems. Many existing network intrusion detection systems (NIDS) utilize stand-alone classifier models to classify network traffic as an attack or as normal. Due to the vast data volume, these stand-alone models struggle to reach higher intrusion detection rates with low false alarm rates( FAR). Additionally, irrelevant features in datasets can also increase the running time required to develop a model. However, data can be reduced effectively to an optimal feature set without information loss by employing a dimensionality reduction method, which a classification model then uses for accurate predictions of the various network intrusions. In this study, we propose a novel feature-driven intrusion detection system, namely -BidLSTM, that integrates a statistical model and bidirectional long short-term memory (BidLSTM). The NSL-KDD dataset is used to train and evaluate the proposed approach. In the first phase, the -BidLSTM system uses a model to rank all the features, then searches an optimal subset using a forward best search algorithm. In next phase, the optimal set is fed to the BidLSTM model for classification purposes. The experimental results indicate that our proposed -BidLSTM approach achieves a detection accuracy of 95.62% and an F-score of 95.65%, with a low FAR of 2.11% on NSL-KDDTest+. Furthermore, our model obtains an accuracy of 89.55%, an F-score of 89.77%, and an FAR of 2.71% on NSL-KDDTest−21, indicating the superiority of the proposed approach over the standard LSTM method and other existing feature-selection-based NIDS methods.
Highlights
In present-day society, various organizations and individuals have become more and more reliant upon information and communication technology (ICT), due to the increasing number of useful technologies
To investigate the proposed method’s robustness, we evaluated the model’s performance using different metrics such as accuracy, precision, F-score, and false alarm rate (FAR)
All experiments and evaluations were carried out using a personal computer (PC) running on the Windows 10 Operating System (OS), with the following specifications: Intel Core i5-9300H CPU, 8GB Random Access Memory (RAM), NVIDIA GeForce GTX 1050, and a 4GB dedicated GDDR5 VRAM
Summary
In present-day society, various organizations and individuals have become more and more reliant upon information and communication technology (ICT), due to the increasing number of useful technologies. Intrusion detection systems (IDSs) have proven essential in the security domain and play a vital role in detecting different types of malicious behaviors and attacks. Hybrid detection is a fusion of anomaly and misuse detection methods used to identify malicious activities [2,10,11]. Researchers have proposed several different intrusion detection systems over the past few decades using machine learning, deep learning, and other statistical methods. In recent times, machine learning and deep learning techniques have gained more attention in many different research areas, including intrusion detection [12]. They have become the most commonly adopted approaches for many intrusion detection systems (IDS)
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