Anomalous Network Traffic Detection Method Based on an Elevated Harris Hawks Optimization Method and Gated Recurrent Unit Classifier

Xiao, Yao; Kang, Chunying; Yu, Hui; Fan, Tao; Zhang, Haofang

doi:10.3390/s22197548

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…Proposed WCGAN-GP [36] WAE-DNN [37] EHHO + GRU [39] Custom CNN + LSTM [43] IG-CS-PSO + RF [45] ABC-BWO-CONV-LSTM [40] RF-RFE + ensemble [44] FOA + ensemble method [42] Bagging BGM [ As the reach of IoT networks grows, the need for efficient IDS like the one presented in this study becomes increasingly critical. Future research efforts will focus on balancing attack category distributions in datasets.…”

Section: Discussionmentioning

confidence: 99%

A Novel Hybrid Feature Selection with Cascaded LSTM: Enhancing Security in IoT Networks

Sundaram,

Natarajan,

Perumalsamy

et al. 2024

Wireless Communications and Mobile Computing

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The rapid growth of the Internet of Things (IoT) has created a situation where a huge amount of sensitive data is constantly being created and sent through many devices, making data security a top priority. In the complex network of IoT, detecting intrusions becomes a key part of strengthening security. Since IoT environments can be easily affected by a wide range of cyber threats, intrusion detection systems (IDS) are crucial for quickly finding and dealing with potential intrusions as they happen. IDS datasets can have a wide range of features, from just a few to several hundreds or even thousands. Managing such large datasets is a big challenge, requiring a lot of computer power and leading to long processing times. To build an efficient IDS, this article introduces a combined feature selection strategy using recursive feature elimination and information gain. Then, a cascaded long–short-term memory is used to improve attack classifications. This method achieved an accuracy of 98.96% and 99.30% on the NSL-KDD and UNSW-NB15 datasets, respectively, for performing binary classification. This research provides a practical strategy for improving the effectiveness and accuracy of intrusion detection in IoT networks.

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Section: Discussionmentioning

confidence: 99%

A Novel Hybrid Feature Selection with Cascaded LSTM: Enhancing Security in IoT Networks

Sundaram,

Natarajan,

Perumalsamy

et al. 2024

Wireless Communications and Mobile Computing

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Hybrid intrusion detection models based on GWO optimized deep learning

Elsaid,

Shehab,

Mattar

et al. 2024

Discov Appl Sci

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In the rapidly evolving landscape of network communication systems, the need for robust security measures has become paramount due to increased vulnerability to cyber threats. Traditional Intrusion Detection Systems (IDSs) face challenges in efficiently handling redundant features, leading to increased computational complexity. This research addresses these challenges by proposing two optimized IDSs leveraging Grey Wolf Optimization (GWO) combined with deep learning (DL) models. The first system integrates Gated Recurrent Unit (GRU) with GWO (GRU-GWO), while the second utilizes Long Short-Term Memory (LSTM) with GWO (LSTM-GWO). These systems aim to enhance feature selection, reducing dimensionality and improving detection accuracy. The NSL-KDD and UNSW-NB15 datasets, representative of contemporary network environments, were employed to evaluate the proposed systems. Experimental results demonstrate significant improvements in intrusion detection accuracy and computational efficiency, underscoring the efficacy of the DL-GWO approach in enhancing network security. The first approach (GRU-GWO-FS) increased accuracy to 90% and 79% for anomaly and signature-based detection on the UNSW-NB15 dataset, compared to 80% and 77% with all features. The second approach (LSTM-GWO-FS) achieved 93% and 79%, compared to 82% and 77%. On the NSL-KDD dataset, GRU-GWO-FS improved accuracy to 94% and 92%, and LSTM-GWO-FS to 94% and 92% for anomaly and signature-based detection, respectively.

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Network traffic anomaly detection method based on chaotic neural network

Sheng

Xin

2023

Alexandria Engineering Journal

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Anomalous Network Traffic Detection Method Based on an Elevated Harris Hawks Optimization Method and Gated Recurrent Unit Classifier

Cited by 5 publications

References 23 publications

A Novel Hybrid Feature Selection with Cascaded LSTM: Enhancing Security in IoT Networks

A Novel Hybrid Feature Selection with Cascaded LSTM: Enhancing Security in IoT Networks

Hybrid intrusion detection models based on GWO optimized deep learning

Network traffic anomaly detection method based on chaotic neural network

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