The sound of intrusion: A novel network intrusion detection system

Al-Darwbi, Mohammed Y.; Lashkari, Arash Habibi; Ghorbani, Ali A.

doi:10.1016/j.compeleceng.2022.108455

Cited by 17 publications

(4 citation statements)

References 14 publications

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“…Security of communication can be provided by using various methods including access control [53], optimization-based secure routing techniques [54], agent-based methods [55], temporal analysis [56], intrusion detection techniques developed for feature selection and classifcation, key management techniques, encryption and decryption methods, trust management techniques, frewalls, and application considerations [57][58][59][60][61][62][63][64][65]. Various authors have proposed a variety of mechanisms for providing security in IoT environments through IDS.…”

Section: Literature Surveymentioning

confidence: 99%

A Comprehensive Survey on Machine Learning‐Based Intrusion Detection Systems for Secure Communication in Internet of Things

Kumar

Selvi

Kannan

2023

Computational Intelligence and Neuroscience

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The Internet of Things (IoT) is a distributed system which is made up of the connections of smart objects (things) that can continuously sense the events in their sensing domain and transmit the data via the Internet. IoT is considered as the next revolution of the Internet since it has provided vast improvements in day-to-day activities of humans including the provision of efficient healthcare services and development of smart cities and intelligent transport systems. The IoT environment, by the application of suitable security mechanisms through efficient security management techniques, intrusion detection systems provide a wall of defence against the attacks on the Internet and on the devices connected with Internet by effective monitoring of the Internet traffic. Therefore, the intrusion detection system (IDS) is a resolution proposed by the researchers to monitor and secure the IoT communication. In this work, a meticulous analysis of the security of IoT networks based on quality-of-service metrics is performed for deploying intrusion detection systems by carrying out experiments on secured communication and measuring the network’s performance based on comparing them with the existing security metrics. Finally, we propose a new and effective IDS using a deep learning-based classification approach, namely, fuzzy CNN, for improving the security of communication. The major and foremost advantages of this system include an upsurge in detection accuracy, the accurate detection of denial of service (DoS) attacks more efficiently, and the reduction of false positive rates.

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Section: Literature Surveymentioning

confidence: 99%

A Comprehensive Survey on Machine Learning‐Based Intrusion Detection Systems for Secure Communication in Internet of Things

Kumar

Selvi

Kannan

2023

Computational Intelligence and Neuroscience

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“…𝐶𝐸(𝑝, 𝑦) = 𝐶𝐸(𝑝) = − log(𝑝) (13) In intrusion detection datasets, there is an imbalance in the different categories of data, with normal traffic samples being much more abundant than other types of attack samples. Negative samples account for the dominant part of the total loss.…”

Section: E Loss Functionmentioning

confidence: 99%

“…In contrast, the convolution module [13] uses deformable convolution to enlarge the receptive field of each patch without increasing the convolution kernel. The model uses a hierarchical focal loss function to assign weights to samples with different categories [14].…”

Section: Introductionmentioning

confidence: 99%

Network Intrusion Detection Based on Feature Image and Deformable Vision Transformer Classification

He,

Zhang,

Zong

et al. 2024

IEEE Access

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Network intrusion detection technology has always been an indispensable protection mechanism for industrial network security. The rise of new forms of network attacks has resulted in a heightened demand for these technologies. Nevertheless, the current models' effectiveness is subpar. We propose a new Deformable Vision Transformer (DE-VIT) method to address this issue. DE-VIT introduces a new deformable attention mechanism module, where the positions of key-value pairs in the attention mechanism are selected in a data-dependent manner, allowing it to focus on relevant areas, capture more informative features, and avoid excessive memory and computational costs. In addition to using deformable convolutions instead of regular convolutions in embedding layers to enhance the receptive field of patches, a sliding window mechanism is also employed to utilize edge information fully. In Parallel, we use a layered focal loss function to improve classification performance and address data imbalance issues. In summary, DE-VIT reduces computational complexity and achieves better results. We conduct experimental simulations on the public intrusion detection datasets, and the accuracy of the enhanced intrusion detection model surpasses that of the Deep Belief Network with Improved Kernel-Based Extreme Learning (DBN-KELM). It reaches 99.5% and 97.5% on the CIC IDS2017 and UNSW-NB15 datasets, exhibiting an increase of 8.5% and 9.1%, respectively.INDEX TERMS Network intrusion detection, deformable vision transformer, deformable convolution, deformable attention mechanism, vision transformer.

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“…However, no new methods have been proposed that can effectively improve NID performance. By treating network traffic as vibrations, waves, or sounds, Aldarwbi et al (2022) transformed network traffic features into waveforms, and based on this, implemented NID using deep learning technology based on audio/speech recognition, proposing a new NID system called "Voice of Intrusion." In response to the problem of signature recognition, Chellammal et al (2023) proposed a deep learning based NID model by combining multiple hidden processing layers in the network to form a multi-layer deep learning network.…”

Section: Nidmentioning

confidence: 99%

A Network Intrusion Detection Method for Various Information Systems Based on Federated and Deep Learning

Zhou,

Shi

2024

International Journal on Semantic Web and Information Systems

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Under the premise of ensuring data privacy, traditional network intrusion detection (NID) methods cannot achieve high accuracy for different types of intrusions. A NID method combining transformer and federated learning (FedL) is proposed for this purpose. First, a multi-party collaborative learning framework was built based on FedL, which achieved data exchange and sharing. Then, by introducing the self-attention mechanism (AttM) to improve the traditional transformer, it could quickly converge. Finally, an NID model integrating transformer and FedL was constructed by combining DNN, GRU, and an encoder module composed of improved transformer, achieving accurate detection of network intrusion. The proposed NID method was compared with the other three methods. The results show that the proposed method has the highest NID accuracy and F1 score on the NSL-KDD and UNSW-NB15 dataset, with the highest accuracy reaching 99.65% and 89.25%, while the F1 score has the highest accuracy, reaching 99.45% and 88.13%, outperforming the other three comparative algorithms in terms of performance.

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The sound of intrusion: A novel network intrusion detection system

Cited by 17 publications

References 14 publications

A Comprehensive Survey on Machine Learning‐Based Intrusion Detection Systems for Secure Communication in Internet of Things

A Comprehensive Survey on Machine Learning‐Based Intrusion Detection Systems for Secure Communication in Internet of Things

Network Intrusion Detection Based on Feature Image and Deformable Vision Transformer Classification

A Network Intrusion Detection Method for Various Information Systems Based on Federated and Deep Learning

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