An Efficient Intrusion Detection Model Based on Recurrent Neural Network

Rajasekar, Vani; Sarika, S.; Velliangiri, S.; S, Iwin Thanakumar Joseph; Kalaivani, K S

doi:10.1109/icdcece53908.2022.9793016

Cited by 3 publications

(1 citation statement)

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“…The "output gate" determines the current output value and oversees the weighting of the output [30]. LSTMs have been applied to IDS in [45][46][47][48][49]. The LSTMbased IDS has shown satisfactory results on various data sets.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

A Survey of Deep Learning Technologies for Intrusion Detection in Internet of Things

Liao,

Murah,

Hasan

et al. 2024

IEEE Access

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Internet of Things (IoT) is transforming how we live and work and its applications are widespread, spanning smart homes, industrial monitoring, smart cities, healthcare, agriculture, and retail Considering its wide range applications, it is vital to address the security challenges arising from the massive collection and transmission of user data by IoT devices. Intrusion detection systems (IDS) based on deep learning techniques offer new means and research directions for resolving IoT security issues. Deep learning models can process large volumes of data and extract complex patterns, making them generally more effective than traditional rule based IDSs. While deep learning techniques are gradually gaining popularity in IDS applications, current research lacks a comprehensive summary of deep learning-based IDS in the context of IoT. This paper provides an introduction to intrusion detection technologies, followed by a detailed comparison, analysis, and discussion of deep learning models, datasets, feature extraction and classifiers, data preprocessing techniques, and experimental design of the models. It also highlights the current challenges and issues associated with deep learning models and relevant techniques for IDS. Finally, it concludes by providing recommendations to assist researchers in this domain.

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