A hybrid CNN+LSTM-based intrusion detection system for industrial IoT networks

Altunay, Hakan Can; Albayrak, Zafer

doi:10.1016/j.jestch.2022.101322

Cited by 88 publications

(32 citation statements)

References 37 publications

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“…Altunay et al [38] proposed a hybrid CNN+LSTM-based IDS. The UNSW-NB15 dataset was used for training the proposed model, and achieved a 93.21% accuracy rate in binary classification, and while 92.9% accuracy rate for multi-class classification.…”

Section: Related Workmentioning

confidence: 99%

A Novel Two-Stage Deep Learning Model for Network Intrusion Detection: LSTM-AE

et al. 2023

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Machine learning and deep learning techniques are widely used to assess intrusion detection systems (IDS) capable of rapidly and automatically recognizing and classifying cyber-attacks on networks and hosts. However, when destructive attacks are becoming more extensive, more challenges develop, needing a comprehensive response. Numerous intrusion detection datasets are publicly accessible for further analysis by the cybersecurity research community. However, no previous research has examined the performance of the proposed model on a variety of publicly accessible datasets in detail. Due to the dynamic nature of the attack and its rapidly changing attack techniques, the publicly accessible intrusion datasets must be updated and benchmarked regularly. The deep neural network (DNN) and convolutional neural network (CNN) are examined in this article as types of deep learning models for developing a flexible and effective IDS capable of detecting and comparing them with the proposed model in detecting cyber-attacks. The constant development of network behavior and the fast growth of attacks need the development of IDS and the evaluation of many datasets produced over time through static and dynamic methods. This kind of research enables the identification of the most efficient algorithm for identifying future cyber-attacks. We proposed a novel two-stage deep learning technique hybridizing Long-Short Term Memory (LSTM) and Auto-Encoders (AE) for detecting attacks. The CICIDS2017 and CSE-CICDIS2018 datasets are used to determine the optimum network parameters for the proposed LSTM-AE. The experimental results show that the proposed hybrid model works well and is applicable for detecting attacks in modern scenarios.

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Section: Related Workmentioning

confidence: 99%

A Novel Two-Stage Deep Learning Model for Network Intrusion Detection: LSTM-AE

et al. 2023

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“…• False Negative (FN): A fundamentally positive condition predicted as negative by the classifier 44 .…”

Section: Evaluation Measuresmentioning

confidence: 99%

SMS Spam Detection System Based on Deep Learning Architectures in Turkish and English Messages

Albayrak

Altunay

2023

Preprint

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SMS still continues its existence despite the emergence of different messaging services. It takes a part in our lives as a communication service to date. Companies use SMS for advertisement purposes due to the fact that e-mail filtering systems have rooted, short message systems are being undersold by the operators, and spam detection and blocking systems used for short messages are ineffective. Consequently, SMS has attracted the attention of those who send spam to mobile phone users. This article presents the proposal of a hybrid model with the aim of detecting SMS spam messages. This detection model uses GRU and CNN as two deep learning methods. The design for this model was laid out by using two different datasets containing combined text messages written in the Turkish and English languages. The testing process was performed on the dataset through benchmarking as well as other machine learning algorithms. It was revealed in the study that the hybrid CNN+GRU approach attained an accuracy of 99.12% by demonstrating a better performance compared to the other algorithms.

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“…Certain research endeavours have tackled the challenge of estimating a skeletal representation to circumvent the necessity of directly learning interaction patterns from video data in [15]. Recent methodologies leverage Convolutional Neural Network (CNN)-based techniques e.g., Cao [16], Yun [17], Guler [18], Yub [19], Altunay [20] to examine both pose and posture of an individual.…”

Section: Literature Surveymentioning

confidence: 99%

Intelligent Surveillance Framework for Physical Abuse Detection

2023

Preprint

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Physical abuse is a societal problem irrespective of whether the society is rich, educated or so. Mostly physically weaker section like children, women and old age people are most vulnerable to it especially in cases of domestic violence or workplace aggression. If the abuser and victim share a relationship then reporting the abuse becomes all the more challenging. Hence an intelligent surveillance system is the need of the hour. In this paper we propose a hybrid deep learning framework for physical abuse detection and prevention through human action recognition. 3D convolution neural network (CNN) based deep learning model is built to analyze this complex human action. Using transfer learning ResNet-18 and GoogleNet model are trained for publicly available UBI fight and UCF crime dataset for detecting physical abuse in the video. The 2D kernels are converted to 3D kernels for both temporal and spatial feature extraction in the video. A layer of bilinear LSTM is added to gather long term temporal information, so that there is improvement in capturing the human action information. This hybrid model used for physical abuse detection is the novel approach and the results show that the performance parameters such as precision, accuracy, specificity and sensitivity score improved as we moved from 2D kernels to 3D, and there is remarkable improvement when bilinear LSTM was added to the deep learning model.

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A hybrid CNN+LSTM-based intrusion detection system for industrial IoT networks

Cited by 88 publications

References 37 publications

A Novel Two-Stage Deep Learning Model for Network Intrusion Detection: LSTM-AE

A Novel Two-Stage Deep Learning Model for Network Intrusion Detection: LSTM-AE

SMS Spam Detection System Based on Deep Learning Architectures in Turkish and English Messages

Intelligent Surveillance Framework for Physical Abuse Detection

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