Intrusion detection in Cloud Internet of Things environment

Rebbah, Mohammed; Rebbah, Dhiaa El Hak; Smail, Omar

doi:10.1109/mathit.2017.8259697

Cited by 11 publications

(3 citation statements)

References 4 publications

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“…e first is due to the structural characteristics of the industrial Internet of ings, the second is the non-technical penetration based on social engineering, and the third is the external network risk brought by the combination with the Internet. In short, there are several reasons for the threat of industrial Internet of ings [15][16][17]:…”

Section: Introductionmentioning

confidence: 99%

Intelligent Intrusion Detection Method of Industrial Internet of Things Based on CNN-BiLSTM

Yi²

2022

Security and Communication Networks

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Aiming at the problems of fuzzy detection characteristics, high false positive rate and low accuracy of traditional network intrusion detection technology, an improved intelligent intrusion detection method of industrial Internet of Things based on deep learning is proposed. Firstly, the data set is preprocessed and transformed into 122 dimensional intrusion data set after one-hot coding; Secondly, aiming at the problem that convolution network cannot deal with data with long-distance attributes, Bidirectional long short-term memory (BiLSTM) is used to mine the relationship between data features; At the same time, the Batch Normalization mechanism is introduced to speed up the training of deep neural network. After the activation function performs nonlinear transformation on the input data of the previous layer, it is normalized to ensure the trainability of the network. The experimental results on NSL-KDD data set show that the accuracy of the proposed CNN-BiLSTM model is 96.3%, the detection rate is 97.1%, and the performance is the best.

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Section: Introductionmentioning

confidence: 99%

Intelligent Intrusion Detection Method of Industrial Internet of Things Based on CNN-BiLSTM

Yi²

2022

Security and Communication Networks

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“…This attack detection system applies immune cells to stored attack features. Rebbah et al [19] combined the attack detection system with Cloud technology. Such a detection system is based on the temporal and spatial profiles calculated for each client according to the data of its request.…”

Section: Related Workmentioning

confidence: 99%

Attack Detection Model for BCoT based on Contrastive Variational Autoencoder and Metric Learning

Wu,

Liu,

Ding

et al. 2024

Preprint

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With development of blockchain technology, clouding computing and Internet of Things (IoT), blockchain and cloud of things (BCoT) has become development tendency. But the security has become the most development hinder of BCoT. Attack detection model is a crucial part of attack revelation mechanism for BCoT. As a consequence, attack detection model has received more concerned. Due to the great diversity and variation of network attacks aiming to BCoT, tradition attack detection models are not suitable for BCoT. In this paper, we propose a novel attack detection model for BCoT, denoted as cVAE-DML. The novel model is based on contrastive variational autoencoder (cVAE) and deep metric learning (DML). By training the cVAE, the proposed model generates private features for attack visiting information as well as shared features between attack visiting information and normal visiting information. Based on those generated features, the proposed model can generate representative new samples to balance the training dataset. At last, the decoder of cVAE is connected to the deep metric learning network to detect attack aiming to BCoT. The efficiency of cVAE-DML is verified using the CIC-IDS 2017 dataset and CSE-CIC-IDS 2018 dataset. The results show that cVAE-DML can improve attack detection efficiency even under the condition of unbalanced samples.

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“…Thus, these methods can only handle views of events. The second method is to analyze the traffic of IoT milieus of the cloud's data [9,10]. The detection approach on the centralized cloud has various limitations such as latency, battery power constraints, and bandwidth.…”

Section: Introductionmentioning

confidence: 99%

Development of PCCNN-Based Network Intrusion Detection System for EDGE Computing

Abodayeh¹,

Raza²,

Rafiq³

et al. 2022

Computers, Materials &Amp; Continua

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Intrusion Detection System (IDS) plays a crucial role in detecting and identifying the DoS and DDoS type of attacks on IoT devices. However, anomaly-based techniques do not provide acceptable accuracy for efficacious intrusion detection. Also, we found many difficulty levels when applying IDS to IoT devices for identifying attempted attacks. Given this background, we designed a solution to detect intrusions using the Convolutional Neural Network (CNN) for Enhanced Data rates for GSM Evolution (EDGE) Computing. We created two separate categories to handle the attack and non-attack events in the system. The findings of this study indicate that this approach was significantly effective. We attempted both multiclass and binary classification. In the case of binary, we clustered all malicious traffic data in a single class. Also, we developed 13 layers of Sequential 1-D CNN for IDS detection and assessed them on the public dataset NSL-KDD. Principal Component Analysis (PCA) was implemented to decrease the size of the feature vector based on feature extraction and engineering. The approach proposed in the current investigation obtained accuracies of 99.34% and 99.13% for binary and multiclass classification, respectively, for the NSL-KDD dataset. The experimental outcomes showed that the proposed Principal Component-based Convolution Neural Network (PCCNN) approach achieved greater precision based on deep learning and has potential use in modern intrusion detection for IoT systems.

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Intrusion detection in Cloud Internet of Things environment

Cited by 11 publications

References 4 publications

Intelligent Intrusion Detection Method of Industrial Internet of Things Based on CNN-BiLSTM

Intelligent Intrusion Detection Method of Industrial Internet of Things Based on CNN-BiLSTM

Attack Detection Model for BCoT based on Contrastive Variational Autoencoder and Metric Learning

Development of PCCNN-Based Network Intrusion Detection System for EDGE Computing

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