An Efficient Low Complexity Edge-Cloud Framework for Security in IoT Networks

Hương, Trương Thu; Bac, Ta Phuong; Long, Dao Minh; Luong, Tran Duc; Bình, Nguyễn Thanh

doi:10.1109/icce48956.2021.9352046

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…In [ 40 ], the Convolutional Neural Network (CNN) model outperformed the RNN, LSTM and GRU models. Huong et al [ 41 , 42 ] proposed a low-complexity edge-cloud DNN model, which achieved better performance than the kNN, DT, RF and SVM models. Lee et al [ 43 ] employed RF for botnet attack classification in an IoT smart factory.…”

Section: Review Of Related Workmentioning

confidence: 99%

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

Popoola

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Ande

et al. 2021

Sensors

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Nowadays, hackers take illegal advantage of distributed resources in a network of computing devices (i.e., botnet) to launch cyberattacks against the Internet of Things (IoT). Recently, diverse Machine Learning (ML) and Deep Learning (DL) methods were proposed to detect botnet attacks in IoT networks. However, highly imbalanced network traffic data in the training set often degrade the classification performance of state-of-the-art ML and DL models, especially in classes with relatively few samples. In this paper, we propose an efficient DL-based botnet attack detection algorithm that can handle highly imbalanced network traffic data. Specifically, Synthetic Minority Oversampling Technique (SMOTE) generates additional minority samples to achieve class balance, while Deep Recurrent Neural Network (DRNN) learns hierarchical feature representations from the balanced network traffic data to perform discriminative classification. We develop DRNN and SMOTE-DRNN models with the Bot-IoT dataset, and the simulation results show that high-class imbalance in the training data adversely affects the precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), geometric mean (GM) and Matthews correlation coefficient (MCC) of the DRNN model. On the other hand, the SMOTE-DRNN model achieved better classification performance with 99.50% precision, 99.75% recall, 99.62% F1 score, 99.87% AUC, 99.74% GM and 99.62% MCC. Additionally, the SMOTE-DRNN model outperformed state-of-the-art ML and DL models.

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Section: Review Of Related Workmentioning

confidence: 99%

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

Popoola

Adebisi

Ande

et al. 2021

Sensors

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“…This may not be afforded by the resource limitation of edge devices. Research [25] has shown that with a model based on a neural network structure, it is not necessary to have a structure that is too complex to be effective in classification or detection.…”

Section: Anomaly Detection For Time-series Datamentioning

confidence: 99%

Detecting cyberattacks using anomaly detection in industrial control systems: A Federated Learning approach

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Bac

Long

et al. 2021

Computers in Industry

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“…Popoola et al [10] performed feature dimensionality reduction based on the LAE algorithm, while the Bidirectional LSTM (BLSTM) algorithm was used for the 5-class classification task. Other feature dimensionality reduction methods include Principal Component Analysis (PCA) in [37][38][39], and t-distributed Stochastic Neighbour Embedding (t-SNE) in [40]. Khan and Kim [48] proposed a hybrid intelligent model using both anomaly-based and misuse-based NIDS approaches.…”

Section: Review Of Related Workmentioning

confidence: 99%

Memory-Efficient Deep Learning for Botnet Attack Detection in IoT Networks

et al. 2021

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Cyber attackers exploit a network of compromised computing devices, known as a botnet, to attack Internet-of-Things (IoT) networks. Recent research works have recommended the use of Deep Recurrent Neural Network (DRNN) for botnet attack detection in IoT networks. However, for high feature dimensionality in the training data, high network bandwidth and a large memory space will be needed to transmit and store the data, respectively in IoT back-end server or cloud platform for Deep Learning (DL). Furthermore, given highly imbalanced network traffic data, the DRNN model produces low classification performance in minority classes. In this paper, we exploit the joint advantages of Long Short-Term Memory Autoencoder (LAE), Synthetic Minority Oversampling Technique (SMOTE), and DRNN to develop a memory-efficient DL method, named LS-DRNN. The effectiveness of this method is evaluated with the Bot-IoT dataset. Results show that the LAE method reduced the dimensionality of network traffic features in the training set from 37 to 10, and this consequently reduced the memory space required for data storage by 86.49%. SMOTE method helped the LS-DRNN model to achieve high classification performance in minority classes, and the overall detection rate increased by 10.94%. Furthermore, the LS-DRNN model outperformed state-of-the-art models.

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An Efficient Low Complexity Edge-Cloud Framework for Security in IoT Networks

Cited by 9 publications

References 12 publications

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

Detecting cyberattacks using anomaly detection in industrial control systems: A Federated Learning approach

Memory-Efficient Deep Learning for Botnet Attack Detection in IoT Networks

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