Intrusion Detection System for Internet of Things Based on Temporal Convolution Neural Network and Efficient Feature Engineering

Derhab, Abdelouahid; Aldweesh, Arwa; Emam, Ahmed; Khan, Farrukh Aslam

doi:10.1155/2020/6689134

Cited by 73 publications

(21 citation statements)

References 50 publications

(80 reference statements)

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“…Qaddoura et al [ 73 ] combined SVM-SMOTE with DNN to handle a class imbalance in binary classification. Derhab et al [ 74 ] employed a combination of SMOTE and Temporal CNN to address the class imbalance in 5-class classification. However, the SMOTE method has not been previously combined with the DRNN model.…”

Section: Review Of Related Workmentioning

confidence: 99%

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

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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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“…Liaqat et al [41] used the up-sampling method to increase the number of benign samples in the training data set. In [42][43][44][45], Synthetic Minority Oversampling Technique (SMOTE) method was used to generate additional samples for the minority classes. Mulyanto et al [46] performed feature selection to reduce dimensionality while focal loss function was used to address class imbalance problem.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…Recent studies recommended SMOTE as an efficient over-sampling method [42][43][44][45]47,51]. Therefore, SMOTE algorithm was proposed to deal with the high class imbalance problem in the training set in an 11-class classification scenario.…”

Section: Synthetic Minority Oversampling Techniquementioning

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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“…In 2020, researchers made a study to emphasize how important to design a system while developing an intrusion detection system for IoT (Derhab et al, 2020). Since the main purpose of the study was to design an intrusion detection system, they created a hybrid model by combining convolutional neural network (CNN) which a deep learning approach with causal convolution, and they called it Temporal Convolution Neural Network (TCNN).…”

Section: Related Workmentioning

confidence: 99%

Detecting Internet of Things Attacks by Using Hybrid Learning and Feature Selection Method

BAYDOĞMUŞ¹

2021

European Journal of Science and Technology

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Internet of Things (IoT) produces an enormous amount of data, which is used in all areas of our lives and increases the number of data on the Internet with each passing day. Smart watches, robot vacuum cleaners, refrigerators with cameras, and more can all be considered IoT devices. Ease of access to the Internet provides people with advantages as well as disadvantages. Malware and intruders have easier access to the devices we use and our information via the internet. At this point, data security gains great importance especially in IoT devices because accessing our personal data via smart watches or refrigerators we use can pose a great threat to individuals and their families. This study focus the importance of data preprocessing and developing a hybrid machine learning-based intrusion detection system (IDS) for IoT. Decision Tree, which is a popular machine learning algorithm, and n_Balot dataset were preferred for investigations. Accordingly, it is aimed to create a hybrid model by applying K-means and Decision Tree algorithms to the n_Balot dataset with under sampling and feature selection. In the data preprocessing, feature selection was performed with Chi-Square method and under sampling performed with RandomOverSampling method. Then, clustering was done by applying K-means to the processed dataset, and the results obtained with the clustering were classified with the Decision tree algorithm. As a result of the study, while the error rate was 0.39% in the predictions made only with the decision tree, the error rate was reduced to 0.01% with the developed hybrid model.

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Intrusion Detection System for Internet of Things Based on Temporal Convolution Neural Network and Efficient Feature Engineering

Cited by 73 publications

References 50 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

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

Detecting Internet of Things Attacks by Using Hybrid Learning and Feature Selection Method

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