A malicious threat detection model for cloud assisted internet of things (CoT) based industrial control system (ICS) networks using deep belief network

Huda, Shamsul; Miah, Suruz; Yearwood, John; Alyahya, Sultan; Al-Dossari, Hmood; Doss, Robin

doi:10.1016/j.jpdc.2018.04.005

Cited by 51 publications

(18 citation statements)

References 25 publications

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“…Shallow learning methods use the selected features to build a classifier to detect intrusions, such as support vector machines (SVM) [12], decision tree (DT) [13], and k-nearest neighbor (KNN) [14]. Deep learning methods can automatically extract features and perform classification, such as AutoEncoder [15,16], deep neural network (DNN) [17], deep belief network (DBN) [18,19,20,21], and recurrent neural network (RNN) [22]. The last category uses various ensemble and hybrid techniques to improve detection performance, including bagging [23], boosting [24], stacking [25], and combined classifier methods [26].…”

Section: Introductionmentioning

confidence: 99%

Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network

Yang

Zheng

et al. 2019

Sensors

211

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Intrusion detection systems play an important role in preventing security threats and protecting networks from attacks. However, with the emergence of unknown attacks and imbalanced samples, traditional machine learning methods suffer from lower detection rates and higher false positive rates. We propose a novel intrusion detection model that combines an improved conditional variational AutoEncoder (ICVAE) with a deep neural network (DNN), namely ICVAE-DNN. ICVAE is used to learn and explore potential sparse representations between network data features and classes. The trained ICVAE decoder generates new attack samples according to the specified intrusion categories to balance the training data and increase the diversity of training samples, thereby improving the detection rate of the imbalanced attacks. The trained ICVAE encoder is not only used to automatically reduce data dimension, but also to initialize the weight of DNN hidden layers, so that DNN can easily achieve global optimization through back propagation and fine tuning. The NSL-KDD and UNSW-NB15 datasets are used to evaluate the performance of the ICVAE-DNN. The ICVAE-DNN is superior to the three well-known oversampling methods in data augmentation. Moreover, the ICVAE-DNN outperforms six well-known models in detection performance, and is more effective in detecting minority attacks and unknown attacks. In addition, the ICVAE-DNN also shows better overall accuracy, detection rate and false positive rate than the nine state-of-the-art intrusion detection methods.

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Section: Introductionmentioning

confidence: 99%

Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network

Yang

Zheng

et al. 2019

Sensors

211

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“…They reported that this approach is effective in detecting attacks, showing a low positive rate after testing it on two IoT botnets, Mirai and Bashlite [37]. Other researchers tried to place IDS into physical objects by designing optimized lightweight algorithms to match attack signatures and packet payloads [38,39]. A lightweight method was also used to monitor node energy consumption and minimize the resources required for intrusion detection [39].…”

Section: Introductionmentioning

confidence: 99%

IoT Botnet Attack Detection Based on Optimized Extreme Gradient Boosting and Feature Selection

Alqahtani

Mathkour

Ismail

2020

Sensors

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Nowadays, Internet of Things (IoT) technology has various network applications and has attracted the interest of many research and industrial communities. Particularly, the number of vulnerable or unprotected IoT devices has drastically increased, along with the amount of suspicious activity, such as IoT botnet and large-scale cyber-attacks. In order to address this security issue, researchers have deployed machine and deep learning methods to detect attacks targeting compromised IoT devices. Despite these efforts, developing an efficient and effective attack detection approach for resource-constrained IoT devices remains a challenging task for the security research community. In this paper, we propose an efficient and effective IoT botnet attack detection approach. The proposed approach relies on a Fisher-score-based feature selection method along with a genetic-based extreme gradient boosting (GXGBoost) model in order to determine the most relevant features and to detect IoT botnet attacks. The Fisher score is a representative filter-based feature selection method used to determine significant features and discard irrelevant features through the minimization of intra-class distance and the maximization of inter-class distance. On the other hand, GXGBoost is an optimal and effective model, used to classify the IoT botnet attacks. Several experiments were conducted on a public botnet dataset of IoT devices. The evaluation results obtained using holdout and 10-fold cross-validation techniques showed that the proposed approach had a high detection rate using only three out of the 115 data traffic features and improved the overall performance of the IoT botnet attack detection process.

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“…In 2018, two IDS was developed utilizing two different types of deep learning models [41]. The first model utilized deep belief network (DBN).…”

Section: Applied For Securing Iiot Networkmentioning

confidence: 99%

Deep Learning Approaches for Intrusion Detection in IIoT Networks – Opportunities and Future Directions

Vaiyapuri¹,

Sbaï²,

Alaskar³

et al. 2021

IJACSA

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In recent years, the Industrial Internet of things (IIoT) is a fastest advancing innovative technology with a potential to digitize and interconnect many industries for huge business opportunities and development of global GDP. IIoT is used in diverse range of industries such as manufacturing, logistics, transportation, oil and gas, mining and metals, energy utilities and aviation. Although IIoT provides promising opportunities for the development of different industrial applications, they are prone to cyberattacks and demands for higher security requirements. The enormous number of sensors present in the IIoT network generates a large amount of data and has attracted the attention of cybercriminals across globe. The intrusion detection system (IDS) that monitors the network traffic and detects the behaviour of the network is considered as one of the key security solution for securing IIoT application from attacks. Recently, the application of machine and deep learning techniques have proved to mitigate multiple security threats and enhance the performance of intrusion detection. In this paper, we present a survey of deep learning-based IDS technique for IIoT. The main objective of this research is to provide the various deep learning-based IDS detection methods, datasets and comwparative analysis. Finally, this research aims to identify the limitations and challenges of existing studies, solutions and future directions.

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A malicious threat detection model for cloud assisted internet of things (CoT) based industrial control system (ICS) networks using deep belief network

Cited by 51 publications

References 25 publications

Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network

Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network

IoT Botnet Attack Detection Based on Optimized Extreme Gradient Boosting and Feature Selection

Deep Learning Approaches for Intrusion Detection in IIoT Networks – Opportunities and Future Directions

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