A Deep Blockchain Framework-Enabled Collaborative Intrusion Detection for Protecting IoT and Cloud Networks

Al-Kadi, Osama; Moustafa, Nour; Turnbull, Benjamin; Choo, Kim‐Kwang Raymond

doi:10.1109/jiot.2020.2996590

Cited by 238 publications

(112 citation statements)

References 32 publications

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“…Comparing the results with work proposed in [9], our Bi-LSTM, SimpleRNN and top performing feature selected models outperform in detecting all attack categories. Figure 4 compares the recall rate of various attack sub-categories with results presented in Alkadi et.…”

Section: F Comparison and Discussionmentioning

confidence: 66%

“…Figure 4 compares the recall rate of various attack sub-categories with results presented in Alkadi et. al [9]. Our proposed approach showed higher recall rates especially in service scan, OS fingerprint, data ex-filtration and keylogging.…”

Section: F Comparison and Discussionmentioning

confidence: 73%

“…The feature selection step reduced the storage space by 90% without loosing the class differentiation ability. This was highlighted by the enhanced recall rate of SimpleRNN and Bi-LSTM models with reduced feature space compared to the full feature set as well as Bi-LSTM model presented in [9]. The dataset splitting method, the reduction of storage requirements combined with superior detection capability of deep learning RNN models makes the proposed method scalable and suitable for FoG layer intrusion detection.…”

Section: Discussionmentioning

confidence: 96%

“…In [9], the authors deployed a bi-directional LSTM deep learning algorithm to train and classify sequential network traffic in cloud networks. The proposed approach yielded a high detection rate (between 95-99% ) in classifying DoS and DDoS attack traffic when evaluated on UNSW-NB15 and BoT-IoT datasets.…”

Section: Related Workmentioning

confidence: 99%

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Intrusion Detection for Time-Series IoT Data with Recurrent Neural Networks and Feature Selection

Syed¹,

Ge²,

Baig³

2021

Preprint

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<div>Deep learning (DL) techniques are being widely researched for their effectiveness in detecting cyber intrusions against the Internet of Things (IoT). However, time sensitive Critical Infrastructures (CIs) that rely on IoT require quicker detection of cyber intrusions close to the constrained devices in order to prevent service delays. FoG layer with higher computing resources and close proximity to IoT devices makes them suitable to deploy deep learning techniques for effective intrusion detection. However, communication overheads due to large volume of IoT data and computation requirements for deep learning models prevents effective use of FoG layer and raises scalability issues. To counter these issues, we propose a novel IoT intrusion detection framework incorporates feature selection step on time-series IoT data, followed by a deep learning Recurrent Neural Network (SimpleRNN and Bi-directional Long Short-Term Memory (LSTM)) based IoT attack detection. The effectiveness of the proposed approach was evaluated using the high-dimensional BoT-IoT dataset which contains large volumes of realistic IoT attack traffic. Results show that feature selection methods significantly ( 90%) reduced the dataset size and still achieved an increased recall rate compared to full feature set without loosing class differentiation ability. The SimpleRNN and Bi-LSTM models also did not suffer any underfitting or overfitting with the reduced feature space. The proposed deep learning based IoT intrusion detection framework is suitable for FoG layer deployment and can scale well with effective use of sub-divided dataset.</div>

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Section: F Comparison and Discussionmentioning

confidence: 66%

Section: F Comparison and Discussionmentioning

confidence: 73%

Section: Discussionmentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Intrusion Detection for Time-Series IoT Data with Recurrent Neural Networks and Feature Selection

Syed¹,

Ge²,

Baig³

2021

Preprint

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“…Besides federated learning and cryptographic algorithms, the advent of recent machine learning and deep learning technologies can also potentially address the privacy concern. For example, Alkadi et al [179] presented a blockchainbased framework with deep learning approaches to identify the intrusion attacks while preserving data privacy. Moreover, the work [180] introduced a privacy-aware deep learning method, which allows the collaboration of multiple nodes to train deep neural networks while preserving data privacy.…”

Section: B Secure Iiot Critical Infrastructuresmentioning

confidence: 99%

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

Dai

Wang

2021

IEEE Internet Things J.

188

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Critical infrastructure systems are vital to underpin the functioning of a society and economy. Due to ever-increasing number of Internet-connected Internet-of-Things (IoTs) / Industrial IoT (IIoT), and high volume of data generated and collected, security and scalability are becoming burning concerns for critical infrastructures in industry 4.0. The blockchain technology is essentially a distributed and secure ledger that records all the transactions into a hierarchically expanding chain of blocks. Edge computing brings the cloud capabilities closer to the computation tasks. The convergence of blockchain and edge computing paradigms can overcome the existing security and scalability issues. In this paper, we first introduce the IoT/IIoT critical infrastructure in industry 4.0, and then we briefly present the blockchain and edge computing paradigms. After that, we show how the convergence of these two paradigms can enable secure and scalable critical infrastructures. Then, we provide a survey on state-of-the-art for security and privacy, and scalability of IoT/IIoT critical infrastructures. A list of potential research challenges and open issues in this area is also provided, which can be used as useful resources to guide future research.

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Interference Techniques Based on Deep Learning in Wireless Networks

Arunprasath,

Suresh,

Khakurel

2023

Resource Management in Advanced Wireless Networks

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A Deep Blockchain Framework-Enabled Collaborative Intrusion Detection for Protecting IoT and Cloud Networks

Cited by 238 publications

References 32 publications

Intrusion Detection for Time-Series IoT Data with Recurrent Neural Networks and Feature Selection

Intrusion Detection for Time-Series IoT Data with Recurrent Neural Networks and Feature Selection

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

Interference Techniques Based on Deep Learning in Wireless Networks

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