An Improved Deep Belief Network IDS on IoT-Based Network for Traffic Systems

Muzafar, Rayeesa; Singh, Yashwant; Sheikh, Zakir Ahmad; Anand, Pooja; Singh, Pradeep Kumar; Workneh, Tewabe Chekole

doi:10.1155/2022/7892130

Cited by 17 publications

(3 citation statements)

References 47 publications

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“…Martins et al (2022) also presented an ensemble approach using machine learning techniques for detecting attacks in IPv6 Routing Protocol for lowpowered and lossy IoT networks with high accuracy. Malik et al (2022) 2 shows the comparison of existing IDS approaches for IoT.…”

Section: Related Workmentioning

confidence: 99%

An ensemble deep learning based IDS for IoT using Lambda architecture

Alghamdi

Bellaïche

2023

Cybersecurity

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The Internet of Things (IoT) has revolutionized our world today by providing greater levels of accessibility, connectivity and ease to our everyday lives. It enables massive amounts of data to be traversed across multiple heterogeneous devices that are all interconnected. This phenomenon makes IoT networks vulnerable to various network attacks and intrusions. Building an Intrusion Detection System (IDS) for IoT networks is challenging as they enable a massive amount of data to be aggregated, which is difficult to handle and analyze in real time mainly because of the heterogeneous nature of IoT devices. This inefficient, traditional IDS approach accentuates the need to develop advanced IDS techniques by employing Machine or Deep Learning. This paper presents a deep ensemble-based IDS using Lambda architecture by following a multi-pronged classification approach. Binary classification uses Long Short Term Memory (LSTM) to differentiate between malicious and benign traffic, while the multi-class classifier uses an ensemble of LSTM, Convolutional Neural Network and Artificial Neural Network classifiers to detect the type of attacks. The model training is performed in the batch layer, while real-time evaluation is carried out through model inferences in the speed layer of the Lambda architecture. The proposed approach gives high accuracy of over 99.93% and saves useful processing time due to the multi-pronged classification strategy and using the lambda architecture.

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Section: Related Workmentioning

confidence: 99%

An ensemble deep learning based IDS for IoT using Lambda architecture

Alghamdi

Bellaïche

2023

Cybersecurity

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“…In Ref. [53], Malik et al designed an NIDS for IoT traffic systems. They applied a Deep Belief Network (DBN) to perform the intrusion detection task.…”

Section: Literature Surveymentioning

confidence: 99%

An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security

Mohy-eddine

Guezzaz

Benkirane

et al. 2023

Big Data Min. Anal.

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Industrial Internet of Things (IIoT) represents the expansion of the Internet of Things (IoT) in industrial sectors. It is designed to implicate embedded technologies in manufacturing fields to enhance their operations.However, IIoT involves some security vulnerabilities that are more damaging than those of IoT. Accordingly, Intrusion Detection Systems (IDSs) have been developed to forestall inevitable harmful intrusions. IDSs survey the environment to identify intrusions in real time. This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security. We combine Isolation Forest (IF) with Pearson's Correlation Coefficient (PCC) to reduce computational cost and prediction time. IF is exploited to detect and remove outliers from datasets. We apply PCC to choose the most appropriate features. PCC and IF are applied exchangeably (PCCIF and IFPCC). The Random Forest (RF) classifier is implemented to enhance IDS performances. For evaluation, we use the Bot-IoT and NF-UNSW-NB15-v2 datasets. RF-PCCIF and RF-IFPCC show noteworthy results with 99.98% and 99.99% Accuracy (ACC) and 6.18 s and 6.25 s prediction time on Bot-IoT, respectively. The two models also score 99.30% and 99.18% ACC and 6.71 s and 6.87 s prediction time on NF-UNSW-NB15-v2, respectively. Results prove that our designed model has several advantages and higher performance than related models.

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“…Many researchers contributed to securing these IoT devices and network infrastructure. [12] Uses Deep Belief Network to detect intrusion in IoT-Based IDS, and the accuracy of their proposed solution is 86%. Deep Learning Model used by [3] to detect abnormal network traffic in an IoT environment.…”

Section: Introductionmentioning

confidence: 99%

A Smart IDS in IoT System to Detect Zero-Day Intrusions Using Automated Signature Update

Mahmood

Shafi

2023

Preprint

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Protecting industry infrastructure from cyber-attacks is the necessity of time. We heard of zero-day attacks, and now every industry is facing them. The death of the Perimeter term in cybersecurity defines that we can't make our infrastructure 100\% secure. We are using Firewalls, Anti-viruses, IPS (Intrusion Prevention Systems) and IDS (Intrusion Detection Systems), etc., to make industry perimeter secure. But we are still facing zero-day attacks, and we can't prevent them. There is a need for smart IDS that can detect intrusion in networks and systems, and prevent them as well. Some previous work is already been done to detect zero-day attacks in networks, and they are using different approaches based on anomaly detection using Machine Learning algorithms. Machine Learning provides many benefits including and not limited to zero-day attacks by analyzing previously learned and trained datasets. But the only problem that remains is the accuracy of ML algorithms. Our approach is using old-fashioned signature-based detection, which guarantees 100\% accuracy but is limited to zero-day attacks. To deal with zero-day attacks, we're using our approach with the ML algorithms and a multi-classifier to detect zero-day intrusion in a network and update their signatures automatically. Our approach will guarantee prevention from known threats using signatures and more accuracy in the detection of zero-day attacks by using ML algorithms with a multi-classifier.

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An Improved Deep Belief Network IDS on IoT-Based Network for Traffic Systems

Cited by 17 publications

References 47 publications

An ensemble deep learning based IDS for IoT using Lambda architecture

An ensemble deep learning based IDS for IoT using Lambda architecture

An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security

A Smart IDS in IoT System to Detect Zero-Day Intrusions Using Automated Signature Update

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