Deep Learning with Dense Random Neural Network for Detecting Attacks against IoT-connected Home Environments

Brun, Olivier; Yin, Yilong; Gelenbe, Erol

doi:10.1016/j.procs.2018.07.183

Cited by 96 publications

(56 citation statements)

References 13 publications

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“…However, no description of the experimental setup, implementation, and subsequently, evaluation of the proposed system is provided. Brun et al [34] designed a deep learning-based approach using dense Random Neural Networks for the detection of network attacks. Although this approach often successfully detects attacks, the system was evaluated on a testbed consisting of only 3 devices and simplistic cyber-attacks were employed.…”

Section: B Machine Learning Idssmentioning

confidence: 99%

A Supervised Intrusion Detection System for Smart Home IoT Devices

Anthi

Williams

Slowinska

et al. 2019

IEEE Internet Things J.

441

167

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The proliferation in Internet of Things (IoT) devices, which routinely collect sensitive information, is demonstrated by their prominence in our daily lives. Although such devices simplify and automate every day tasks, they also introduce tremendous security flaws. Current insufficient security measures employed to defend smart devices make IoT the 'weakest' link to breaking into a secure infrastructure, and therefore an attractive target to attackers. This paper proposes a three layer Intrusion Detection System (IDS) that uses a supervised approach to detect a range of popular network based cyber-attacks on IoT networks. The system consists of three main functions: 1) classify the type and profile the normal behaviour of each IoT device connected to the network, 2) identifies malicious packets on the network when an attack is occurring, and 3) classifies the type of the attack that has been deployed. The system is evaluated within a smart home testbed consisting of 8 popular commercially available devices. The effectiveness of the proposed IDS architecture is evaluated by deploying 12 attacks from 4 main network based attack categories such as: Denial of Service (DoS), Man-In-The-Middle (MITM)/Spoofing, Reconnaissance, and Replay. Additionally, the system is also evaluated against 4 scenarios of multi-stage attacks with complex chains of events. The performance of the system's three core functions result in an F-measure of: 1) 96.2%, 2) 90.0%, and 3) 98.0%. This demonstrates that the proposed architecture can automatically distinguish between IoT devices on the network, whether network activity is malicious or benign, and detect which attack was deployed on which device connected to the network successfully.

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Section: B Machine Learning Idssmentioning

confidence: 99%

A Supervised Intrusion Detection System for Smart Home IoT Devices

Anthi

Williams

Slowinska

et al. 2019

IEEE Internet Things J.

441

167

View full text Add to dashboard Cite

show abstract

“…Besides, the authors of study [107] have proposed a deep learning approach with Dense Random Neural Network (DRNN) to predict the probability of an ongoing network attack based on the packet capture. Their methodology primarily focuses on online detection of network attacks against IoT gateways.…”

Section: Deep Learning and Iot Securitymentioning

confidence: 99%

Deep learning and big data technologies for IoT security

Amanullah

Habeeb

Nasaruddin

et al. 2020

Computer Communications

273

119

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Technology has become inevitable in human life, especially the growth of Internet of Things (IoT), which enables communication and interaction with various devices. However, IoT has been proven to be vulnerable to security breaches. Therefore, it is necessary to develop fool proof solutions by creating new technologies or combining existing technologies to address the security issues. Deep learning, a branch of machine learning has shown promising results in previous studies for detection of security breaches. Additionally, IoT devices generate large volumes, variety, and veracity of data. Thus, when big data technologies are incorporated, higher performance and better data handling can be achieved. Hence, we have conducted a comprehensive survey on state-of-the-art deep learning, IoT security, and big data technologies. Further, a comparative analysis and the relationship among deep learning, IoT security, and big data technologies have also been discussed. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three aforementioned domains. Finally, we have identified and discussed the challenges in incorporating deep learning for IoT security using big data technologies and have provided directions to future researchers on the IoT security aspects. Contents * Corresponding author.

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“…Brun et al [38] investigated the detection of denial of service and sleep attacks targeting IoT gateways, and proposed a detection method based on a random neural network model although, its performance was similar to a threshold detector. A deep learning model based on MLP was proposed by Pektas et al [39] that focused on detecting botnets by flagging C&C traffic.…”

Section: Deep Learning For Tracing and Discovering Threat Behavioursmentioning

confidence: 99%

Enhancing Network Forensics with Particle Swarm and Deep Learning: The Particle Deep Framework

Koroniotis

Moustafa

2020

7th International Conference on Artificial Intelligence and Applications

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The popularity of IoT smart things is rising, due to the automation they provide and its effects on productivity. However, it has been proven that IoT devices are vulnerable to both well established and new IoT-specific attack vectors. In this paper, we propose the Particle Deep Framework, a new network forensic framework for IoT networks that utilised Particle Swarm Optimisation to tune the hyperparameters of a deep MLP model and improve its performance. The PDF is trained and validated using Bot-IoT dataset, a contemporary network-traffic dataset that combines normal IoT and non-IoT traffic, with well known botnet-related attacks. Through experimentation, we show that the performance of a deep MLP model is vastly improved, achieving an accuracy of 99.9% and false alarm rate of close to 0%.

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Deep Learning with Dense Random Neural Network for Detecting Attacks against IoT-connected Home Environments

Cited by 96 publications

References 13 publications

A Supervised Intrusion Detection System for Smart Home IoT Devices

A Supervised Intrusion Detection System for Smart Home IoT Devices

Deep learning and big data technologies for IoT security

Enhancing Network Forensics with Particle Swarm and Deep Learning: The Particle Deep Framework

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