EclipseIoT: A secure and adaptive hub for the Internet of Things

Anthi, Eirini; Ahmad, Shazaib; Rana, Omer; Theodorakopoulos, George; Burnap, Pete

doi:10.1016/j.cose.2018.07.016

Cited by 28 publications

(23 citation statements)

References 20 publications

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“…As a result, it is not possible to employ complex and robust security measures. Additionally, given the diversity of these devices, it is very challenging to develop and deploy a security mechanism that can endure with the scale and range of devices [9].…”

Section: Introductionmentioning

confidence: 99%

A Supervised Intrusion Detection System for Smart Home IoT Devices

Anthi

Williams

Slowinska

et al. 2019

IEEE Internet Things J.

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444

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: Introductionmentioning

confidence: 99%

A Supervised Intrusion Detection System for Smart Home IoT Devices

Anthi

Williams

Slowinska

et al. 2019

IEEE Internet Things J.

Self Cite

444

167

View full text Add to dashboard Cite

show abstract

“…A very interesting idea was developed in EclipseIoT [22], which in addition to authentication and access control, features an early detection provision based on canary files. These are forged files with enticing names (e.g., "SmartLock.py") placed amongst genuine ones.…”

Section: B Behaviour-based Smart Home Idsmentioning

confidence: 99%

Self-Configurable Cyber-Physical Intrusion Detection for Smart Homes Using Reinforcement Learning

Heartfield

Loukas

Bezemskij

et al. 2021

IEEE Trans.Inform.Forensic Secur.

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The modern Internet of Things (IoT)-based smart 1 home is a challenging environment to secure: devices change, 2 new vulnerabilities are discovered and often remain unpatched, 3 and different users interact with their devices differently and 4 have different cyber risk attitudes. A security breach's impact is 5 not limited to cyberspace, as it can also affect or be facilitated 6 in physical space, for example, via voice. In this environment, 7 intrusion detection cannot rely solely on static models that 8 remain the same over time and are the same for all users.9We present MAGPIE, the first smart home intrusion detection 10 system that is able to autonomously adjust the decision function 11 of its underlying anomaly classification models to a smart home's 12 changing conditions (e.g., new devices, new automation rules and 13 user interaction with them). The method achieves this goal by 14 applying a novel probabilistic cluster-based reward mechanism 15 to non-stationary multi-armed bandit reinforcement learning. 16 MAGPIE rewards the sets of hyperparameters of its underlying 17 isolation forest unsupervised anomaly classifiers based on the 18 cluster silhouette scores of their output. 19 Experimental evaluation in a real household shows that MAG-20 PIE exhibits high accuracy because of two further innovations: 21 it takes into account both cyber and physical sources of data; 22 and it detects human presence to utilise models that exhibit the 23 highest accuracy in each case. MAGPIE is available in open-24 source format, together with its evaluation datasets, so it can 25 benefit from future advances in unsupervised and reinforcement 26 learning and be able to be enriched with further sources of data 27 as smart home environments and attacks evolve.

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“…Also, Transport Layer Security (TLS), as used by [22] on 802.11 devices, is not an option for WSN devices given its constrained nature [23; 24]. Because of that, a security mechanism must be used to overcome this issue.…”

Section: A Problem Fundamentals and Contributionmentioning

confidence: 99%

Lightweight and Secure Publish-Subscribe System for Cloud-Connected Ultra Low Power IoT Devices

Junior

Silva²,

Guelfi³

et al. 2021

JCIS

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The Internet of Things (IoT) enables the development of innovative applications in various domains such as healthcare, transportation, and Industry 4.0. The integration of the cloud platform's large processing and storage capacity with the ubiquitous sensing and actuation provided by the devices creates an IoT architecture that provides vast raw data. The IoT devices send the data to the cloud platform with the developed IoT applications, which can use publish-subscribe systems. However, messages with sensitive content require endto-end security. Besides that, IoT devices may present processing, memory, payload, and energy restrictions. In this sense, messages in an IoT architecture need to achieve both energy-efficiency and secure message delivery. Thus, this article's main contribution refers to a system that standardizes the publish-subscribe topic and payload used by the cloud platform and the IoT devices.Our system also provides end-to-end security while surpassing the energy-efficiency to send data than the main related works in the literature regarding the use of publish-subscribe systems in IoT.

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EclipseIoT: A secure and adaptive hub for the Internet of Things

Cited by 28 publications

References 20 publications

A Supervised Intrusion Detection System for Smart Home IoT Devices

A Supervised Intrusion Detection System for Smart Home IoT Devices

Self-Configurable Cyber-Physical Intrusion Detection for Smart Homes Using Reinforcement Learning

Lightweight and Secure Publish-Subscribe System for Cloud-Connected Ultra Low Power IoT Devices

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