Multi-layer Approach to Internet of Things (IoT) Security

Tukur, Yusuf Muhammad; Thakker, Dhavalkumar; Awan, Irfan-Ullah

doi:10.1109/ficloud.2019.00023

Cited by 7 publications

(1 citation statement)

References 27 publications

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“…Unfortunately, little research has been done on the impact of malicious insiders to the IoT systems, as the great majority of the available literature focuses on other security challenges endangering the systems. This is evidenced in one of our published research works 24 as presented in Table 2. The identified gaps have also motivated some of our further researches in the area, including this work.…”

Section: Introductionmentioning

confidence: 57%

Edge‐based blockchain enabled anomaly detection for insider attack prevention in Internet of Things

Tukur

Thakker

Awan

2020

Trans Emerging Tel Tech

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Internet of Things (IoT) platforms are responsible for overall data processing in the IoT System. This ranges from analytics and big data processing to gathering all sensor data over time to analyze and produce long-term trends. However, this comes with prohibitively high demand for resources such as memory, computing power and bandwidth, which the highly resource constrained IoT devices lack to send data to the platforms to achieve efficient operations. This results in poor availability and risk of data loss due to single point of failure should the cloud platforms suffer attacks. The integrity of the data can also be compromised by an insider, such as a malicious system administrator, without leaving traces of their actions. To address these issues, we propose in this work an edge-based blockchain enabled anomaly detection technique to prevent insider attacks in IoT. The technique first employs the power of edge computing to reduce the latency and bandwidth requirements by taking processing closer to the IoT nodes, hence improving availability, and avoiding single point of failure. It then leverages some aspect of sequence-based anomaly detection, while integrating distributed edge with blockchain that offers smart contracts to perform detection and correction of abnormalities in incoming sensor data. Evaluation of our technique using real IoT system datasets showed that the technique remarkably achieved the intended purpose, while ensuring integrity and availability of the data which is critical to IoT success.

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Section: Introductionmentioning

confidence: 57%

Edge‐based blockchain enabled anomaly detection for insider attack prevention in Internet of Things

Tukur

Thakker

Awan

2020

Trans Emerging Tel Tech

Self Cite

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The Case for Validating ADDIE Model as a Digital Forensic Model for Peer-to-Peer Network Investigation

2022

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Rapid technological advancement can substantially impact the processes of digital forensic investigation and present a myriad of challenges to the investigator. With these challenges, it is necessary to have a standard digital forensic framework as the foundation of any digital investigation. State-of-the-art digital forensic models assume that it is safe to move from one investigation stage to the next. It guides the investigators with the required steps and procedures. This brings a great stride to validate a non-specific framework to be used in most digital investigation procedures. This paper considers a new technique for detecting active peers that participate in a peer-to-peer (P2P) network. As part of our study, we crawled the µTorrent P2P client over ten days in different instances while logging all participating peers. We then employed digital forensic techniques to analyse the popular users and generate evidence within them with high accuracy. We evaluated our approach against the standard Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model for the digital investigation to achieve the credible digital evidence presented in this paper. Finally, we presented a validation case for the ADDIE model using the United States Daubert Test and the United Kingdom’s Forensic Science Regulator Guidance – 218 (FSR-G-218) and Forensic Science Regulator Guidance – 201 (FSR-G-201) to formulate it as a standard digital forensic model.

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Clustering-based data integrity verification approach for multi-replica in a fog environment

Maheswari,

Bhanu,

Savarimuthu

2023

J Supercomput

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Multi-layer Approach to Internet of Things (IoT) Security

Cited by 7 publications

References 27 publications

Edge‐based blockchain enabled anomaly detection for insider attack prevention in Internet of Things

Edge‐based blockchain enabled anomaly detection for insider attack prevention in Internet of Things

The Case for Validating ADDIE Model as a Digital Forensic Model for Peer-to-Peer Network Investigation

Clustering-based data integrity verification approach for multi-replica in a fog environment

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