Internet of Things Forensics: A Review

Atlam, Hany F.; Hemdan, Ezz El‐Din; Alenezi, Ahmed; Alassafi, Madini O.; Wills, Gary

doi:10.1016/j.iot.2020.100220

Cited by 57 publications

(23 citation statements)

References 27 publications

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“…They attempted to contribute to the body of knowledge in this field by exploring and analyzing the client-centric and cloudnative forensic artifacts. In [258], IoT forensics was comprehensively reviewed. The authors, first, systematically discussed the issues related to IoT security.…”

Section: Network Forensicsmentioning

confidence: 99%

Digital Forensics Subdomains: The State of the Art and Future Directions

et al. 2021

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For reliable and relevant scientific evidence to be admitted in a court of law, it is important to apply digital forensic investigation techniques to corroborate a suspected potential security incident. Mainly, traditional digital forensics techniques have focused on computer desktops and servers. However, recent advances in digital media and platforms have seen an increased need for the application of digital forensic investigation techniques to other subdomains including small and mobile devices, databases, networks, cloudbased platforms, and the Internet of Things (IoT). To assist forensic investigators, conduct investigations within these subdomains, academic researchers have attempted to develop a number of investigative processes. However, many of these processes are domain-specific or describe domain-specific investigative tools. Hence, we hypothesize that the literature is littered with potentially overlapping and contradicting investigative process for conducting investigations within these subdomains. To investigate this hypothesis, a digital forensic model-orientated Systematic Literature Review (SLR) within the above digital forensic subdomains was undertaken. The purpose of the SLR was to identify the different and heterogeneous practices that have emerged within the specific subdomains. A key finding from the SLR is that there is a potential information overload and a high-degree of ambiguity among investigative processes in the above subdomains. The outcome of this study proposes a high-level abstract metamodel called The Digital Forensic Metamodel (DFM), which combines common processes, activities, techniques, and tasks for the above subdomains.

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Section: Network Forensicsmentioning

confidence: 99%

Digital Forensics Subdomains: The State of the Art and Future Directions

et al. 2021

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“…Therefore, in this experiment, we investigate how the Chord network remains connected even when a large number of nodes fail simultaneously. The considered network size is 2 6 nodes that stores 2 8 keys and we randomly select a fraction of nodes p that fail. We select the percentage of node failures to be p = {0.05, 0.11, 0.16, 0.2}.…”

Section: Simultaneous Node Failuresmentioning

confidence: 99%

“…These smart objects, when supported by an “anywhere, anytime, and anything connection” become the basic building blocks of IoT [ 1 , 2 ]. As stated in [ 3 , 4 , 5 , 6 ], there are 30 billion interconnected nodes and 75 billion nodes are expected by 2025. Utilizing IoT-connected devices can aid in monitoring vital, confined, restricted, and hazardous places and processes [ 7 , 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Towards Scalable and Efficient Architecture for Modeling Trust in IoT Environments

Ghaleb

Azzedin

2021

Sensors

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The Internet of Services (IoS) is gaining ground where cloud environments are utilized to create, subscribe, publish, and share services. The fast and significant evolution of IoS is affecting various aspects in people’s life and is enabling a wide spectrum of services and applications ranging from smart e-health, smart homes, to smart surveillance. Building trusted IoT environments is of great importance to achieve the full benefits of IoS. In addition, building trusted IoT environments mitigates unrecoverable and unexpected damages in order to create reliable, efficient, stable, and flexible smart IoS-driven systems. Therefore, ensuring trust will provide the confidence and belief that IoT devices and consequently IoS behave as expected. Before hosting trust models, suitable architecture for Fog computing is needed to provide scalability, fast data access, simple and efficient intra-communication, load balancing, decentralization, and availability. In this article, we propose scalable and efficient Chord-based horizontal architecture. We also show how trust modeling can be mapped to our proposed architecture. Extensive performance evaluation experiments have been conducted to evaluate the performance and the feasibility and also to verify the behavior of our proposed architecture.

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“…The increasing availability and use of data acquisition technologies is leading to exponential collection of data [1]. The number of structured datasets is growing rapidly, and these datasets are becoming larger and more complex [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Binary Cuckoo Search With Frequent Values and Rough Set Theory for Feature Selection

Alia¹,

Taweel

2021

IEEE Access

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Redundant and irrelevant features in datasets decrease classification accuracy, and increase computational time of classification algorithms, overfitting problem and complexity of the underlying classification model. Feature selection is a preprocessing technique used in classification algorithms to improve the selection of relevant features. Several approaches that combine Rough Set Theory (RST) with Nature Inspired Algorithms (NIAs) have been used successfully for feature selection. However, due to the inherit limitations of RST for some data types and the inefficient convergence of NIAs for high dimensional datasets, these approaches have mainly focused on a specific type of low dimensional nominal dataset. This paper proposes a new filter feature selection approach based on Binary Cuckoo Search (BCS) and RST, which is more efficient for low and high dimensional nominal, mixed and numerical datasets. It enhances BCS by developing a new initialization and global update mechanisms to increase the efficiency of convergence for high dimensional datasets. It also develops a more efficient objective function for numerical, mixed and nominal datasets. The proposed approach was validated on 16 benchmark datasets; 4 nominal, 4 mixed and 8 numerical drawn from the UCI repository. It was also evaluated against standard BCS; five NIAs with fuzzy RST approaches; two popular traditional FS approaches; and multi objective evolutionary, Genetic, and Particle Swarm Optimization (PSO) algorithms. Decision tree and naive Bayes algorithms were used to measure the classification performance of the proposed approach. The results show that the proposed approach achieved improved classification accuracy while minimizing the number of features compared to other state-of-the-art methods. The code is available at https://github.com/abualia4/EBCS.

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Internet of Things Forensics: A Review

Cited by 57 publications

References 27 publications

Digital Forensics Subdomains: The State of the Art and Future Directions

Digital Forensics Subdomains: The State of the Art and Future Directions

Towards Scalable and Efficient Architecture for Modeling Trust in IoT Environments

Enhanced Binary Cuckoo Search With Frequent Values and Rough Set Theory for Feature Selection

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