Digital Forensics Classification Based on a Hybrid Neural Network and the Salp Swarm Algorithm

Alazab, M; Khurma, RA; Awajan, A; Wedyan, M

Digital Forensics Classification Based on a Hybrid Neural Network and the Salp Swarm Algorithm

Alazab, M Khurma, RA Awajan, A Wedyan, M

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Electronics (Switzerland), 2022, 11, (12)
Issue Date:: 2022-06-01

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Field	Value	Language
dc.contributor.author	Alazab, M
dc.contributor.author	Khurma, RA
dc.contributor.author	Awajan, A
dc.contributor.author	Wedyan, M https://orcid.org/0000-0001-6731-7246
dc.date.accessioned	2023-03-22T01:29:40Z
dc.date.available	2023-03-22T01:29:40Z
dc.date.issued	2022-06-01
dc.identifier.citation	Electronics (Switzerland), 2022, 11, (12)
dc.identifier.issn	1450-5843
dc.identifier.issn	2079-9292
dc.identifier.uri	http://hdl.handle.net/10453/168035
dc.description.abstract	In recent times, cybercrime has increased significantly and dramatically. This made the need for Digital Forensics (DF) urgent. The main objective of DF is to keep proof in its original state by identifying, collecting, analyzing, and evaluating digital data to rebuild past acts. The proof of cybercrime can be found inside a computer’s system files. This paper investigates the viability of Multilayer perceptron (MLP) in DF application. The proposed method relies on analyzing the file system in a computer to determine if it is tampered by a specific computer program. A dataset describes a set of features of file system activities in a given period. These data are used to train the MLP and build a training model for classification purposes. Identifying the optimal set of MLP parameters (weights and biases) is a challenging matter in training MLPs. Using traditional training algorithms causes stagnation in local minima and slow convergence. This paper proposes a Salp Swarm Algorithm (SSA) as a trainer for MLP using an optimized set of MLP parameters. SSA has proved its applicability in different applications and obtained promising optimization results. This motivated us to apply SSA in the context of DF to train MLP as it was never used for this purpose before. The results are validated by comparisons with other meta-heuristic algorithms. The SSAMLP-DF is the best algorithm because it achieves the highest accuracy results, minimum error rate, and best convergence scale.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Electronics (Switzerland)
dc.relation.isbasedon	10.3390/electronics11121903
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.title	Digital Forensics Classification Based on a Hybrid Neural Network and the Salp Swarm Algorithm
dc.type	Journal Article
utslib.citation.volume	11
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-22T01:29:39Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	12

Abstract:

In recent times, cybercrime has increased significantly and dramatically. This made the need for Digital Forensics (DF) urgent. The main objective of DF is to keep proof in its original state by identifying, collecting, analyzing, and evaluating digital data to rebuild past acts. The proof of cybercrime can be found inside a computer’s system files. This paper investigates the viability of Multilayer perceptron (MLP) in DF application. The proposed method relies on analyzing the file system in a computer to determine if it is tampered by a specific computer program. A dataset describes a set of features of file system activities in a given period. These data are used to train the MLP and build a training model for classification purposes. Identifying the optimal set of MLP parameters (weights and biases) is a challenging matter in training MLPs. Using traditional training algorithms causes stagnation in local minima and slow convergence. This paper proposes a Salp Swarm Algorithm (SSA) as a trainer for MLP using an optimized set of MLP parameters. SSA has proved its applicability in different applications and obtained promising optimization results. This motivated us to apply SSA in the context of DF to train MLP as it was never used for this purpose before. The results are validated by comparisons with other meta-heuristic algorithms. The SSAMLP-DF is the best algorithm because it achieves the highest accuracy results, minimum error rate, and best convergence scale.

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http://hdl.handle.net/10453/168035