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Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data

As criminal activity increasingly relies on digital devices, the field of digital forensics plays a vital role in identifying and investigating criminals. In this paper, we addressed the problem of anomaly detection in digital forensics data. Our objective was to propose an effective approach for id...

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Autores principales: Islam, Umar, Alwageed, Hathal Salamah, Farooq, Malik Muhammad Umer, Khan, Inayat, Awwad, Fuad A., Ali, Ijaz, Abonazel, Mohamed R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10302442/
https://www.ncbi.nlm.nih.gov/pubmed/37420791
http://dx.doi.org/10.3390/s23125626
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author Islam, Umar
Alwageed, Hathal Salamah
Farooq, Malik Muhammad Umer
Khan, Inayat
Awwad, Fuad A.
Ali, Ijaz
Abonazel, Mohamed R.
author_facet Islam, Umar
Alwageed, Hathal Salamah
Farooq, Malik Muhammad Umer
Khan, Inayat
Awwad, Fuad A.
Ali, Ijaz
Abonazel, Mohamed R.
author_sort Islam, Umar
collection PubMed
description As criminal activity increasingly relies on digital devices, the field of digital forensics plays a vital role in identifying and investigating criminals. In this paper, we addressed the problem of anomaly detection in digital forensics data. Our objective was to propose an effective approach for identifying suspicious patterns and activities that could indicate criminal behavior. To achieve this, we introduce a novel method called the Novel Support Vector Neural Network (NSVNN). We evaluated the performance of the NSVNN by conducting experiments on a real-world dataset of digital forensics data. The dataset consisted of various features related to network activity, system logs, and file metadata. Through our experiments, we compared the NSVNN with several existing anomaly detection algorithms, including Support Vector Machines (SVM) and neural networks. We measured and analyzed the performance of each algorithm in terms of the accuracy, precision, recall, and F1-score. Furthermore, we provide insights into the specific features that contribute significantly to the detection of anomalies. Our results demonstrated that the NSVNN method outperformed the existing algorithms in terms of anomaly detection accuracy. We also highlight the interpretability of the NSVNN model by analyzing the feature importance and providing insights into the decision-making process. Overall, our research contributes to the field of digital forensics by proposing a novel approach, the NSVNN, for anomaly detection. We emphasize the importance of both performance evaluation and model interpretability in this context, providing practical insights for identifying criminal behavior in digital forensics investigations.
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spelling pubmed-103024422023-06-29 Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data Islam, Umar Alwageed, Hathal Salamah Farooq, Malik Muhammad Umer Khan, Inayat Awwad, Fuad A. Ali, Ijaz Abonazel, Mohamed R. Sensors (Basel) Article As criminal activity increasingly relies on digital devices, the field of digital forensics plays a vital role in identifying and investigating criminals. In this paper, we addressed the problem of anomaly detection in digital forensics data. Our objective was to propose an effective approach for identifying suspicious patterns and activities that could indicate criminal behavior. To achieve this, we introduce a novel method called the Novel Support Vector Neural Network (NSVNN). We evaluated the performance of the NSVNN by conducting experiments on a real-world dataset of digital forensics data. The dataset consisted of various features related to network activity, system logs, and file metadata. Through our experiments, we compared the NSVNN with several existing anomaly detection algorithms, including Support Vector Machines (SVM) and neural networks. We measured and analyzed the performance of each algorithm in terms of the accuracy, precision, recall, and F1-score. Furthermore, we provide insights into the specific features that contribute significantly to the detection of anomalies. Our results demonstrated that the NSVNN method outperformed the existing algorithms in terms of anomaly detection accuracy. We also highlight the interpretability of the NSVNN model by analyzing the feature importance and providing insights into the decision-making process. Overall, our research contributes to the field of digital forensics by proposing a novel approach, the NSVNN, for anomaly detection. We emphasize the importance of both performance evaluation and model interpretability in this context, providing practical insights for identifying criminal behavior in digital forensics investigations. MDPI 2023-06-15 /pmc/articles/PMC10302442/ /pubmed/37420791 http://dx.doi.org/10.3390/s23125626 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Islam, Umar
Alwageed, Hathal Salamah
Farooq, Malik Muhammad Umer
Khan, Inayat
Awwad, Fuad A.
Ali, Ijaz
Abonazel, Mohamed R.
Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data
title Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data
title_full Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data
title_fullStr Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data
title_full_unstemmed Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data
title_short Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data
title_sort investigating the effectiveness of novel support vector neural network for anomaly detection in digital forensics data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10302442/
https://www.ncbi.nlm.nih.gov/pubmed/37420791
http://dx.doi.org/10.3390/s23125626
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