- Title
- Investigating the effectiveness of novel support vector neural network for anomaly detection in digital forensics data
- Creator
- Islam, Umar; Alwageed, Hathal; Farooq, Malik; Khan, Inayat; Awwad, Fuad; Ali, Ijaz; Abonazel, Mohamed
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/195905
- Identifier
- vital:18598
- Identifier
-
https://doi.org/10.3390/s23125626
- Identifier
- ISSN:1424-8220 (ISSN)
- Abstract
- 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. © 2023 by the authors.
- Publisher
- MDPI
- Relation
- Sensors Vol. 23, no. 12 (2023), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2023 by the authors
- Rights
- Open Access
- Subject
- 4008 Electrical engineering; 4009 Electronics, sensors and digital hardware; 4606 Distributed computing and systems software; Anomaly; Cybersecurity; Forensics; Machine learning; NN; Novel support vector neural network; SVM
- Full Text
- Reviewed
- Funder
- This research received funding from King Saud University through Researchers Supporting Project Number RSPD2023R576, King Saud University, Riyadh, Saudi Arabia.
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