Forensic detection of heterogeneous activity in data using deep learning methods
The abundance of digital images has been facilitated by smartphones and inexpensive storage. Digital forensic investigation requires the processing of tons of digital images collected on devices to either identify or validate the device's user or to ascertain whether the operator has any connec...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266730532300128X |
| _version_ | 1797287600777592832 |
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| author | Benedicta Nana Esi Nyarko Wu Bin Jinzhi Zhou Justice Odoom Samuel Akwasi Danso Gyarteng Emmanuel Sarpong Addai |
| author_facet | Benedicta Nana Esi Nyarko Wu Bin Jinzhi Zhou Justice Odoom Samuel Akwasi Danso Gyarteng Emmanuel Sarpong Addai |
| author_sort | Benedicta Nana Esi Nyarko |
| collection | DOAJ |
| description | The abundance of digital images has been facilitated by smartphones and inexpensive storage. Digital forensic investigation requires the processing of tons of digital images collected on devices to either identify or validate the device's user or to ascertain whether the operator has any connections to the case that would be of interest. Examining and evaluating heterogeneous activity presents several difficulties, including variability, complex interaction across information, and volume. Digital forensics processes are said to need the inspection and analysis stages. This research presents a hybrid optimization of the Grey Wolf and artificial bee colony (GW-ABC) optimization with deep learning model Convolutional Neural Network (CNN) i.e., GW-ABC-CNN, and the developed framework is integrated as a module for Autopsy software. The main objective of this research is to detect the heterogeneous activity of humans from the Heterogeneous Human Activity Recognition (HHAR) database. The developed model is integrated into the data-source ingest module; in this module, pre-processing, feature extraction, and detection process is performed. Moreover, in the pre-processing stage, the Min-Max normalization method is used and the required frequency and time features are extracted using the GW-ABC method. In addition, CNN is used to detect heterogeneous activity; this detection process is performed by four layers. Finally, the effectiveness of the developed model is assessed, and the outcomes of using the GW-ABC-CNN paradigm were compared to those of other strategies to evaluate the model's effectiveness. |
| first_indexed | 2024-03-07T18:35:42Z |
| format | Article |
| id | doaj.art-e15879f08ef2404b9a80af661531a986 |
| institution | Directory Open Access Journal |
| issn | 2667-3053 |
| language | English |
| last_indexed | 2024-03-07T18:35:42Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj.art-e15879f08ef2404b9a80af661531a9862024-03-02T04:55:15ZengElsevierIntelligent Systems with Applications2667-30532024-03-0121200303Forensic detection of heterogeneous activity in data using deep learning methodsBenedicta Nana Esi Nyarko0Wu Bin1Jinzhi Zhou2Justice Odoom3Samuel Akwasi Danso4Gyarteng Emmanuel Sarpong Addai5School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China; Corresponding author.School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, ChinaSchool of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, ChinaSchool of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, ChinaSchool of Computer Science, Ghana Communication Technology University, Accra, GhanaSchool of Computer Science and Engineering, University of Electronic Science and Engineering, Chengdu, ChinaThe abundance of digital images has been facilitated by smartphones and inexpensive storage. Digital forensic investigation requires the processing of tons of digital images collected on devices to either identify or validate the device's user or to ascertain whether the operator has any connections to the case that would be of interest. Examining and evaluating heterogeneous activity presents several difficulties, including variability, complex interaction across information, and volume. Digital forensics processes are said to need the inspection and analysis stages. This research presents a hybrid optimization of the Grey Wolf and artificial bee colony (GW-ABC) optimization with deep learning model Convolutional Neural Network (CNN) i.e., GW-ABC-CNN, and the developed framework is integrated as a module for Autopsy software. The main objective of this research is to detect the heterogeneous activity of humans from the Heterogeneous Human Activity Recognition (HHAR) database. The developed model is integrated into the data-source ingest module; in this module, pre-processing, feature extraction, and detection process is performed. Moreover, in the pre-processing stage, the Min-Max normalization method is used and the required frequency and time features are extracted using the GW-ABC method. In addition, CNN is used to detect heterogeneous activity; this detection process is performed by four layers. Finally, the effectiveness of the developed model is assessed, and the outcomes of using the GW-ABC-CNN paradigm were compared to those of other strategies to evaluate the model's effectiveness.http://www.sciencedirect.com/science/article/pii/S266730532300128XForensic analysisHeterogeneous activityConvolutional neural networkDeep learningDigital forensics |
| spellingShingle | Benedicta Nana Esi Nyarko Wu Bin Jinzhi Zhou Justice Odoom Samuel Akwasi Danso Gyarteng Emmanuel Sarpong Addai Forensic detection of heterogeneous activity in data using deep learning methods Intelligent Systems with Applications Forensic analysis Heterogeneous activity Convolutional neural network Deep learning Digital forensics |
| title | Forensic detection of heterogeneous activity in data using deep learning methods |
| title_full | Forensic detection of heterogeneous activity in data using deep learning methods |
| title_fullStr | Forensic detection of heterogeneous activity in data using deep learning methods |
| title_full_unstemmed | Forensic detection of heterogeneous activity in data using deep learning methods |
| title_short | Forensic detection of heterogeneous activity in data using deep learning methods |
| title_sort | forensic detection of heterogeneous activity in data using deep learning methods |
| topic | Forensic analysis Heterogeneous activity Convolutional neural network Deep learning Digital forensics |
| url | http://www.sciencedirect.com/science/article/pii/S266730532300128X |
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