Efficient Computer Forensic Analysis Using Machine Learning Approaches

Conference paper


Toraskar, T., Bhangale, U., Patil, S., More, N. and Chaudhari, U. (2019). Efficient Computer Forensic Analysis Using Machine Learning Approaches. IEEE Bombay Section Signature Conference (IBSSC). Mumbai, India 26 - 28 Jul 2019 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ibssc47189.2019.8973099
AuthorsToraskar, T., Bhangale, U., Patil, S., More, N. and Chaudhari, U.
TypeConference paper
Abstract

In this digital era, the number of Cybercrimes is increasing that has resulted in increased number of pending cybercrimes cases such as artifacts as a malware, hacking and cyber fraud or e-harassment. In order to deal with these cases, digital forensics must include the concrete law enforcement in the court of law. In digital forensics, it is challenging task to detect reliable evidence because of worldwide use and advancements in digital communication technologies. Common approaches such as file signature analysis and the data carving can be done using the forensics tools, however, digital evidence examiners are keen to find the relevant data which helps in finding the truth behind the case. To reduce the examination time in the data examination or analysis process, this paper explores the role of unsupervised pattern recognition to identify the notable artefact. The Self-Organising Map (SOM) is used to automatically cluster notable artefacts. In this work, four cases are presented to demonstrate the use of SOM in examining the digital data saved in a CSV format. Multiple SOMs are created including Extension Mismatch SOM that represents the intentional changes done on the default extension of the file in order to hide it from the forensic examiner. Other types of SOM are created for the EXIF Metadata (i.e. MAC attributes). USB Device Attached (Device Make, Device Model, Device ID, Date/Time, Source File, Tags).

Year2019
Journal2019 IEEE Bombay Section Signature Conference, IBSSC 2019
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Digital Object Identifier (DOI)https://doi.org/10.1109/ibssc47189.2019.8973099
Web address (URL)https://ieeexplore.ieee.org/document/8973099
Accepted author manuscript
License
File Access Level
Open
Publication dates
Online28 Jul 2019
Publication process dates
Deposited19 Aug 2024
ISBN978-1-5386-7401-7
Web address (URL) of conference proceedingshttps://ieeexplore.ieee.org/xpl/conhome/8966556/proceeding
Permalink -

https://openresearch.lsbu.ac.uk/item/97578

Download files


Accepted author manuscript
  • 11
    total views
  • 3
    total downloads
  • 1
    views this month
  • 0
    downloads this month

Export as

Related outputs

Artificial Intelligence and Endo-Histo-OMICs: New Dimensions of Precision Endoscopy and Histology in Inflammatory Bowel Disease
Iacucci, M., Santacroce, G., Zanmarchi, I., Maeda, Y., del Amor, R., Meseguer, P., Kolawole, B., Chaudhari, U., Di Sabatino, A., Danese, S., Mori, Y., Grisan, E., Naranjo, V. and Ghosh, S. (2024). Artificial Intelligence and Endo-Histo-OMICs: New Dimensions of Precision Endoscopy and Histology in Inflammatory Bowel Disease. The Lancet Gastroenterology & Hepatology. 9 (8), pp. 758-772. https://doi.org/10.1016/S2468-1253(24)00053-0