Efficacy of ADDIE model in peer-to-peer networks: Digital evidence investigation

Book chapter


Ahmad Musa, Irfan-Ullah Awan and Ibrahim Abobaker 2021. Efficacy of ADDIE model in peer-to-peer networks: Digital evidence investigation. in: 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud) IEEE.
AuthorsAhmad Musa, Irfan-Ullah Awan and Ibrahim Abobaker
Abstract

While the need for content distribution proliferates
– becoming more mammoth and complex on the Internet – the P2P network perseveres as one of the best avenues to service the
demand for content distribution. It enjoys a wide range of clients that transport data in bits securely, making it susceptible to moving dubious contents, hence becoming exposed to varying security threats that require credible digital investigation to address. The tools and techniques used in performing digital investigations are still mostly lagging, successfully slowing down law enforcement agencies in general. The acquisition of digital evidence over the Internet is still elusive in the battle against cybercrime. This paper considers a new technique for detecting passive peers that participate in a P2P network. As part of our study, we crawled the μTorrent P2P client over 10 days while logging all participating peers. We then employed digital forensic techniques to analyze the popular users and generate evidence within them with high accuracy. Finally, we evaluated our proposed approach against the standard Analysis, Design, Development, Implementation, and Evaluation, or ADDIE model for digital investigation to arrive at the credible digital evidence presented in this paper.

KeywordsPeer-to-peer; Investigation; Digital forensics; ADDIE model; Evidence detection
Year2021
Book title2021 8th International Conference on Future Internet of Things and Cloud (FiCloud)
PublisherIEEE
Output statusPublished
ISBN978166525742
9781665425759
Publication process dates
Deposited23 Jan 2023
Digital Object Identifier (DOI)https://doi.org/10.1109/FiCloud49777.2021.00033
Official URLhttps://ieeexplore.ieee.org/document/9590242
Event 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud)
Permalink -

https://repository.canterbury.ac.uk/item/939y8/efficacy-of-addie-model-in-peer-to-peer-networks-digital-evidence-investigation

  • 31
    total views
  • 0
    total downloads
  • 31
    views this month
  • 0
    downloads this month

Export as

Related outputs

Functional and performance analysis of discrete event network simulation tools
Musa, A. and Awan, I. 2022. Functional and performance analysis of discrete event network simulation tools. Simulation Modelling Practice and Theory. 116, p. 102470. https://doi.org/10.1016/j.simpat.2021.102470
The case for validating ADDIE model as a digital forensic model for peer to peer network investigation
Musa, A., Awan, I-U and Zarah, F. 2022. The case for validating ADDIE model as a digital forensic model for peer to peer network investigation. Information System Frontiers. https://doi.org/10.1007/s10796-022-10360-8
Machine learning for intrusion detection and network performance
Ibrahim Abobaker and Ahmad Musa 2021. Machine learning for intrusion detection and network performance. in: 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud)
Self regulated learning in flipped classrooms: A systematic literature review
Rasheed Abubakar Rasheed, Amirrudin Kamsin, Nor Aniza Abdullah, Habeebah Adamu Kakudi, Auwal Shehu Ali, Ahmad Musa and Adamu Sani Yahaya 2020. Self regulated learning in flipped classrooms: A systematic literature review. International Journal of Information and Education Technology. 10 (11). https://doi.org/10.18178/ijiet.2020.10.11.1469
An investigation into peer-to-peer network security using Wireshark
Ahmad Musa, Aliyu Abubakar, Usman Abdul Gimba and Rasheed Abubakar Rasheed 2019. An investigation into peer-to-peer network security using Wireshark. in: 2019 15th International Conference on Electronics, Computer and Computation (ICECCO) IEEE.
Transfer learning based histopathologic image classification for burns recognition
Aliyu Abubakar, Hassan Ugail, Ali Maina Bukar, Ali Ahmad Aminu and Ahmad Musa 2019. Transfer learning based histopathologic image classification for burns recognition. in: 2019 15th International Conference on Electronics, Computer and Computation (ICECCO) IEEE.