Computing in Kent: December 2022

Thursday, 15 December 2022

Forensics for peer-to-peer network

Title

The case for validating ADDIE model as a digital forensic model for peer to peer network investigation

Authors

Musa, A., Awan, I-U and Zarah, F.

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

Rapid technological advancement can substantially impact the processes of digital forensic investigation and present a myriad of challenges to the investigator. With these challenges, it is necessary to have a standard digital forensic framework as the foundation of any digital investigation. State-of-the-art digital forensic models assume that it is safe to move from one investigation stage to the next. It guides the investigators with the required steps and procedures. This brings a great stride to validate a non-specific framework to be used in most digital investigation procedures. This paper considers a new technique for detecting active peers that participate in a peer-to-peer (P2P) network. As part of our study, we crawled the μTorrent P2P client over ten days in different instances while logging all participating peers. We then employed digital forensic techniques to analyse the popular users and generate evidence within them with high accuracy. We evaluated our approach against the standard Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model for the digital investigation to achieve the credible digital evidence presented in this paper. Finally, we presented a validation case for the ADDIE model using the United States Daubert Test and the United Kingdom’s Forensic Science Regulator Guidance – 218 (FSR-G-218) and Forensic Science Regulator Guidance – 201 (FSR-G-201) to formulate it as a standard digital forensic model

Tuesday, 13 December 2022

Using voice to keep awake

A Recent paper on using Voice Recognition and machine learning to detect drowsiness when driving.

Jasim, S. S., Abdul Hassan , A. K. and Turner , S. (2022) “Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Voice Recognition”, ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 10(2), pp. 142-151. doi: 10.14500/aro.11000.

Full text at: https://aro.koyauniversity.org/index.php/aro/article/view/1000

Globally, drowsiness detection prevents accidents. Blood biochemicals, brain impulses, etc., can measure tiredness. However, due to user discomfort, these approaches are challenging to implement. This article describes a voice-based drowsiness detection system and shows how to detect driver fatigue before it hampers driving. A neural network and Gray Wolf Optimizer are used to classify sleepiness automatically. The recommended approach is evaluated in alert and sleep-deprived states on the driver tiredness detection voice real dataset. The approach used in speech recognition is mel-frequency cepstral coefficients (MFCCs) and linear prediction coefficients (LPCs). The SVM algorithm has the lowest accuracy (71.8%) compared to the typical neural network. GWOANN employs 13-9-7-5 and 30-20-13-7 neurons in hidden layers, where the GWOANN technique had 86.96% and 90.05% accuracy, respectively, whereas the ANN model achieved 82.50% and 85.27% accuracy, respectively.

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Thursday, 8 December 2022

Disinfomation and Zoombombing

To celebrate Canterbury Christ Church University's (CCCU) 60th Anniversary a Medium site focusing on some the science, technology activities at CCCU, has been set up https://medium.com/the-christ-church-science-a-to-z . Among this two posts have been specifically produced by the Computing team.

"Laptop Computer" by WDnet Studio is marked with CC0 1.0 .

D is Disinformation (https://medium.com/the-christ-church-science-a-to-z/d-is-for-disinformation-1415366ebfc7)

This focuses on an approach that integrates Artificial Intelligence into the investigation and detection of disinformation in text sources.

Z is Zoombombing (https://medium.com/the-christ-church-science-a-to-z/z-is-for-zoombombing-18de82ab3b6e )

Recent investigations at Canterbury Christ Church University reports how forensic evidence from two popular video conferencing tools, Microsoft Teams and Google Meet, could be collected by forensic examiners, and how these artefacts can be used as evidence.