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  Intrinsic Forensic Obscurity of Solid State Drives and Impact on Digital Evidence Recovery  
  Authors : Song Shombot Emmanuel; Ameer Al-Nemrat; Shehu Mohammed Ahmed
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This research explores the challenges posed by solid state drives (SSD) n digital forensics. Evidence acquisition has never been this stochastic with hard disk drives (HDD) because, they have been the most dominant storage devices since the 1950s. This is why most of the legal and technical guidelines for conducting acceptable forensics are built around hard disk drives. Today, the proliferation of solid state drives in the market has caused unforeseeable turbulence with substantial impact on the way non volatile memory forensics are conducted. This research thoroughly investigates solid state drives architecture through literature review and practical usability, to understand its functionality so that the opportunity for learning can be identified, and a solution can be proposed to bridge the knowledge gap in forensic practice. Series of experiments were conducted and results indicates that solid state drives are structurally different from hard disk drives thus, established forensic guidelines cannot be fully adoptable when dealing with solid state drives.


Published In : IJCSN Journal Volume 9, Issue 3

Date of Publication : June 2020

Pages : 123-129

Figures :02

Tables : 07


Song Shombot Emmanuel : graduated from the University of East London with a Bachelor's degree in Software Engineering in 2012 and a Master's degree in Information Security and Digital Forensics in 2017. He is currently working as a Lecturer in Federal University Lafia, Nigeria. S.S. Emmanuel has been a constant recipient of the Dean's list award of academic excellence in 4 consecutive semesters during his Undergraduate years and also won the Tertiary Education Trust Fund Award (TETFUND in 2016. The author's current research interest is IoT and Big Data Analytics.

Dr. Ameer Al Nemrat : is a senior lecturer at the School of Architecture, Computing and Engineering, University of East London (UEL). He also the Director of Professional Doctorate in Information Security & the MSc Information Security and Digital Forensics programmes. In addition, Ameer i s the founder and the director of the Electronic Evidence Laboratory, UEL. Ameer is a research active in the area of cybercrime and digital forensics where he has been publishing research papers in peer reviewed conferences and internationally reputed jour nals. He is a co editor of the book "Issues in Cybercrime, Security, and Digital Forensics". He also was the guest editor of the special issue of the International Journal of Electronic Security and Digital Forensics (IJESDF). Ameer has led a Cybercrime Pr ogramme Project with a German institution, which won for the second time in a row, the "Good Practice Award" from The European Commission under the Leonardo da Vinci scheme which focuses on the teaching and training needs of those involved in vocati onal ed ucation and training." Ameer was also nominated as the "Best Lecturer" for 2015 Student Led Teaching Awards Spotlight on Great Teaching 2015.

Shehu Mohammed Ahmed : is a graduate from Federal University of Technology Minna with bachelor's of Technology in Mathematics and Computer Science 2010 and Master's of Technology in Mathematics 2016. He is currently working as Lecturer in Department of Computer Science Federal University of Lafia. The Authors current research area is Algorithms and Computational mathematics.


Heart disease, NaÔve Bayes algorithm, Feature selection, Classification

We have presented a new framework, MFES-NB, to address the issue of heart disease diagnosis. The model introduces a number of experiments to evaluate its performance. This system can help medical practitioner in efficient decision making based on the given parameter. We have train and test the system using a stratified 10- folds cross validation and obtained an accuracy score of 72%. This model demonstrates promising result and gives the patient to have early detection of heart disease presence.


[1] A. L. Bui, T. B. Horwich, and G. C. Fonarow, "Epidemiology and risk profile of heart failure," Nature Reviews Cardiology, vol. 8, no. 1, pp. 30-41, 2011. [2] J. Mour„o-Miranda, A. L. W. Bokde, C. Born, H. Hampel, and M. Stetter, "Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data," NeuroImage, vol. 28, no. 4, pp. 980-995, 2005. [3] S. Ghwanmeh, A. Mohammad, and A. Al-Ibrahim, "Innovative artificial neural networks-based decision support system for heart diseases diagnosis," Journal of Intelligent Learning Systems and Applications, vol. 5, no. 3, pp. 176-183, 2013. [4] Q. K. Al-Shayea, "Artificial neural networks in medical diagnosis," International Journal of Computer Science Issues, vol. 8, no. 2, pp. 150-154, 2011. [5] R. Kavitha, & E. Kannan, "An efficient framework for heart disease classification using feature extraction and feature selection technique in data mining". International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), Pudukkottai, pp. 1-5, 2016. [6] A. K. Paul, P. C. Shill, M. R. I. Rabin, and M. A. H. Akhand, "Genetic algorithm based fuzzy decision support system for the diagnosis of heart disease". In 5th International Conference on Informatics, Electronics and Vision (ICIEV), pp. 145-150. IEEE, 2016. [7] A. Dey, J. Singh, N. Singh, "Analysis of Supervised Machine Learning Algorithms for Heart Disease Prediction with Reduced Number of Attributes using Principal Component Analysis". Analytics. 140(2), 27- 31, 2016. [8] S. A. Mostafa, A. Mustapha, M. A. Mohammed, M. S. Ahmad, & M. A. Mahmoud, "A fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring application". International Journal of Medical Informatics, 112, 173-184, 2018. [9] A. Tatu, G. Albuquerque, M. Eisemann, J. Schneidewind, H. Theisel, M. Magnor, & D. Keim, "Combining automated analysis and visualization techniques for effective exploration of highdimensional data". In Visual Analytics Science and Technology, VAST IEEE Symposium on (pp. 59-66), 2009. [10] A. Ozcift, & A. Gulten, "Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms". Computer Methods and Programs in Biomedicine, 104(3), 443-451, 2011. [11] A. U. Haq, J. P. Li, M. H. Memon, S. Nazir, and R. Sun, Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms," Mobile Information Systems, vol. 2018, 2018. [12] X. Liu, X. Wang, Q. Su, M. Zhang, Y. Zhu, Q. Wang, et al., "A hybrid classification system for heart disease diagnosis based on the RFRS method," Computational and mathematical methods in medicine, vol. 2017, 2017. [13] S. U. Amin, K. Agarwal, and R. Beg, "Genetic neural network based data mining in prediction of heart disease using risk factors," in 2013 IEEE Conference on Information & Communication Technologies, pp. 1227-1231, 2013. [14] M. Anbarasi, E. Anupriya, and N. Iyengar, "Enhanced prediction of heart disease with feature subset selection using genetic algorithm," International Journal of Engineering Science and Technology, vol. 2, pp. 5370- 5376, 2010. [15] N. Bhatla and K. Jyoti, "An analysis of heart disease prediction using different data mining techniques," International Journal of Engineering, vol. 1, pp. 1-4, 2012. [17] A. Shahi, M. N. Suleiman, M. N., N. Mustapha and T. Perumal, "NaÔve Bayesian decision model for the interoperability of heterogeneous systems in an intelligent building environment". Automation in construction 54, pp. 83-92, 2015.