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.
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. Mourão-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, "Naïve Bayesian decision model for the
interoperability of heterogeneous systems in an
intelligent building environment". Automation in
construction 54, pp. 83-92, 2015.
