June 2021 - Top 10 Read Articles in Network Security and Its Applications

June 2021: Top 10
Read Articles in
Network Security and
Its Applications
International Journal of Network Security &
Its Applications (IJNSA)
http://airccse.org/journal/ijnsa.html
ISSN: 0974 - 9330 (Online); 0975 - 2307 (Print)

SECURITY & PRIVACY THREATS, ATTACKS AND COUNTERMEASURES IN
INTERNET OF THINGS
Faheem Masoodi1
Shadab Alam2
and Shams Tabrez Siddiqui2
1
Department of Computer Science, University of Kashmir, J&k, India 2
Department of Computer
Science, Jazan University, KSA
ABSTRACT
The idea to connect everything to anything and at any point of time is what vaguely defines the
concept of the Internet of Things (IoT). The IoT is not only about providing connectivity but also
facilitating interaction among these connected things. Though the term IoT was introduced in
1999 but has drawn significant attention during the past few years, the pace at which new
devices are being integrated into the system will profoundly impact the world in a good way but
also poses some severe queries about security and privacy. IoT in its current form is susceptible
to a multitudinous set of attacks. One of the most significant concerns of IoT is to provide
security assurance for the data exchange because data is vulnerable to some attacks by the
attackers at each layer of IoT. The IoT has a layered structure where each layer provides a
service. The security needs vary from layer to layer as each layer serves a different purpose. This
paper aims to analyze the various security and privacy threats related to IoT. Some attacks have
been discussed along with some existing and proposed countermeasures.
KEYWORDS
Internet of Things, privacy, attacks, security, threats, protocols.
For More Details : http://aircconline.com/ijnsa/V11N2/11219ijnsa05.pdf
Volume Link : http://airccse.org/journal/jnsa19_current.html

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PHISHING MITIGATION TECHNIQUES: A LITERATURE SURVEY
Wosah Peace Nmachi and Thomas Win
School of Computing & Engineering University of Gloucestershire, Park Campus, Cheltenham
GL50 2RH United Kingdom
ABSTRACT
Email is a channel of communication which is considered to be a confidential medium of
communication for exchange of information among individuals and organisations. The
confidentiality consideration about e-mail is no longer the case as attackers send malicious
emails to users to deceive them into disclosing their private personal information such as
username, password, and bank card details, etc. In search of a solution to combat phishing
cybercrime attacks, different approaches have been developed. However, the traditional exiting
solutions have been limited in assisting email users to identify phishing emails from legitimate
ones. This paper reveals the different email and website phishing solutions in phishing attack
detection. It first provides a literature analysis of different existing phishing mitigation
approaches. It then provides a discussion on the limitations of the techniques, before concluding
with an explorationin to how phishing detection can be improved.
KEYWORDS
Cyber-security, Phishing Email Attack, Deep Learning, Stylometric Analysis, Cyber Human
Behaviour
For More Details : https://aircconline.com/ijnsa/V13N2/13221ijnsa05.pdf

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A CONCEPTUAL SECURE BLOCKCHAIN-BASED ELECTRONIC
VOTING SYSTEM
Ahmed Ben Ayed
Department of Engineering and Computer Science, Colorado Technical University, Colorado
Springs, Colorado, USA
ABSTRACT
Blockchain is offering new opportunities to develop new types of digital services. While research
on the topic is still emerging, it has mostly focused on the technical and legal issues instead of
taking advantage of this novel concept and creating advanced digital services. In this paper, we
are going to leverage the open source Blockchain technology to propose a design for a new
electronic voting system that could be used in local or national elections. The Blockchain-based
system will be secure, reliable, and anonymous, and will help increase the number of voters as
well as the trust of people in their governments.
KEYWORDS
Blockchain, Electronic Voting System, e-Voting, I-Voting, iVote

REFERENCES
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AUTHORS
Ahmed Ben Ayed, has received his Bachelor of Science in Computer Information Systems,
Master of Science in Cyber Security and Information Assurance, and currently a doctoral student
at Colorado Technical University, and an Adjunct Professor at California Takshila University.
His research interests are Android Security, Pattern Recognition of Malicious Applications,
Machine Learning, Cryptography, Information & System Security and Cyber Security.

A LITERATURE SURVEY AND ANALYSIS ON SOCIAL ENGINEERING DEFENSE
MECHANISMS AND INFOSEC POLICIES
Dalal Alharthi and Amelia Regan
Department of Computer Science, University of California Irvine, Irvine, California
ABSTRACT
Social engineering attacks can be severe and hard to detect. Therefore, to prevent such attacks,
organizations should be aware of social engineering defense mechanisms and security policies.
To that end, the authors developed a taxonomy of social engineering defense mechanisms,
designed a survey to measure employee awareness of these mechanisms, proposed a model of
Social Engineering InfoSec Policies (SE-IPs), and designed a survey to measure the
incorporation level of these SE-IPs. After analyzing the data from the first survey, the authors
found that more than half of employees are not aware of social engineering attacks. The paper
also analyzed a second set of survey data, which found that on average, organizations
incorporated just over fifty percent of the identified formal SE-IPs. Such worrisome results show
that organizations are vulnerable to social engineering attacks, and serious steps need to be taken
to elevate awareness against these emerging security threats.
KEYWORDS
Cybersecurity, Social Engineering, Employee Awareness, Defense Mechanisms, Security
Policies

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AUTHORS
Dalal Alharthi is a Ph.D. Candidate in Computer Science at the University
of California, Irvine. She is also a Resident Engineer at Palo Alto Networks
and a Senior Prisma Cloud Consultant at Dell. She is equipped with 12+
years of work experience between academia and industry. Her research
interests are in the field of Cybersecurity, Network Security, Cloud Security,
Privacy, Human-Computer Interaction (HCI), and Artificial Intelligence
(AI).
Amelia Regan received a BAS in Systems Engineering from the University
of Pennsylvania, an MS degree in Applied Mathematics from Johns Hopkins
University, and an MSE degree and Ph.D. degree at the University of Texas.
She is a Professor of Computer Science at the University of California,
Irvine. Her research interests include network optimization, cyber-physical
transportation systems, machine learning tools for temporal-spatial data
analysis, and cybersecurity.

COMPARISON OF MALWARE CLASSIFICATION METHODS USING CONVOLUTIONAL
NEURAL NETWORK BASED ON API CALL STREAM
Matthew Schofield1
, Gulsum Alicioglu2
, Bo Sun1
, Russell Binaco1
, Paul Turner1
, Cameron
Thatcher1
, Alex Lam1
and Anthony Breitzman1
1
Department of Computer Science, Rowan University, Glassboro, New Jersey, USA
2
Department of Electrical and Computer Engineering, Rowan University, Glassboro, New
Jersey, USA
ABSTRACT
Malicious software is constantly being developed and improved, so detection and classification
of malwareis an ever-evolving problem. Since traditional malware detection techniques fail to
detect new/unknown malware, machine learning algorithms have been used to overcome this
disadvantage. We present a Convolutional Neural Network (CNN) for malware type
classification based on the API (Application Program Interface) calls. This research uses a
database of 7107 instances of API call streams and 8 different malware types:Adware, Backdoor,
Downloader, Dropper, Spyware, Trojan, Virus,Worm. We used a 1-Dimensional CNN by
mapping API calls as categorical and term frequency-inverse document frequency (TF-IDF)
vectors and compared the results to other classification techniques.The proposed 1-D CNN
outperformed other classification techniques with 91% overall accuracy for both categorical and
TFIDF vectors.
KEYWORDS
Convolutional Neural Network, Malware Classification, N-gram Analysis, Term Frequency-
Inverse Document Frequency Vectors, Windows API Calls.

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AUTHORS
Matthew Schofield is currently enrolled at Rowan University pursuing his
B.S/M.S degree in Computer Science anticipating graduation in December
2021. He is currently working on his master’s thesis on Deep Reinforcement
Learning in Incentivization Systems. His research interests are in Machine
Learning and Deep Reinforcement Learning.
Gulsum Alicioglu received M.Sc. Degree in Industrial Engineering from Gazi
University, Turkey, in 2018. Currently, she is a Ph.D. candidate at the
Department of Electrical and Computer Engineering of Rowan University,
USA. Her research interests aredata visualization, machine learning, and
explainable artificial intelligence.

Bo Sun is an associate professor of Computer Science and led the project effort
of this paper.She received her B.S. in Computer Science from Wuhan
University, her M.S.in Computer Science from Lamar University, and her
Ph.D. in Modeling and Simulation from Old Dominion University. Her research
interests include Visual Analytics and Data Visualization.
Russell Binaco graduated from Rowan University with an M.S. in Computer
Science in Spring 2020. He now works as a software engineer for Innovative
Defense Technologies, and as an adjunct for Rowan University. At Rowan, he
earned undergraduate degrees in Computer Science and Electrical and
Computer Engineering. He has also been published in the Journal of the
International Neuropsychological Society for research using Machine Learning
to classify patients’ levels of cognitive decline with regards to Alzheimer’s
Disease.
Paul Turner received his B.S. in Computer Science from Rowan University in
2018 and is currently enrolled in an M.S. program at the aforementioned
University. His interests include machine learning, text mining, and cloud
computing.
Cameron Thatcher received his B.S in Computer Science from Rowan
University in 2019 and is currently pursuing his M.S. in Computer Science at
Rowan University. His research interests include Machine Learning and Data
Mining.
Alex Lam is currently attending Rowan University pursuing his B.S/M.S
degree in Computer Science and Data Analytics. He has also been published in
the 3rd ACM SIGSPATIAL International Workshop on Analytics for Local
Events and News (LENS’19) for research in identifying real-world events
using bike-sharing data.
Anthony Breitzman holds an M.A. in Mathematics from Temple University,
and an M.S. and Ph.D. from Drexel University. He is an associate professor of
Computer Science at Rowan University and his research interests are Data
Mining, Text Mining, Machine Learning, Algorithm Design, Convolution
Algorithms, and Number Theory.

EFFECT MAN-IN THE MIDDLE ON THE NETWORK PERFORMANCE IN VARIOUS
ATTACK STRATEGIES
Iyas Alodat
Department of Computer and Information System, Jerash University, Jerash, Jordan
ABSTRACT
In this paper, we examined the effect on network performance of the various strategies an
attacker could adopt to launch Man-In The Middle (MITM) attacks on the wireless network,
such as fleet or random strategies. In particular, we're focusing on some of those goals for MITM
attackers - message delay, message dropping. According to simulation data, these attacks have a
significant effect on legitimate nodes in the network, causing vast amounts of infected packets,
end-to-end delays, and significant packet loss.
KEYWORDS
Wireless Network, Mobile Network, security; Man-In-The-Middle Attack; smart cities;
simulation; Intelligent Transportation System; Internet-of-Things.

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MINING PATTERNS OF SEQUENTIAL MALICIOUS APIS TO DETECT MALWARE
Abdurrahman Pektaş1
, Elif Nurdan Pektaş2
and Tankut Acarman1
1
Department of Computer Engineering, Galatasaray University, İstanbul, Turkey 2
Siemens
Turkey, Yakack Caddesi No: 111, 34870 Kartal, Istanbul, Turkey
ABSTRACT
In the era of information technology and connected world, detecting malware has been a major
security concern for individuals, companies and even for states. The New generation of malware
samples upgraded with advanced protection mechanism such as packing, and obfuscation
frustrate anti-virus solutions. API call analysis is used to identify suspicious malicious behavior
thanks to its description capability of a software functionality. In this paper, we propose an
effective and efficient malware detection method that uses sequential pattern mining algorithm to
discover representative and discriminative API call patterns. Then, we apply three machine
learning algorithms to classify malware samples. Based on the experimental results, the proposed
method assures favorable results with 0.999 F-measure on a dataset including 8152 malware
samples belonging to 16 families and 523 benign samples.
KEYWORDS
Android, Malware, Frequent Sequence Mining, Behavioural Pattern, API Calls, Dynamic
Analysis

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learning research, Vol. 12, pp.2825–2830.
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AUTHORS
Abdurrahman Pektaş received his B.Sc. and M Sc. at Galatasaray University
and his PhD at the University of Joseph Fourier, all in computer engineering, in
2009, 2012 and 2015, respectively. He is a senior researcher at Galatasaray
University. His research interests are analysis, detection and classification of
malicious software, machine learning and security analysis tool development.
Elif Nurdan Pektaş received his B.Sc. and M Sc. at Galatasaray University all
in computer engineering, in 2010, and 2014, respectively. She is leading
software developer at Siemens Turkey. Her research interests are developing
IoT based applications, deep learning, cloud based application and automated
testing.
Tankut Acarman received his Ph.D. degree in Electrical and Computer
engineering from the Ohio State University in 2002. He is professor and head of
computer engineering department at Galatasaray University in Istanbul, Turkey.
His research interests lie along all aspects of autonomous s ystems, intelligent
vehicle technologies and security. He is the co-author of the book entitled
“Autonomous Ground.

HYBRIDIZATION OF DCT BASED STEGANOGRAPHY AND RANDOM GRIDS
Pratarshi Saha1
, Sandeep Gurung2
and Kunal Krishanu Ghose3
1,2
Department of Computer Science & Engineering, Sikkim Manipal Institute of Technology,
Majhitar, Sikkim, India
3
QualComm, Sandiego, CA, USA
ABSTRACT
With the increasing popularity of information technology in communication network, security
has become an inseparable but vital issue for providing for confidentiality, data security, entity
authentication and data origin authentication. Steganography is the scheme of hiding data into a
cover media to provide confidentiality and secrecy without risking suspicion of an intruder.
Visual cryptography is a new technique which provides information security using simple
algorithm unlike the complex, computationally intensive algorithms used in other techniques like
traditional cryptography. This technique allows visual information to be encrypted in such a way
that their decryption can be performed by the Human Visual System (HVS), without any
complex cryptographic algorithms. To provide a better secured system that ensures high data
capacity and information security, a multilevel security system can be thought for which can be
built by incorporating the principles of steganography and visual cryptography.
KEYWORDS
Data Security, DCT based Steganography, Random Grids, Visual Cryptography, Hybrid
For More Details : http://airccse.org/journal/nsa/5413nsa13.pdf

REFERENCES
[1] Ahmad Movahedian Attar, Isfahan University of Technology, Omid Taheri, Isfahan
University of Technology, Saeid Sadri, Isfahan University of Technology, Mohammad Javad
Omidi, Isfahan University of Technology,” Data Hiding in Halftone Images Using Error
Diffusion Half toning Method with Adaptive Thresholding”, 2006,pp. 2.
[2] Adi Shamir and Moni Naor, “Visual Cryptography”, 1964, pp. 1-2, 3-5.
[3] Hardik Patel and Preeti Dave, “Steganography Technique based on DCT Coefficients”, Jan –
Feb 2012, International Journal of Engineering Research and Applications, Vol 2, Issue 1,pp
713-717, www.ijera.com.
[4] Jonathan Weir and Wei Qi Yan Queen’s University Belfast, Belfast, BT7 1NN,UK,A, 2010,
“Comprehensive Study of Visual Cryptography”, pp. 70.
[5] Kafri, O., Keren, E., “Encryption of pictures and shapes by Random Grids.” Optics,Letters,
1987, 377–379.
[6] Shyong Jian Shyu , Department of Computer Science and Information Engineering, Ming
Chuan University, 5 Der Ming Rd, Gawi Shan, Taoyuan 333,Taiwan, ROC. “Image
Encryption by Random Grids”, 2006, The Journal of Pattern Recognition Society,
www.sciencedirect.com.
[7] Tzung-Her Chen, Kai-Hsiang Tsao Department of Computer Science and Information
Engineering, National Chiayi University, 300 University Rd., Chiayi City60004, Taiwan,
“Threshold Visual Secret Sharing using Random Grids”,2011, pp. 1198.
AUTHORS
First Author:-
Pratarshi Saha is a Final year student in the Department of Computer Science and Engineering
at Sikkim Manipal Institute of Technology, Mazitar, Sikkim, India. He subject of interests are
Computer and Information Security, Design and Analysis of Algorithms and Computer
Networks.
Second Author:-
Sandeep Gurung received his M. Tech degree in Computer Science and Engineering from the
Sikkim Manipal University in 2009 and is currently pursuing his Ph.D. degree in Computer
Science and Engineering. He is a Assistant Professor in the Department of Computer Science at
Sikkim Manipal Institute of Technology, Mazitar, Sikkim, India. His research interests include
Computer Networks, Cryptography, Distributed Systems and Soft Computing.

Third Author:-
Kunal Krishanu Ghose did his MS (Engg.) in Electrical and Communication Engineering with
specialization Wireless Sensor Network from University at Buffalo, NY, USA in 2009 and B.
Tech (ECE) from NIT Durgapur, INDIA in 2006. After completion of B. Tech, he joined as a
System Engineer in Aricent (Hughes Software System), Chennai for a year in 2007. Presently, he
is working in Qualcomm Inc., Sandiego, CA, USA as a Sr. Engineer in Architecture
Performance Department, looking after the Quad core processor technology. His areas of
research interest are Mobile Network, Communications, and Cryptography.

PERFORMANCE EVALUATION OF MACHINE LEARNING TECHNIQUES FOR DOS
DETECTION IN WIRELESS SENSOR NETWORK
Lama Alsulaiman and Saad Al-Ahmadi
Department of Computer Science, King Saud University, Riyadh, Saudi Arabia
ABSTRACT
The nature of Wireless Sensor Networks (WSN) and the widespread of using WSN introduce
many security threats and attacks. An effective Intrusion Detection System (IDS) should be used
to detect attacks. Detecting such an attack is challenging, especially the detection of Denial of
Service (DoS) attacks. Machine learning classification techniques have been used as an approach
for DoS detection. This paper conducted an experiment using Waikato Environment for
Knowledge Analysis (WEKA)to evaluate the efficiency of five machine learning algorithms for
detecting flooding, grayhole, blackhole, and scheduling at DoS attacks in WSNs. The evaluation
is based on a dataset, called WSN-DS. The results showed that the random forest classifier
outperforms the other classifiers with an accuracy of 99.72%.
KEYWORDS
Wireless Sensor Networks, Machine Learning, Denial of Service

REFERENCES
[1] N. A. A. Aziz and K. A. Aziz, “Managing disaster with wireless sensor networks,” in
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[2] I. Almomani, B. Al-Kasasbeh, and M. Al-Akhras, “WSN-DS: A Dataset for Intrusion
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Attacks on Wireless Sensor Networks Revisited,” in Proceedings - Conference on Local
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10.1109/LCN.2017.110.
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10.1049/iet-com.2019.0172.
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Venkatraman, “Deep Learning Approach for Intelligent Intrusion Detection System,” IEEE
Access, 2019, doi: 10.1109/ACCESS.2019.2895334.

[11] B. Riyaz and S. Ganapathy, “A deep learning approach for effective intrusion detection in
wireless networks using CNN,” Soft Comput., vol. 24, no. 22, pp. 17265–17278, 2020, doi:
10.1007/s00500-020-05017-0.
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[16] G. Holmes, A. Donkin, and I. H. Witten, “WEKA: A machine learning workbench,” 1994,
doi: 10.1109/anziis.1994.396988.
AUTHORS
Lama Alsulaiman received her bachelor's degree in Computer Science from Imam Muhammad
ibn Saud Islamic University. She is currently pursuing the masterdegree in the Computer Science
program at King Saud University. Her research interests are mainly in the field of Computer
Networks, Networks Security, Software-Defined Networks.
Saad Al-Ahmadi received the MS and Ph.D. degree in computer science
from King Saud University, Saudi Arabia. He is an Associate Professor in the
Department of Computer Science, King Saud University. Also, he serves as a
part-time consultant in many public and private organizations. His current
research interests include Cybersecurity, IoT, machine learning for healthcare,
and future generation networks.

APPLYING THE HEALTH BELIEF MODEL TO CARDIAC IMPLANTED MEDICAL DEVICE
PATIENTS
George W. Jackson1
and Shawon Rahman2
1
College of Business and Technology, Capella University, Minneapolis, USA
2
Professor, Dept.of Computer Science & Engineering, University of Hawaii-Hilo,
200W.KawiliStreet, Hilo, HI96720, USA
ABSTRACT
Wireless Implanted Medical Devices (WIMD) are helping millions of users experience a better
quality of life. Because of their many benefits, these devices are experiencing dramatic growth in
usage, application, and complexity. However, this rapid growth has precipitated an equally rapid
growth of cybersecurity risks and threats. While it is apparent from the literature WIMD
cybersecurity is a shared responsibility among manufacturers, healthcare providers, and patients;
what explained what role patients should play in WIMD cybersecurity and how patients should
be empowered to assume this role. The health belief model (HBM) was applied as the theoretical
framework for a multiple case study which examined the question: How are the cybersecurity
risks and threats related to wireless implanted medical devices being communicated to patients
who have or will have these devices implanted in their bodies? The subjects of this multiple case
study were sixteen cardiac device specialists in the U.S., each possessing at least one year of
experience working directly with cardiac implanted medical device (CIMD) patients, who
actively used cardiac device home monitoring systems. The HBM provides a systematic
framework suitable for the proposed research. Because of its six-decade history of validity and
its extraordinary versatility, the health belief model, more efficiently than any other model
considered, provides a context for understanding and interpreting the results of this study. Thus,
the theoretical contribution of this research is to apply the HBM in a setting where it has never
been applied before, WIMD patient cybersecurity awareness. This analysis (using a multiple case
study) will demonstrate how the HBM can assist the health practitioners, regulators,
manufacturers, security practitioners, and the research community in better understanding the
factors, which support WIMD patient cybersecurity awareness and subsequent adherence to
cybersecurity best practices.
KEYWORDS
Health Belief Model, Healthcare Cybersecurity, Cardiac Implanted Device, Wireless Implanted
Medical Devices, WIMD, WIMD cybersecurity

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[6] Davinson, N., & Sillence, E. (2014). Using the health belief model to explore users'
perceptions of ‘being safe and secure’ in the world of technology-mediated financial
transactions. International Journal of Human-Computer Studies, 72(2), 154-168.
[7] Khan, B., Alghathbar, K. S., Nabi, S. I., & Khan, M. K. (2011). The effectiveness of
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Business Management, 5(26), 10862-10868.
[8] Marton, C., & Chun, W. C. (2012). A review of theoretical models of health information
seeking on the web. Journal of Documentation, 68(3), 330-352.
[9] Herath, T., & Rao, H. R. (2009). Protection motivation and deterrence: A framework for
security policy compliance in organizations. European Journal of Information Systems,
18(2), 106-125.
[10] Armitage, C. J., & Conner, M. (2000). Social cognition models and health behaviour: A
structured review. Psychology and health, 15(2), 173-189.
[11] Camara, C., Peris-Lopez, P., & Tapiador, J.E. (2015). Security and privacy issues in
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55, 272-289.
[12] Denning, T., Borning, A., Friedman, B., Gill, B. T., Kohno, T., & Maisel, W. H. (2010).
Patients, pacemakers, and implantable defibrillators: Human values and security for wireless
implantable medical devices. Proceedings of the SIGCHI Conference on Human Factors in
Computing Systems, 917-926.

[13] Fu, K. (2009). Inside risks: Reducing risks of implantable medical devices. Communications
of the ACM, 52(6), 25-27.
[14] Fu, K., & Blum, J. (2013). Controlling for cybersecurity risks of medical device software.
Communications of the ACM, 56(10), 35-37
[15] Kotz, D. (2011). A threat taxonomy for mHealth privacy. Proceedings of Third International
Conference on Communication Systems and Network (COMSNETS), 1-6.
[16] Leavitt, N. (2010). Researchers fight to keep implanted medical devices safe from hackers.
Computer, 43(8), 11-14.
[17] Ray, A., Jones, P., & Zhang, Y. (2013). Medical device security-A new frontier. Biomedical
Instrumentation & Technology, 47(1), 72-72.
[18] Sametinger, J., Rozenblit, J., Lysecky, R., & Ott, P. (2015). Security Challenges for Medical
Devices. Communications of the ACM, 58(4), 74-82.
[19] Williams, P. A., & Woodward, A. J. (2015). Cybersecurity vulnerabilities in medical
devices: a complex environment and multifaceted problem. Medical Devices: Evidence and
Research, 8, 305–316.
[20] Middaugh, D. J. (2016). Do security flaws put your patients' health at risk? MedSurg
Nursing, 25(2), 131-133.
[21] Boulos, P., Sargolzaei, A., Ziaei, A., & Sargolzaei, S. (2016). Pacemakers: A Survey on
Development History, Cyber-Security Threats, and Countermeasures.
[22] Perakslis, E. D. (2014). Cybersecurity in health care. New England Journal of Medicine,
371(5), 395-397.
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Diabetes Science and Technology,
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[28] Klonoff, D. C. (2015). Cybersecurity for connected diabetes devices. Journal of diabetes
science and technology.
[29] Rushanan, M., Rubin, A. D., Kune, D. F., & Swanson, C. M. (2014). SoK: Security and
privacy in implantable medical devices and body area networks. Proceedings of IEEE
Security and Privacy 2014 Symposium, 524-539.
[30] Wirth, A. (2011). Cybercrimes pose growing threat to medical devices. Biomedical
Instrumentation & Technology, 45(1), 26-34.
[31] Hansen, J. A., & Hansen, N. M. (2010). A taxonomy of vulnerabilities in implantable
medical devices. In Proceedings of the Second Annual Workshop On Security and Privacy in
Medical and Home-Care Systems, 13-20/
[32] Murphy, S. (2015). Is cyber security possible in healthcare? National Cybersecurity Institute
Journal,1(3)49-63.
[33] Gupta, S. (2012). Implantable Medical Devices-Cyber Risks and Mitigation Approaches. In
Proceedings of the Cybersecurity in Cyber-Physical Workshop, The National Institute of
Standards and Technology (NIST), US.
[34] Ellouze, N., Rekhis, S., Boudriga, N., & Allouche, M. (2017). Cardiac Implantable Medical
Devices forensics: Postmortem analysis of lethal attacks scenarios. Digital Investigation, 21,
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[35] Halperin, D., Heydt-Benjamin, T. S., Fu, K., Kohno, T., & Maisel, W. H. (2008). Security
and privacy for implantable medical devices. IEEE pervasive computing, 7(1), 30-39.
[36] Burleson, W., Clark, S. S., Ransford, B., & Fu, K. (2012, June). Design challenges for
secure implantable medical devices. In Proceedings of the 49th Annual Design Automation
Conference (pp. 12-17). ACM.
[37] Rostami, M., Burleson, W., Koushanfar, F., & Juels, A. (2013, May). Balancing security
and utility in medical devices? In Proceedings of the 50th Annual Design Automation
Conference (p. 13). ACM.
[38] Faizi, Salman and Rahman, Shawon;” Securing Cloud Computing Through IT
Governance”; International Journal of Information Technology in Industry (ITII), vol. 7,
no.1, 2019, Pages: 1-14
[39] Jackson, George and Rahman, Shawon; “Exploring Challenges and Opportunities in
Cybersecurity Risk and Threat Communications related to the Medical Internet of Things
(MIoT)”, International Journal of Network Security & Its Applications (IJNSA), Vol. 11,
No.4, July 2019.

[40] Loukaka, Alain and Rahman, Shawon; “Discovering New Cyber Protection Approaches
From a Security Professional Prospective”; International Journal of Computer Networks &
Communications (IJCNC) Vol.9, No.4, July 2017
[41] Al-Mamun, Abdullah, Rahman, Shawon and et al;“ Security Analysis of AES and
Enhancing its Security by Modifying S-Box with an Additional Byte ”; International Journal
of Computer Networks & Communications (IJCNC), Vol.9, No.2, March 2017
[42] Opala, Omondi John; Rahman, Shawon; and Alelaiwi, Abdulhameed; “The Influence of
Information Security on the Adoption of Cloud computing: An Exploratory Analysis”,
International Journal of Computer Networks & Communications (IJCNC), Vol.7, No.4, July
2015
[43] Faizi, Salman and Rahman, Shawon; “Secured Cloud for Enterprise Computing”; 34th
International Conference on Computers and Their Applications (CATA-2019), March 18-20,
2019, Waikiki Beach Marriott Resort & Spa, Honolulu, Hawaii, USA
[44] Faizi, Salman and Rahman, Shawon; “Choosing the Best-fit Lifecycle Framework while
Addressing Functionality and Security Issues”; 34th International Conference on Computers
and Their Applications (CATA-2019), March 18-20, 2019, Waikiki Beach Marriott Resort &
Spa, Honolulu, Hawaii, USA
[45] Schneider, Marvin and Rahman, Shawon “Protection Motivation Theory Factors that
Influence Undergraduates to Adopt Smartphone Security Measures ”; International Journal of
Information Technology in Industry (ITII), Vol 9, No 1 (2021)
AUTHORS
Dr. George W. Jackson, Jr. has earned a Ph.D. in Information Technology,
Information Assurance, and Cybersecurity degree from Capella University. He has
nearly 30 years of experience in Information Technology, Information Security,
and IT Project Management. A seasoned business and technology expert
possessing an MBA and IT Management and professional certifications in Project
Management, Information Security, and Healthcare Information Security and
Privacy.
Dr. Shawon S. M. Rahman is a Professor of Computer Science at the University
of Hawaii-Hilo and a part-time faculty of Information Technology, Information
Assurance and Security Program at the Capella University. Dr. Rahman’s research
interests include software engineering education, information assurance and
security, digital forensics, web accessibility, cloud computing, and software testing
and quality assurance. He has published over 125 peer reviewed articles in various
international journals, conferences, and books. He is an active member of many
professional organizations including IEEE, ACM, ASEE, ASQ, and UPE.

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