James I. Obuhuma

Work place: Department of Computer Science, Africa Nazarene University, Nairobi, Kenya

E-mail: jobuhuma@gmail.com

Website: https://orcid.org/0000-0002-1360-4562

Research Interests: Information Systems, Systems Architecture, Computer systems and computational processes


James I. Obuhuma is a Computer Science PhD student, School of Computing and Informatics at Maseno University, Kenya. He holds an MSc in Computer Science from the University of Nairobi. His MSc thesis focused on Road Traffic Analysis using GPS Technology that opened his interest in Intelligent Systems, particularly Intelligent Transportation Systems (ITS) that informed his PhD research topic. He is currently a member of faculty, Department of Computer Science, Africa Nazarene University, Kenya. Apart from Computing and Informatics field, he is also a Design Thinking coach. He is part of the Impact Week team that fosters entrepreneurship and innovation through building of sustainable business models.

Author Articles
Multi-Factor Authentication for Improved Enterprise Resource Planning Systems Security

By Carolyne Kimani James I. Obuhuma Emily Roche

DOI: https://doi.org/10.5815/ijitcs.2023.03.04, Pub. Date: 8 Jun. 2023

Universities across the globe have increasingly adopted Enterprise Resource Planning (ERP) systems, a software that provides integrated management of processes and transactions in real-time. These systems contain lots of information hence require secure authentication. Authentication in this case refers to the process of verifying an entity’s or device’s identity, to allow them access to specific resources upon request. However, there have been security and privacy concerns around ERP systems, where only the traditional authentication method of a username and password is commonly used. A password-based authentication approach has weaknesses that can be easily compromised. Cyber-attacks to access these ERP systems have become common to institutions of higher learning and cannot be underestimated as they evolve with emerging technologies. Some universities worldwide have been victims of cyber-attacks which targeted authentication vulnerabilities resulting in damages to the institutions reputations and credibilities. Thus, this research aimed at establishing authentication methods used for ERPs in Kenyan universities, their vulnerabilities, and proposing a solution to improve on ERP system authentication. The study aimed at developing and validating a multi-factor authentication prototype to improve ERP systems security. Multi-factor authentication which combines several authentication factors such as: something the user has, knows, or is, is a new state-of-the-art technology that is being adopted to strengthen systems’ authentication security. This research used an exploratory sequential design that involved a survey of chartered Kenyan Universities, where questionnaires were used to collect data that was later analyzed using descriptive and inferential statistics. Stratified, random and purposive sampling techniques were used to establish the sample size and the target group. The dependent variable for the study was limited to security rating with respect to realization of confidentiality, integrity, availability, and usability while the independent variables were limited to adequacy of security, authentication mechanisms, infrastructure, information security policies, vulnerabilities, and user training. Correlation and regression analysis established vulnerabilities, information security policies, and user training to be having a higher impact on system security. The three variables hence acted as the basis for the proposed multi-factor authentication framework for improve ERP systems security.

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Shortcomings of Ultrasonic Obstacle Detection for Vehicle Driver Assistance and Profiling

By James I. Obuhuma Henry O. Okoyo Sylvester O. McOyowo

DOI: https://doi.org/10.5815/ijitcs.2019.06.04, Pub. Date: 8 Jun. 2019

Obstacle detection is a challenging problem that has attracted much attention recently, especially in the context of research in self-driving car technologies. A number of obstacle detection technologies exist. Ultrasound is among the commonly used technologies due to its low cost compared to other technologies. This paper presents some findings on the research that has been carried out by the authors with regard to vehicle driver assistance and profiling. It discusses an experiment for detection of obstacles in a vehicle driver’s operational environment using ultrasound technology. Experiment results clearly depict the capabilities and limitations of ultrasound technology in detection of obstacles under motion and obstacles with varied surfaces. Ultrasound’s wavelength, beam width, directionality among others are put into consideration. Pros and cons of other technologies that could replace ultrasound, for instance RADAR and LIDAR technologies are also discussed. The study recommends sensor fusion where several types of sensor technologies are combined to complement one another. The study was a technical test of configurable technology that could guide future studies on obstacle detection intending to use infrared, sound, radio or laser technologies particularly when both the sensor and obstacle are in motion and when obstacles have differing unpredictable surface properties.

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Driver Behaviour Profiling Using Dynamic Bayesian Network

By James I. Obuhuma Henry O. Okoyo Sylvester O. McOyowo

DOI: https://doi.org/10.5815/ijmecs.2018.07.05, Pub. Date: 8 Jul. 2018

In the recent past, there has been a rapid increase in the number of vehicles and diversification of road networks worldwide. The biggest challenge now lies on how to monitor and analyse behaviours of vehicle drivers as a catalyst to road safety. Driver behaviour depends on the state and nature of the road, the state of the driver, vehicle conditions, and actions of other road users among other factors. This paper illustrates the ability of Dynamic Bayesian Networks towards determination of driving styles with respect to acceleration, cornering and braking patterns. Bayesian Networks are probabilistic graphical models that map a set of variables and their conditional dependencies. Sample test results showed that the 2-Time-slice Bayesian Network model is suitable for generation of driver profiles using only four GPS data parameters namely speed, altitude, direction and signal strength against time. The model classifies driver profiles into two sets of observations: driver behaviour and nature of operational environment. Adoption of the model could offer a cost effective, easy to implement and use solution that could find many applications in vehicle driver recruiting firms, vehicle insurance companies and transport and road safety authorities among other sectors.

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