School of Technology

Permanent URI for this collectionhttp://192.168.8.146:4000/handle/123456789/18

Browse

Now showing 1 - 20 of 125

A byod framework for secure use of mobile devices in universities: The case of universities in Kenya
(kca university, 2017) Ndeng’ere, David K.
This project was to find out security threats, challenges and attacks brought about by BYOD adoption in institutions. Universities in Kenya as institutions of higher learning were chosen as a case study because BYOD was in extensive use and hence the threats, challenges and attacks would be more pronounced and frequent as compared to the non-academic institutions. The Threats, challenges and attacks were found out using a questionnaire that was sent to ICT administrators of 10 randomly sampled universities. Other existing frameworks were reviewed in order to find out how they tackled threats and challenges associated with BYOD. Framework as a BYOD solution was adopted for this study because the physical implementation of a BYOD solution in universities would be beyond the time limit of this project. The proposed framework was developed by modifying the BFS security framework and advanced it to include advanced devices access to the campus network, Malware detection and prevention, Mobile devices users’ categorization and access to servers and rogue access points by disabling Hotspots applications in mobile devices. Simulation methodology (using OPNET version 14.5) was used to test and validate the proposed framework by subjecting the framework network model to a mobile attacker node and putting preventive measures to address the attack and then comparing the simulation results of the various aspects of network performance tested as well as the campus server that was being targeted. The sampled universities had not put adequate measurers to address the BYOD challenges and attacks they experienced and hence the proposed framework would be very useful if physically implemented.
A Crypto-ransomware Detection Model For The Pre-encryption Stage Using Random Forest Algorithm
(KCA University, 2022) Njoroge, Priscillah W
Cryptographic ransomware is a challenging cybersecurity threat that encrypts the victim's files and demands a ransom in exchange for the decryption key. Traditional signature-based protection methods, such as antivirus and anti-malware, have proven in-effective at preventing crypto-ransomware attacks, therefore the production of ransomware is on the rise. Additionally, crypto ransomware incorporates advanced encryption algorithms causing irreversible effects even if the victim chooses to pay the ransom. Given the magnitude and variety of threats we face today, it is critical to have solutions in place to effectively analyse and detect crypto-ransomware attacks during the pre-encryption stage before encryption happens. Only if these threats are identified during the pre-encryption phase can they be adequately mitigated. Existing methods for early detection of crypto ransomware rely on a timing thresholding methodology to set the border of the pre-encryption stage. However, the fixed time threshold strategy, suggests that the samples begin encryption at the exact moment. This is not always the case since timing varies between crypto-ransomware families as a result of the obfuscation techniques used to evade detection. Furthermore, scarcity of data during an attack's initial stages reduces the ability of feature extraction algorithms in early detection solutions to discover attack features lowering detection accuracy. This research, therefore, proposed development of a Dynamic Crypto-Ransomware Detection Model (DCRDM). DCRDM monitors the pre-encryption stage for every case separately relying on the initial appearance of any APIs related to cryptography to establish the pre-encryption stage boundary, whereby features are extracted and used in training a prediction model using the Random Forest machine learning algorithm. The sample data was obtained from widely used ransomware repositories. The model achieved a detection accuracy of 98.6% with False Positive Rate of 1.9%.
A framework for enhancing corporate data security in a bring your own device (Byod) environment: a case of government organizations in Kenya.
(KCA University, 2018) Sowek, Geoffrey K
iiABSTRACTInformation and Communication Technology (ICT) has become an integral part of the lives of many people today. In the business scene, ICT has been embedded into the fabric of many organizations. The modern business environment is highly dynamic and is characterized by increased competition and changing employee and customer demands. Emerging technologies such as Cloud Computing, Mobile Computing and Bring Your Own Device (BYOD) have shifted the trajectory of how Information Technology (IT) is consumed. This shift has resulted in a phenomenon referred to as IT Computerization. Proliferation of mobile devices such as smart phones-and table tshasled to a notable shift in the way organizational resources are accessed by employees. Organizations are now adopting BYOD which allows employees to use their personal devices to access sensitive organizational data both within the organization and remotely. The greater capability and flexibility that these cutting-edge devices offer make them popular among many employees especially the younger generation. Adoption of BYOD by organizations presents various benefits such as increased employee productivity, better customer service and increased efficiency. Government agencies in Kenya are increasingly adopting the BYOD concept in line with the Government’s digitization program aimed at increasing efficiency of Government services and as a cost-cutting measure. However, the biggest challenge to successful adoption is the security of sensitive data. BYOD adoption presents data security risks such as loss of data, data leakages, distributed denial of service (DDoS), malware and other vulnerabilities.
A framework for knowledge as a service in the support of mobile interface ambient learning.
(kca university, 2017) Karanja, Richard Gicharu
Knowledge as a Service (KaaS) has been a promising computing paradigm in the circles of cloud computing environments. In recent times there has been a growing need for access to knowledge on demand that is fully aligned with the cloud computing paradigm which derives from the idea that users will be able to access on- demand to any application from any location in the world. In KaaS, knowledge is considered an understanding of information based on its relevance on a problem area and is perceived as a precious resource essential in decision making. This research paper has developed a framework hinged on this technology that can be used to utilize knowledge from ambient learning systems in regard to sustainable development goals with a specific approach to the fourth goal targeting inclusive and equitable quality education through open education resources for lifelong learning. The main aim was to provide a platform for dissemination and exploitation of available knowledge that will help improve the quality of education on the ambient learning system. The research also involved a look at different ambient learning projects that aim to meet this SDG goal and helped come up with a KaaS model that can be implemented alongside an ambient learning system. This has helped find out how a collaborative effort can be approached in order to form a knowledge network that can allow access to heterogeneous sources of knowledge which can in turn be of benefit to the knowledge consumers i.e. ambient learning system developers.
A framework of wide area network (WAN) optimization
(kca university, 2014) Ribiro, N. S
The fast increasing adoption of information technology and increased digitization has led to generation of large data which needs to be sent across the network. The world is becoming a small globe by the essence of networking and exchange of real information is paramount in many corporate agencies such as banks whose clients access their services throughout the country. Fast and consistent application response across the LAN and WAN help in ensuring uncompromising access to mission critical applications and services and enable high-performance businesses to use their applications to accelerate a competitive advantage. This research covered how LAN and WAN optimization can be achieved through application of various existing technologies which are both on paper and practice. This was significant in establishing trends for emergent technology in this field of information and computer technology. LAN and WAN optimization ensures that the limited available resources are used effectively to ensure effective and efficient performance of the resources which are accessed through the network.
A Hybrid Model For Predicting E-learning Course Dropout Rate For Post Graduate Students
(KCA University, 2023) Kitaka, Winfred
In universities all around Kenya, e-learning has grown in popularity, especially for postgraduate programs. Students now find it simpler to access education from any location at any time thanks to the use of technology in the delivery of courses and academic resources. However, dropout rates continue to be a serious issue despite the many advantages of online learning. For a variety of reasons, including a lack of desire, insufficient assistance, and trouble understanding the course materials, students withdraw from online courses. Dropouts drive up educational institutions' average cost per student because it typically costs more to retain a possible dropout than to enroll a new student. The rise of online learning is hampered by the prevalence of school dropouts, which waste the student's initial time and financial investment. Low graduation rates that follow high dropout rates will surely damage the standing of educational institutions in the community and eventually result in a downward loop of declining government support. To lower the dropout rate, online educational institutions can employ this technology to quickly spot probable dropouts and put retention measures in place before the dropout behavior takes place. The study's objective was to create a hybrid machine learning prediction model for postgraduate E-learning students who drop out utilizing the Support Vector Machine and Random Forest algorithms to improve prediction accuracy. The researcher employed a descriptive survey and an experimental study approach. The research methodology will be appropriate because the researcher trained the Dropout Prediction Detection model using a machine learning technique. In 2024, 61.7% of students are expected to graduate. With the aid of the data, the researcher was better able to determine whether students had spent more time studying than was anticipated. 62.5% of the respondent's price posed the most challenge to finishing the investigation, however 37.5% of the fee posed no issue. In order to prepare students for postgraduate study, 68.3% strongly agreed that undergraduates should be taught research techniques, and 29.2% also agreed. A 100% accuracy rate for forecasting student dropout was demonstrated by the hybrid model. By using machine learning to predict student attrition, educational institutions have a ground-breaking chance to effectively address this pervasive problem. The study also recommended that the students choose a study strategy that would best fit their schedules in order to prevent unneeded stress from juggling numerous tasks at once. Deep learning models can be strengthened by techniques like Synthetic Minority Over-sampling Technique to handle the unbalanced datasets typical in dropout prediction problems.
A job matching system to transform casual job market in Kenya
(kca university, 2018) Lagat, Stephen
This project objective was to come up with job matching application for use by casual workers and employers. The process of identifying challenges, opportunities, desired features and solution was designed to be a rigorous process, involving vast research and participant involvement. Casual job market is largely classified as informal sector, which is less catered for in many fronts. It has not been well served in terms of creation of innovations, sufficient to link- up the players in the industry. With high population of people engaging as either casual worker or employers, the researcher found a fertile ground to explore opportunities, and adopt data communication aspects. Questionnaires and focus group were constituted and used as quantitative and qualitative data collection tools. Extensive literature review was also conducted to explore existing casual matching models, and underlying technologies. The proposed casual job matching application was developed using evolutionary prototyping methodologies. The research produced 21 revisions of the artifact. The application is hosted on the cloud, and is distributed through play store and peer-to peer sharing. Prototyping came out as an important methodology in contribution of knowledge to research.
A Linear Regression Model For Predicting The Level Of Need For Social Protection In Kenya
(KCA University, 2023) Barasa, Anthony W
This research delved into the complex dynamics of social disadvantage in Kenya, focusing on the nation's unique social protection needs. Using the Kenya Integrated Household Budget Survey (KIHBS), we explored factors such as Household income, Educational Attainment, Employment status, Health Indicators, and Disability Status. The research findings identified household income and disability status as crucial determinants for social disadvantage, underlining the importance of fair economic opportunities. Education and employment also emerged as significant influences, emphasizing the need for comprehensive educational access and robust job creation strategies. Based on the linear regression model statistics, the R-square value of 0.656 showed a stable model. Other regression validation metrics such as residual errors helped to confirm this. The study recommends refining data consolidation techniques to uncover deeper disparities within Kenya's diverse population. While highlighted key social disadvantage determinants have been highlighted, a more detailed examination of the urban-rural divide is essential. The study, deeply rooted in theoretical frameworks, suggests that further research should explore how these theories tangibly relate to the experiences of Kenyans, providing a foundation for creating more inclusive societies both in Kenya and globally.
A Long Short-term Memory (LSTM) Network Model For Predicting Water Consumption In Residential Properties Using Smart Water Meter Data
(KCA University, 2023) Kamau, Stanely M.
Rapid urbanization in Kenya and the subsequent population increase have caused a severe imbalance between water demand and water availability. This imbalance poses serious challenges in managing water consumption in urban areas. Furthermore, water leakages and variable human activity generate non-linear patterns in domestic water consumption data which make traditional linear time series models such as autoregressive integrated moving average (ARIMA) ineffective. Using a case study research design with Nairobi City, the author developed a novel Long Short-Term Memory (LSTM) network model for predicting water demand through deep learning of smart water meters data. The model uses high frequency non-linear time series data collected between January and December 2022 from smart sensors within an Internet of Things (IoT) framework, alongside other information such as timestamp and temperature. Nine different variables were constructed from the study data and used to train and validate the LSTM network model for smart water meter data management. The model was then evaluated using root mean square error (RMSE) and the correlation coefficient. Although significant variation was observed in the daily and monthly patterns of domestic water consumption, the model outcomes were relatively accurate. LSTM generated values that mirrored observed values more closely than the ARIMA model. Evaluation metrics also indicated that LSTM had lower prediction errors. It is expected that the developed model will be generalizable for estimating future water consumption in other urban households in Kenya and other regions. The study is limited by a small sample dataset of 320 households and the lack of socio economic and demographic factors to determine water consumption. A more extensive study with multiple influencing factors is recommended to assist water authorities and service providers to properly distribute water, identify leakages, and take corrective actions to prevent degradation of the ecological environment.
A Model For Assessing The Performance Of Post Graduate Research Supervisors In Kenyan Universities
(kca university, 2021) Mbom, Luke O
Higher education has become important in Kenya due to the increase in the number of students, introduction of new courses at the universities and an increase in the number of universities. Our study undertook to create a model that investigated and identified the attributes that can be used to determine the performance of supervisors when assessing post graduate research students’ in Kenyan universities. The study was driven by the desire to evaluate why many post graduate students do not complete their research in a timely manner. The findings of this study can be used as a measure to determine promotions, to improve supervision performance in Kenyan universities, to improve service delivery in the industries and to ensure average or high completion rates of students at the post graduate level in Kenyan universities. The research targeted a population of 20 and more post graduate students and coordinators from Kenyan Universities. Online questionnaires were used to get data from the respondents and Likert scale was used to convert data into numeric values. We investigated whether the supervisor performance can be determined by the supervisor characteristics, student characteristics and use of learning resources and facilities. The data collected from the students and coordinators was analyzed using a system designed using Django web frameworks and data analytics was applicable.
A Model For Assessing The Performance Of Post Graduate Research Supervisors In Kenyan Universities
(KCA University, 2021) Mbom, Luke O
Higher education has become important in Kenya due to the increase in the number of students, introduction of new courses at the universities and an increase in the number of universities. Our study undertook to create a model that investigated and identified the attributes that can be used to determine the performance of supervisors when assessing post graduate research students’ in Kenyan universities. The study was driven by the desire to evaluate why many post graduate students do not complete their research in a timely manner. The findings of this study can be used as a measure to determine promotions, to improve supervision performance in Kenyan universities, to improve service delivery in the industries and to ensure average or high completion rates of students at the post graduate level in Kenyan universities. The research targeted a population of 20 and more post graduate students and coordinators from Kenyan Universities. Online questionnaires were used to get data from the respondents and Likert scale was used to convert data into numeric values. We investigated whether the supervisor performance can be determined by the supervisor characteristics, student characteristics and use of learning resources and facilities. The data collected from the students and coordinators was analyzed using a system designed using Django web frameworks and data analytics was applicable.
A Model For Evaluating The Efficacy Of E-learning In Higher Educational Institutions Using Educational Data Mining
(kca university, 2022) Kangethe, George N
Educational Data Mining (EDM) and Learning Analytics (LA) play a key role in developing methods for discovering student learning patterns and behaviors by interrogating this robust set of data now available in learning environments. The main objective of this study is to develop a model for evaluating efficacy of eLearning at Higher Educational Institutions (HEI’s). To measure the efficacy of eLearning, data on student activity within eLearning LMS and student academic performance is analyzed. In this study, Orange data mining tool is used for the analysis of the data. Support Vector Machine, Random Forest, Decision Tree, Nave Bayes, Logistic Regression, and Neural Network are among the categorization techniques provided within Orange. These classifiers are compared based on their accuracy. The selected classifiers are evaluated against a k-fold cross validation, accuracy, precision, recall, and F-score. According to the empirical findings, the Support Vector Machine (SVM) algorithm was the best data mining model for estimating students' academic achievement.
A model for measuring impact Of digitization in schools
(Kca University, 2017) Mwambela, Nicodemus K.
Certainly, digitization has become ubiquitous; now. Almost in all the sectors we routinely interact with digital technologies. In this generation it is elating to be referred as ‗digital‘ and degrading to be referred to as ‗analogue‘. In the context of Kenyan secondary school, digitization like a railway track moves in two major planes namely the use of Management information systems (MIS) for purposes of school management and ICT integration for the delivery of the curriculum. There is great deal more information and research on the use of Information Technology in curriculum delivery in class. But the literature is conspicuously silent on the integration of the two systems that will help effectively create a smart school system, or a digital school. This research undertook to study the extent of impact of the Use of ICT in curriculum delivery and use of MIS in school management. What lacks is the formal way of assessing the impact of the corporate effect of digitization on both the academic performance of the school and service delivery needed creating. This research undertook to create a model to give a quantitative assessment of the impact of digitization in secondary schools. This study was driven by desire to evaluate the impact of digitization in our educational institutions. This will help shed light on whether our educational institutions are in sync with digital progress and whether that is producing a measurable difference in terms of academic performance f the learners and the service delivery of the institutions. The research targeted 35 teachers from seven of the 11 public schools of Makadara Sub- county in Nairobi county. The study sought the teachers input in assessing the adequacy of infrastructure, the reliability and speed of computer systems and the impact that this had on both their students interest and improved academic performance which is referred as slope in this study. The input from teachers solicited through questionnaires was converted into numeric values by use of likert scale from which two measures, the MIS metric index and ICT Integration Index were developed as independent variables. These measures calculated for each of the seven sample schools were compared to the academic performance trend of each of the schools calculated using the MS Excel slope function. The two set of data were found to have a strong positive correlation of 0.49 for MIS Metric Index and 0.63 for ICT integration Index. From the two of these values an empirical model was developed which took the form of multiple regression relation. A manual statistical calculation was used to solve for the constants of the relation thereby arriving at the model. This model can be used to either interpolate or extrapolate the values of school digitization index and its impact of the school. The reliability and validation of the model was evaluated by use of Cronbach Alpha measure of internal consistency and found to have the acceptable value of 0.735.
A model for predicting non-adherence among per-exposure prophylaxis (prep) clients at Suba region.
(KCA University, 2018) Fred B., Nyatika,
The immune system protects the body againstdiseases or any foreign body that is harmful to the body, it is the body’s natural defense against illnesses. The threats that the immune system attacks include viruses, bacteria and parasites. Human Immunodeficiency Virus (HIV) is a virus that is responsible for the Acquired Immunodeficiency Syndrome (AIDS). AIDS is a set of symptoms that occur after HIV infection, it is when the body’s immune system is too weak to fight off infection(Rachel Nall 2016). There has been numerous attempts by the government and indeed the foreign donors to try and contain the epidemic. The use of test and treathas had a great impact by ensuring that those who are tested HIV positive are put on careimmediatelyand with good adherence then we expect to have low transmission rates. The latest approach by the world health organization is the recommendation to use test and treat combined with putting the most at risk for HIV infection on the daily pill of Pre-exposure Prophylaxis(PrEP) which reduces the risk of getting infected by HIV by more than 90%(CDC 2018a). PrEP is very effective if taken consistently failure to which may lead to HIV infection.It has been observed that there still exist a number of clients who are becoming HIV positive even after being put on PrEP.The objective of the study was to develop a model to predict non-adherence to PrEP among PrEP enrolled clients in Suba region of Homabay County.
A Model For Predicting Students Academic Performance In Public Secondary Schools In Kitui West Constituency
(KCA University, 2021) Ndambuki, Peter M
In the present era of data deluge, institutions have accumulated huge amounts of data in their databases. Educational institutions all over the world are not an exception, having as well accumulated large amounts of data in their various educational management information systems databases of various forms and formats. The accumulation of such data in various educational institutions has led to the rise of two research fields namely; Educational data mining and learning analytics in an effort to discover hidden knowledge (insights) that can greatly improve operations in educational institutions. Among the hidden knowledge include but not limited to; predicting students’ performance, students’ drop out, discovering students interest which could avert popular student’s unrest in various institutions etc. This study seeks to take advantage of such an opportunity and develop a model using dataset obtained from public secondary schools in Kitui west constituency that can be used to predict students’ academic performance. There has been attempts from various researchers all over the globe to address this problem. Although such studies achieved some level of success, various limitation discussed in details in the empirical review militated against the performance of the earlier models. Desk research methodology was used to extract relevant secondary data from various schools’ departments within Kitui west constituency. Then preprocessing which includes feature selection after which the cleaned dataset was loaded to staging Data Lake in Hadoop. Data was queried from the Data Lake to python using Pyspark where data analysis procedures took place. Dataset consisting of optimal subset of features was used to train four machine-learning algorithms: Gradient boost classifier, Random forest classifier, Decision tree classifier and Deep Neural Network classifier. Generally, Decision tree and Random forest classifiers registered the best performance overall, with an accuracy of 97%, but after stratified Kfold cross validation, Decision tree classifier’s performance proved more stable with an average of 97% compared to Random forest classifier with 93%. Thus, Decision tree classifier was recommended for deployment in predicting students ‘academic performance for its reliable accuracy and relatively good precision on predicting the study’s target group. The developed Model will place students in to two groups: PASS and FAIL. The aim being to arouse an initiation of intervention from various stakeholders to reduce dismal performance among public secondary schools in Kitui west constituency.
A Model for Predicting Traffic Congestion Using Deep Learning Algorithm: Case of Nairobi Metropolitan
(KCA University, 2023) Cheruiyot, Ezra K
Traffic congestion is a widespread problem that plagues urban transportation systems, causing delays, increased fuel consumption, and environmental pollution. Addressing this issue requires accurate prediction of traffic congestion, enabling proactive management strategies and real-time information dissemination. Deep learning algorithms have emerged as powerful tools for traffic prediction, offering the potential to forecast congestion patterns effectively. The development of a model for predicting traffic congestion that is capable of accurately detecting and reducing the overall density of traffic in most urban areas frequented by motorists, such as offices, downtown, and establishments, has become one of the main challenges for engineers and designers in recent years. Traffic prediction models in use today are based on several modern technologies, including wireless sensor networks and surveillance cameras. In Kenya, the Nairobi Metropolitan Area has greatly felt the impacts of traffic congestion due to ever growing urban population. This is primarily because the number of vehicles has rapidly increased as compared to the infrastructure growth. This study presented a platform for addressing the traffic congestion through the establishment of Intelligent Traffic Management model using Deep Learning Algorithm. The study utilized observation checklist and questionnaire as the source of data for the study. An observation data collection sheet was used in collecting the data from the four main roads. To obtain data from the traffic officers, questionnaires was used. SPSS version 28 were used to analyze the data. Further from the correlation analysis, all the variables including High cost of travel/fares (r=.494), High vehicle maintenance (r=.206), Environmental pollution (r=.359), Staff fatigue (drivers and conductors) (r=.488), Accidents (r=.310), Poor road design (r=.308), Poor Traffic control system (r=.410), Road construction and maintenance works (r=.353), Vehicle break downs (r=.179), Roadside parking/obstruction (r=.452), High number of private cars (r=.233), High number of public transport vehicles (r=.071), Behavior of road usage (r=.228) Accidents (r=-.042), Poor road use (r=-.042) and Poor traffic management (r=-.209) had positive correlation with traffic congestion in Nairobi Metropolitan Area. Regression analysis further found that poor traffic management by traffic officers, a high number of public transport vehicles, poor road design, accidents, a high number of private cars, poor road use, poor traffic control systems, driver behavior, vehicle breakdowns, road construction and maintenance, and roadside parking explained up to 34.4% of the variation in travel time. In comparison, factors such as driver behavior, roundabout type, time of day, number of lanes, vehicle type, weather conditions, and travel rate explained 13.7% of the variation in road travel rates. Therefore, improved infrastructure, traffic management practices, and enhanced driver behavior are concluded to reduce travel time and improve transportation efficiency in the region. The study recommends that traffic engineering and urban planning practices should prioritize the optimization of road networks. The study recommends that local authorities and law enforcement agencies should collaborate to enforce traffic rules and regulations rigorously. The study also recommends that implementation of robust traffic management strategy by improving traffic signal synchronization, implementing intelligent traffic management systems, and investing in technology-driven solutions like real-time traffic monitoring and congestion alerts. Adequate and efficient traffic management by officers should also be ensured, as this factor has been found to play a substantial role in congestion mitigation. Additionally, policymakers should consider congestion pricing mechanisms during peak hours. This will incentivize drivers to use alternative routes or modes of transportation, thus reducing traffic congestion during high demand periods. Revenues generated from congestion pricing can be reinvested in transportation infrastructure and improvements.
A Model for selecting security protocols for wireless sensor networks
(kca university, 2013) Ndia, John Gichuki
ABSTRACT The process of mapping security requirements to the most appropriate security protocol has over the time proved a great challenge. Though there are various security mechanisms designed to curb security threats, they come with various properties and therefore the choice of the best security protocol for a given application becomes quite complex. To ease the process of mapping security requirement of sensor applications to security protocol, security environments for WSNs have been defined formally. There are numerous WSNs applications being developed day to day, ranging from simple environmental monitoring e.g. collecting of temperatures in an agricultural farm to complex applications like for monitoring battle field. Therefore, this research dissertation objective was to enable selection of best security protocol that falls under a certain security class for the various existing WSNs applications and applications to be developed in the future. The research endeavored to identify and evaluate the security protocols that are practically used in WSNs and to identify the best tool to be used in simulation process, and finally to validate selection of security protocols
A Model Of BYOD Integration To Increase Corporate Information Security In Banks: Case Of Equity Bank Kenya
(kca university, 2021) Dalla, Gladys M
Bring Your Own Device or BYOD is a novel approach where employees and stakeholders in organizations bring their personal computer devices to the workplace. Employees are able to access organization information and data through their devices under the BYOD policy. On the other hand, the BYOD approach heightens the risk of malware attacks and therefore, diminishes the integrity of the information security within the organization. The current study sought to develop a model for the integration of BYOD in the banking sector while maintaining a sustainable corporate information security. The theories guiding the study are the technology threat avoidance theory and unified Theory of Acceptance and Use of Technology. On the other hand, the research study adopted a cross-sectional survey design to collect data. The survey design is effective in coming up with quantitative data that aids one develop inferences regarding a particular phenomenon. The study established that Mobile device management, Information security policies, Security culture and Employee education as BYOD factors have significant effect on Sustainable corporate information security. Following data collection the researcher was able to clean, code and analyze data using SPSS v27. An OLS model was derived from the analyzed data. The derived statistical model can be instrumental in integration of BYOD while maintain information security. The generated model was tested and validated through multiple regressions test statistics. The Adjusted R value obtained through model summary was r2=0.437 indicating that the independent variables of Mobile device management, Information security policies, Security culture and Employee education contribute 43% variation in Sustainable corporate information security. The ANOVA statistic showed that the independent variables are significant to the dependent variable. Subsequently, the independent variables in the study have a significant impact on the dependent variable of Sustainable corporate information security. Moreover, the researcher recommends that organizations in the banking sector have a device register in the BYOD platform to ensure information security. The findings of the research are significant to the corporate sector as it adds knowledge that will help guide the security model employed in running BYOD. Integration of BYOD is a necessity in most industries, hence this research provides a robust model for heightening Sustainable corporate information security.
A Model Of BYOD Integration To Increase Corporate Information Security In Banks: Case Of Equity Bank Kenya
(KCA University, 2021) Dalla, Gladys M
Bring Your Own Device or BYOD is a novel approach where employees and stakeholders in organizations bring their personal computer devices to the workplace. Employees are able to access organization information and data through their devices under the BYOD policy. On the other hand, the BYOD approach heightens the risk of malware attacks and therefore, diminishes the integrity of the information security within the organization. The current study sought to develop a model for the integration of BYOD in the banking sector while maintaining a sustainable corporate information security. The theories guiding the study are the technology threat avoidance theory and unified Theory of Acceptance and Use of Technology. On the other hand, the research study adopted a cross-sectional survey design to collect data. The survey design is effective in coming up with quantitative data that aids one develop inferences regarding a particular phenomenon. The study established that Mobile device management, Information security policies, Security culture and Employee education as BYOD factors have significant effect on Sustainable corporate information security. Following data collection the researcher was able to clean, code and analyze data using SPSS v27. An OLS model was derived from the analyzed data. The derived statistical model can be instrumental in integration of BYOD while maintain information security. The generated model was tested and validated through multiple regressions test statistics. The Adjusted R value obtained through model summary was r2=0.437 indicating that the independent variables of Mobile device management, Information security policies, Security culture and Employee education contribute 43% variation in Sustainable corporate information security. The ANOVA statistic showed that the independent variables are significant to the dependent variable. Subsequently, the independent variables in the study have a significant impact on the dependent variable of Sustainable corporate information security. Moreover, the researcher recommends that organizations in the banking sector have a device register in the BYOD platform to ensure information security. The findings of the research are significant to the corporate sector as it adds knowledge that will help guide the security model employed in running BYOD. Integration of BYOD is a necessity in most industries, hence this research provides a robust model for heightening Sustainable corporate information security.
A model to detect and protect toll fraud in VoIP PBX infrastructure
(kca university, 2017) Muli, Aloise Kyengo