Information Assurance Technique for Mitigation of Data Breaches in the Human Service Sector

This research paper analyzes data breaches in the human service sector. The hypothesis for the solution to this problem is that there will be a significant reduction in data breaches in the human service sector due to an increase in information assurance. The hypothesis is tested using data from the United States Department of Health and Human Services data breach notification repository during January 2018-December 2020. Our result shows that without the increased mitigation of information assurance, data breaches in the human service sector will continue to increase.

Enhanced Information Systems Success Model for Patient Information Assurance

The current health information systems have many challenges such as lack of standard user interfaces,data security and privacy issues,inability to uniquely identify patients across multiple hospital information systems,probable misuse of patient data,high technological costs,resistance to technology deployments in hospital management,lack of data gathering,processing and analysis standardization.All these challenges,among others hamper either the acceptance of the health information systems,operational efficiency or expose patient information to cyber attacks.In this paper,an enhanced information systems success model for patient information assurance is developed using an amalgamation of Technology Acceptance Model(TAM)and Information Systems Success Model(ISS).This involved the usage of Linear Structured Relationship(LISREL)software to model a combination of ISS and Intention to Use(ITU),TAM and ITU,ISS and user satisfaction(US),and finally TAM and US.The sample size of 110 respondents was obtained based on the total population of 221 using the Conhrans formula.Thereafter,simple random sampling was employed to select members within each category of employees to take part in the study.The questionnaire as a research tool was checked for reliability via Cronbach’s Alpha.The results obtained showed that for ISS and ITU modeling,only perceived ease of use,system features,response time,flexibility,timeliness,accuracy,responsiveness and user training positively influenced the intention to use.However,for the TAM and ITU modeling,only TAM’s measures such as timely information,efficiency,increased transparency,and proper patient identification had a positive effect on intension to use.The ISS and US modeling revealed that perceived ease of use had the greatest impact on user satisfaction while response time had the least effect on user satisfaction.On its part,the TAM and US modeling showed that timely information,effectiveness,consistency,enhanced communication,and proper patients identification had a positive influence on user satisfaction.

Survivability evaluation for networks carrying complex traffic flows

The capability of a system to fulfill its mission promptly in the presence of attacks,failures,or accidents is one of the qualitative definitions of survivability.In this paper,we propose a model for survivability quantification,which is acceptable for networks carrying complex traffic flows.Complex network traffic is considered as general multi-rate,heterogeneous traffic,where the individual bandwidth demands may aggregate in complex,nonlinear ways.Blocking probability is the chosen measure for survivability analysis.We study an arbitrary topology and some other known topologies for the network.Independent and dependent failure scenarios as well as deterministic and random traffic models are investigated.Finally,we provide survivability evaluation results for different network configurations.The results show that by using about 50%of the link capacity in networks with a relatively high number of links,the blocking probability remains near zero in the case of a limited number of failures.

Using Linear Regression Analysis and Defense in Depth to Protect Networks during the Global Corona Pandemic

The purpose of this research was to determine whether the Linear Regression Analysis can be effectively applied to the prioritization of defense-in-depth security tools and procedures to reduce cyber threats during the Global Corona Virus Pandemic. The way this was determined or methods used in this study consisted of scanning 20 peer reviewed Cybersecurity Articles from prominent Cybersecurity Journals for a list of defense in depth measures (tools and procedures) and the threats that those measures were designed to reduce. The methods further involved using the Likert Scale Model to create an ordinal ranking of the measures and threats. The defense in depth tools and procedures were then compared to see whether the Likert scale and Linear Regression Analysis could be effectively applied to prioritize and combine the measures to reduce pandemic related cyber threats. The results of this research reject the H0 null hypothesis that Linear Regression Analysis does not affect the relationship between the prioritization and combining of defense in depth tools and procedures (independent variables) and pandemic related cyber threats (dependent variables).

Reducing Threats by Using Bayesian Networks to Prioritize and Combine Defense in Depth Security Measures

Studied in this article is whether the Bayesian Network Model (BNM) can be effectively applied to the prioritization of defense in-depth security tools and procedures and to the combining of those measures to reduce cyber threats. The methods used in this study consisted of scanning 24 peer reviewed Cybersecurity Articles from prominent Cybersecurity Journals using the Likert Scale Model for the article’s list of defense in depth measures (tools and procedures) and the threats that those measures were designed to reduce. The defense in depth tools and procedures are then compared to see whether the Likert scale and the Bayesian Network Model could be effectively applied to prioritize and combine the measures to reduce cyber threats attacks against organizational and private computing systems. The findings of the research reject the H0 null hypothesis that BNM does not affect the relationship between the prioritization and combining of 24 Cybersecurity Article’s defense in depth tools and procedures (independent variables) and cyber threats (dependent variables).