The goal of this manuscript is to present a research finding, based on a study conducted to identify, examine, and validate Social Media (SM) socio-technical information security factors, in line with usable-security ...The goal of this manuscript is to present a research finding, based on a study conducted to identify, examine, and validate Social Media (SM) socio-technical information security factors, in line with usable-security principles. The study followed literature search techniques, as well as theoretical and empirical methods of factor validation. The strategy used in literature search includes Boolean keywords search, and citation guides, using mainly web of science databases. As guided by study objectives, 9 SM socio-technical factors were identified, verified and validated. Both theoretical and empirical validation processes were followed. Thus, a theoretical validity test was conducted on 45 Likert scale items, involving 10 subject experts. From the score ratings of the experts, Content Validity Index (CVI) was calculated to determine the degree to which the identified factors exhibit appropriate items for the construct being measured, and 7 factors attained an adequate level of validity index. However, for reliability test, 32 respondents and 45 Likert scale items were used. Whereby, Cronbach’s alpha coefficient (α-values) were generated using SPSS. Subsequently, 8 factors attained an adequate level of reliability. Overall, the validated factors include;1) usability—visibility, learnability, and satisfaction;2) education and training—help and documentation;3) SM technology development—error handling, and revocability;4) information security —security, privacy, and expressiveness. In this case, the confirmed factors would add knowledge by providing a theoretical basis for rationalizing information security requirements on SM usage.展开更多
Ecotourism is a sustainable, green and smokeless industry of the society by providing alternative source to the livelihood and local community, in order to re-naturalize the environment to bring the man closer to the ...Ecotourism is a sustainable, green and smokeless industry of the society by providing alternative source to the livelihood and local community, in order to re-naturalize the environment to bring the man closer to the natural environment. The use of web application has changed the way tourists gather information about tourist attraction spots of the research area. The aim of the study is to assess and identify ecotourism attraction sites of Chokie Mountain watersheds for touristic activities to develop a web-based GIS mapping portal for the improvement of nature-based ecotourism activities. The web-based GIS portal for Web Mapping Application is available for the user with the skills necessary to create standard web mapping services. We have implemented web mapping services based on formal cartographic visualization rules in the open source QGIS software and threejs JavaScript plugin. Threejs plugin is used for 3D visualization, interaction and export terrain data, map canvas image and vector data to HTML page and JS. The result of web-based GIS portal supports spatial and non-spatial database of tourist attraction and tourist service data with attractive user interface.展开更多
Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remain...Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.展开更多
The purpose of this manuscript is to present research findings based on the reported cases of medical information breaches due to Social Media (SM) usage, in selected medical institutions in Uganda. The study employed...The purpose of this manuscript is to present research findings based on the reported cases of medical information breaches due to Social Media (SM) usage, in selected medical institutions in Uganda. The study employed online survey techniques. Altogether, 710 questionnaires (Google forms) were developed, and operationalized. The main respondents included 566 medical students, and 143 medical staff from Mbarara University of Science and Technology (MUST), and Kampala International University (KIU), accordingly. Using SPSS, the main statistical analysis tools employed include frequency distribution summary, and Chi-square (x<sup>2</sup>) test. According to the frequency distribution summary, 27% to 42% of the respondents within categorical divides acknowledged occurrence of medical information breaches due to SM usage. Notably, higher levels of the breaches were reported among male students (64%), age-group 18 to 35 years (68%), and WhatsApp users (63%). On the other hand, Chi-square results showed significant levels (p p > 0.05) between medical institutions and medical information breaches. Overall, the vulnerable areas of the breaches identified would serve as important reference points in the process of rationalizing SM usage in medical institutions. Nevertheless, further studies could focus on identification of the key SM usage factors associated with medical information breaches in medical institutions in Uganda.展开更多
The advancement in cellular communications has enhanced the special attention given to the study of resource allocation schemes.This study is to enhance communications to attain efficiency and thereby offers fairness ...The advancement in cellular communications has enhanced the special attention given to the study of resource allocation schemes.This study is to enhance communications to attain efficiency and thereby offers fairness to all users in the face of congestion experienced anytime a new product is rolled out.The comparative analysis was done on the performance of Enhanced Proportional Fair,Qos-Aware Proportional Fair and Logarithmic rule scheduling algorithms in Long Term Evolution in this work.These algorithms were simulated using LTE system toolbox in MATLAB and their performances were compared using Throughput,Packet delay and Packet Loss Ratio.The results showed Qos-Aware Proportional Fair has a better performance in all the metrics used for the evaluation.展开更多
Due to the complexity of data,interpretation of pattern or extraction of information becomes difficult;therefore application of machine learning is used to teach machines how to handle data more efficiently.With the i...Due to the complexity of data,interpretation of pattern or extraction of information becomes difficult;therefore application of machine learning is used to teach machines how to handle data more efficiently.With the increase of datasets,various organizations now apply machine learning applications and algorithms.Many industries apply machine learning to extract relevant information for analysis purposes.Many scholars,mathematicians and programmers have carried out research and applied several machine learning approaches in order to find solution to problems.In this paper,we focus on general review of machine learning including various machine learning techniques.These techniques can be applied to different fields like image processing,data mining,predictive analysis and so on.The paper aims at reviewing machine learning techniques and algorithms.The research methodology is based on qualitative analysis where various literatures is being reviewed based on machine learning.展开更多
Numerous Internet of Things(IoT)systems produce massive volumes of information that must be handled and answered in a quite short period.The growing energy usage related to the migration of data into the cloud is one ...Numerous Internet of Things(IoT)systems produce massive volumes of information that must be handled and answered in a quite short period.The growing energy usage related to the migration of data into the cloud is one of the biggest problems.Edge computation helps users unload the workload again from cloud near the source of the information that must be handled to save time,increase security,and reduce the congestion of networks.Therefore,in this paper,Optimized Energy Efficient Strategy(OEES)has been proposed for extracting,distributing,evaluating the data on the edge devices.In the initial stage of OEES,before the transmission state,the data gathered from edge devices are supported by a fast error like reduction that is regarded as the largest energy user of an IoT system.The initial stage is followed by the reconstructing and the processing state.The processed data is transmitted to the nodes through controlled deep learning techniques.The entire stage of data collection,transmission and data reduction between edge devices uses less energy.The experimental results indicate that the volume of data transferred decreases and does not impact the professional data performance and predictive accuracy.Energy consumption of 7.38 KJ and energy conservation of 55.57 kJ was found in the proposed OEES scheme.Predictive accuracy is 97.5 percent,data performance rate was 97.65 percent,and execution time is 14.49 ms.展开更多
The exponential growth of Internet of Things(IoT)devices has introduced significant security challenges,particularly in securing token-based communication protocols used for authentication and authorization.This surve...The exponential growth of Internet of Things(IoT)devices has introduced significant security challenges,particularly in securing token-based communication protocols used for authentication and authorization.This survey systematically reviews the vulnerabilities in token transmission within IoT environments,focusing on various sophisticated attack vectors such as replay attacks,token hijacking,man-in-the-middle(MITM)attacks,token injection,and eavesdropping among others.These attacks exploit the inherent weaknesses of token-based mechanisms like OAuth,JSON Web Tokens(JWT),and bearer tokens,which are widely used in IoT ecosystems for managing device interactions and access control.The impact of such attacks is profound,leading to unauthorized access,data exfiltration,and control over IoT devices,posing significant threats to privacy,safety,and the operational integrity of critical IoT applications in sectors like healthcare,smart cities,and industrial automation.This paper categorizes these attack vectors,explores real-world case studies,and analyzes their effects on resource-constrained IoT devices that have limited processing power and memory,rendering them more susceptible to such exploits.Furthermore,this survey presents a comprehensive evaluation of existing mitigation techniques,including cryptographic protocols,lightweight secure transmission frameworks,secure token management practices,and network-layer defenses such as Transport Layer Security(TLS)and multi-factor authentication(MFA).The study also highlights the trade-offs between security and performance in IoT systems and identifies key gaps in current research,emphasizing the need for more scalable,energy-efficient,and robust security frameworks to address the evolving landscape of token transmission attacks in IoT devices.展开更多
文摘The goal of this manuscript is to present a research finding, based on a study conducted to identify, examine, and validate Social Media (SM) socio-technical information security factors, in line with usable-security principles. The study followed literature search techniques, as well as theoretical and empirical methods of factor validation. The strategy used in literature search includes Boolean keywords search, and citation guides, using mainly web of science databases. As guided by study objectives, 9 SM socio-technical factors were identified, verified and validated. Both theoretical and empirical validation processes were followed. Thus, a theoretical validity test was conducted on 45 Likert scale items, involving 10 subject experts. From the score ratings of the experts, Content Validity Index (CVI) was calculated to determine the degree to which the identified factors exhibit appropriate items for the construct being measured, and 7 factors attained an adequate level of validity index. However, for reliability test, 32 respondents and 45 Likert scale items were used. Whereby, Cronbach’s alpha coefficient (α-values) were generated using SPSS. Subsequently, 8 factors attained an adequate level of reliability. Overall, the validated factors include;1) usability—visibility, learnability, and satisfaction;2) education and training—help and documentation;3) SM technology development—error handling, and revocability;4) information security —security, privacy, and expressiveness. In this case, the confirmed factors would add knowledge by providing a theoretical basis for rationalizing information security requirements on SM usage.
文摘Ecotourism is a sustainable, green and smokeless industry of the society by providing alternative source to the livelihood and local community, in order to re-naturalize the environment to bring the man closer to the natural environment. The use of web application has changed the way tourists gather information about tourist attraction spots of the research area. The aim of the study is to assess and identify ecotourism attraction sites of Chokie Mountain watersheds for touristic activities to develop a web-based GIS mapping portal for the improvement of nature-based ecotourism activities. The web-based GIS portal for Web Mapping Application is available for the user with the skills necessary to create standard web mapping services. We have implemented web mapping services based on formal cartographic visualization rules in the open source QGIS software and threejs JavaScript plugin. Threejs plugin is used for 3D visualization, interaction and export terrain data, map canvas image and vector data to HTML page and JS. The result of web-based GIS portal supports spatial and non-spatial database of tourist attraction and tourist service data with attractive user interface.
基金This study is financed by the European Union-NextGenerationEU,through the National Recovery and Resilience Plan of the Republic of Bulgaria,Project No.BG-RRP-2.013-0001.
文摘Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.
文摘The purpose of this manuscript is to present research findings based on the reported cases of medical information breaches due to Social Media (SM) usage, in selected medical institutions in Uganda. The study employed online survey techniques. Altogether, 710 questionnaires (Google forms) were developed, and operationalized. The main respondents included 566 medical students, and 143 medical staff from Mbarara University of Science and Technology (MUST), and Kampala International University (KIU), accordingly. Using SPSS, the main statistical analysis tools employed include frequency distribution summary, and Chi-square (x<sup>2</sup>) test. According to the frequency distribution summary, 27% to 42% of the respondents within categorical divides acknowledged occurrence of medical information breaches due to SM usage. Notably, higher levels of the breaches were reported among male students (64%), age-group 18 to 35 years (68%), and WhatsApp users (63%). On the other hand, Chi-square results showed significant levels (p p > 0.05) between medical institutions and medical information breaches. Overall, the vulnerable areas of the breaches identified would serve as important reference points in the process of rationalizing SM usage in medical institutions. Nevertheless, further studies could focus on identification of the key SM usage factors associated with medical information breaches in medical institutions in Uganda.
文摘The advancement in cellular communications has enhanced the special attention given to the study of resource allocation schemes.This study is to enhance communications to attain efficiency and thereby offers fairness to all users in the face of congestion experienced anytime a new product is rolled out.The comparative analysis was done on the performance of Enhanced Proportional Fair,Qos-Aware Proportional Fair and Logarithmic rule scheduling algorithms in Long Term Evolution in this work.These algorithms were simulated using LTE system toolbox in MATLAB and their performances were compared using Throughput,Packet delay and Packet Loss Ratio.The results showed Qos-Aware Proportional Fair has a better performance in all the metrics used for the evaluation.
文摘Due to the complexity of data,interpretation of pattern or extraction of information becomes difficult;therefore application of machine learning is used to teach machines how to handle data more efficiently.With the increase of datasets,various organizations now apply machine learning applications and algorithms.Many industries apply machine learning to extract relevant information for analysis purposes.Many scholars,mathematicians and programmers have carried out research and applied several machine learning approaches in order to find solution to problems.In this paper,we focus on general review of machine learning including various machine learning techniques.These techniques can be applied to different fields like image processing,data mining,predictive analysis and so on.The paper aims at reviewing machine learning techniques and algorithms.The research methodology is based on qualitative analysis where various literatures is being reviewed based on machine learning.
基金The authors would like to thank for the support from Taif University Researchers Supporting Project number(TURSP-2020/98),Taif University,Taif,Saudi Arabia.
文摘Numerous Internet of Things(IoT)systems produce massive volumes of information that must be handled and answered in a quite short period.The growing energy usage related to the migration of data into the cloud is one of the biggest problems.Edge computation helps users unload the workload again from cloud near the source of the information that must be handled to save time,increase security,and reduce the congestion of networks.Therefore,in this paper,Optimized Energy Efficient Strategy(OEES)has been proposed for extracting,distributing,evaluating the data on the edge devices.In the initial stage of OEES,before the transmission state,the data gathered from edge devices are supported by a fast error like reduction that is regarded as the largest energy user of an IoT system.The initial stage is followed by the reconstructing and the processing state.The processed data is transmitted to the nodes through controlled deep learning techniques.The entire stage of data collection,transmission and data reduction between edge devices uses less energy.The experimental results indicate that the volume of data transferred decreases and does not impact the professional data performance and predictive accuracy.Energy consumption of 7.38 KJ and energy conservation of 55.57 kJ was found in the proposed OEES scheme.Predictive accuracy is 97.5 percent,data performance rate was 97.65 percent,and execution time is 14.49 ms.
文摘The exponential growth of Internet of Things(IoT)devices has introduced significant security challenges,particularly in securing token-based communication protocols used for authentication and authorization.This survey systematically reviews the vulnerabilities in token transmission within IoT environments,focusing on various sophisticated attack vectors such as replay attacks,token hijacking,man-in-the-middle(MITM)attacks,token injection,and eavesdropping among others.These attacks exploit the inherent weaknesses of token-based mechanisms like OAuth,JSON Web Tokens(JWT),and bearer tokens,which are widely used in IoT ecosystems for managing device interactions and access control.The impact of such attacks is profound,leading to unauthorized access,data exfiltration,and control over IoT devices,posing significant threats to privacy,safety,and the operational integrity of critical IoT applications in sectors like healthcare,smart cities,and industrial automation.This paper categorizes these attack vectors,explores real-world case studies,and analyzes their effects on resource-constrained IoT devices that have limited processing power and memory,rendering them more susceptible to such exploits.Furthermore,this survey presents a comprehensive evaluation of existing mitigation techniques,including cryptographic protocols,lightweight secure transmission frameworks,secure token management practices,and network-layer defenses such as Transport Layer Security(TLS)and multi-factor authentication(MFA).The study also highlights the trade-offs between security and performance in IoT systems and identifies key gaps in current research,emphasizing the need for more scalable,energy-efficient,and robust security frameworks to address the evolving landscape of token transmission attacks in IoT devices.
