Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051519
T. Olaleye, Adeola Olaleye, Emmanuel Ofoegbunam, Gbenga Abodunrin, Temitope Abioye, W. Ahiara
Internet of Things continues to redefine modus operandi across diverse socioeconomic and professional domains thereby generating an un-abating global discuss on the adoption and functionalities of smart devices. Since emotions play a critical role in decision making according to the psychological domain of emotion science, the paramount importance of periodic delineation of stakeholders' mood is imperative for policy makers. Whereas opinion mining analytics of IoT discussions have returned state-of-the-arts, there is need to address germane factors seldom factored into existing literatures. This study therefore consolidates on current frameworks through a bi-modal descriptive and content-based analytics of IoT ecosystem for detecting key mood domain and the BlueCheckCredibility status of IoT tweeters using Profile of Mood State and Nomogram-based analytics. With a 99.5% precision rate by Logistic regression model, social characteristic attributes of acquired ethnographic data points turns mutually exclusive to the credibility status of IoT opinion molders while tweet properties contributes higher discriminative tendencies for identifying negative IoT emotions. The impact of Internet of Things on data science is likewise unraveled through bi-gram content analytics to identify topical discussions encapsulated in the acquired tweet corpus.
{"title":"Opinion mining analytics of IoT ecosystem by Profile of Mood State with Logistic Regression","authors":"T. Olaleye, Adeola Olaleye, Emmanuel Ofoegbunam, Gbenga Abodunrin, Temitope Abioye, W. Ahiara","doi":"10.1109/ITED56637.2022.10051519","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051519","url":null,"abstract":"Internet of Things continues to redefine modus operandi across diverse socioeconomic and professional domains thereby generating an un-abating global discuss on the adoption and functionalities of smart devices. Since emotions play a critical role in decision making according to the psychological domain of emotion science, the paramount importance of periodic delineation of stakeholders' mood is imperative for policy makers. Whereas opinion mining analytics of IoT discussions have returned state-of-the-arts, there is need to address germane factors seldom factored into existing literatures. This study therefore consolidates on current frameworks through a bi-modal descriptive and content-based analytics of IoT ecosystem for detecting key mood domain and the BlueCheckCredibility status of IoT tweeters using Profile of Mood State and Nomogram-based analytics. With a 99.5% precision rate by Logistic regression model, social characteristic attributes of acquired ethnographic data points turns mutually exclusive to the credibility status of IoT opinion molders while tweet properties contributes higher discriminative tendencies for identifying negative IoT emotions. The impact of Internet of Things on data science is likewise unraveled through bi-gram content analytics to identify topical discussions encapsulated in the acquired tweet corpus.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131859370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051622
C. I. Nwakanma, Love Allen Chijioke Ahakonye, J. Njoku, Joy Eze, Dong‐Seong Kim
Protecting the industrial internet of things (IIoT) devices through vulnerability detection is critical as the consequences of attacks can be devastating. Machine learning (ML) has assisted several works in this regard, improving vulnerability detection accuracy. Based on established vulnerability assessment, development and performance comparison of various ML detection algorithms is essential. This work presents a description of the IIoT protocols and their vulnerabilities. The performance of the ML-based detection system was developed using the WUSTL-IIoT-2018 dataset for industrial control systems (SCADA) cy-bersecurity research. The approach was validated using the ICS-SCADA and CICDDoS2019 datasets, a recent dataset that captures new dimensions of distributed denial of service (DDoS) attacks on networks. The evaluation and validation results show that the proposed scheme could help with high vulnerability detection and mitigation accuracy across all evaluated datasets.
{"title":"Effective Industrial Internet of Things Vulnerability Detection Using Machine Learning","authors":"C. I. Nwakanma, Love Allen Chijioke Ahakonye, J. Njoku, Joy Eze, Dong‐Seong Kim","doi":"10.1109/ITED56637.2022.10051622","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051622","url":null,"abstract":"Protecting the industrial internet of things (IIoT) devices through vulnerability detection is critical as the consequences of attacks can be devastating. Machine learning (ML) has assisted several works in this regard, improving vulnerability detection accuracy. Based on established vulnerability assessment, development and performance comparison of various ML detection algorithms is essential. This work presents a description of the IIoT protocols and their vulnerabilities. The performance of the ML-based detection system was developed using the WUSTL-IIoT-2018 dataset for industrial control systems (SCADA) cy-bersecurity research. The approach was validated using the ICS-SCADA and CICDDoS2019 datasets, a recent dataset that captures new dimensions of distributed denial of service (DDoS) attacks on networks. The evaluation and validation results show that the proposed scheme could help with high vulnerability detection and mitigation accuracy across all evaluated datasets.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133859030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051575
J. Ndunagu, R. Jimoh, Ugwuegbulam Chidiebere, George Deborah. Opeoluwa
The data gleaned from the National Open University of Nigeria's (NOUN) E-ticketing system was studied in this paper. NOUN is one of the Open and Distance Learning (ODL) institutions, where students and their facilitators are in different physical locations. Multinomial Naive Bayes algorithm, preferred using “intent” for its classification method for the chatbot system. Within 4 months of the launch of the NOUN E-ticketing system, 38,263 tickets (students' complaints and inquiries) were generated and 30,601 have been manually responded to and closed while the remaining 7,662 tickets are still in progress. The chatbot's goal is to respond to students inquiries quickly and efficiently while easing the burden on the management system. With the availability of chatbot, students' responses will be automated and accessible 24/7. The NOUN chatbot will increase student engagement, strengthen communication and create a seamless interaction for both the ODL institutions and its students all together culminating to a robust congenial student-ODL relationship ultimately leading to a higher attraction rate and more importantly, a lower attrition rate, not just in ODL institutions alone, but to other conventional higher institutions.
{"title":"Enhanced Open and Distance Learning Using an Artificial Intelligence (AI)-Powered Chatbot: a Conceptual Framework","authors":"J. Ndunagu, R. Jimoh, Ugwuegbulam Chidiebere, George Deborah. Opeoluwa","doi":"10.1109/ITED56637.2022.10051575","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051575","url":null,"abstract":"The data gleaned from the National Open University of Nigeria's (NOUN) E-ticketing system was studied in this paper. NOUN is one of the Open and Distance Learning (ODL) institutions, where students and their facilitators are in different physical locations. Multinomial Naive Bayes algorithm, preferred using “intent” for its classification method for the chatbot system. Within 4 months of the launch of the NOUN E-ticketing system, 38,263 tickets (students' complaints and inquiries) were generated and 30,601 have been manually responded to and closed while the remaining 7,662 tickets are still in progress. The chatbot's goal is to respond to students inquiries quickly and efficiently while easing the burden on the management system. With the availability of chatbot, students' responses will be automated and accessible 24/7. The NOUN chatbot will increase student engagement, strengthen communication and create a seamless interaction for both the ODL institutions and its students all together culminating to a robust congenial student-ODL relationship ultimately leading to a higher attraction rate and more importantly, a lower attrition rate, not just in ODL institutions alone, but to other conventional higher institutions.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123828285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051603
Adaora Obayi, Obinna Onyedeke, I. Uzo, Azuka Ijeomah
This paper presents a systematic review of android app respiratory system on smartphone. For some diseases, doctors have succeeded in inventing the necessary treatments that lasts for a short period, but in several cases, the treatment can stay for a lifetime. The goal of this system is to detect if a patient has any respiratory disease(s) by specifying the symptoms the patient encounters, schedules an appointement in the hospital for patient through the system to the linked specialist doctors to avoid contact in the case of Covid-19 patient. This research will help raise patient's awareness of the high risk of late discovery of having respiratory diseases (like Lung Cancer. corona virus etc), and also to develop a model that will help detect this disease early through mobile application. The focus of this review is to encourage medical institutions to adopt the health android app that can help patients in self-managing behavioral activities such as physical activities, using symptoms to determine the stage(early or critical) of the disease and drug suggestions with research evaluation using the app, this could help patients monitor and manage their health conditions.
{"title":"Android Application for Human Respiratory System Diagnosis: A Systematic Review","authors":"Adaora Obayi, Obinna Onyedeke, I. Uzo, Azuka Ijeomah","doi":"10.1109/ITED56637.2022.10051603","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051603","url":null,"abstract":"This paper presents a systematic review of android app respiratory system on smartphone. For some diseases, doctors have succeeded in inventing the necessary treatments that lasts for a short period, but in several cases, the treatment can stay for a lifetime. The goal of this system is to detect if a patient has any respiratory disease(s) by specifying the symptoms the patient encounters, schedules an appointement in the hospital for patient through the system to the linked specialist doctors to avoid contact in the case of Covid-19 patient. This research will help raise patient's awareness of the high risk of late discovery of having respiratory diseases (like Lung Cancer. corona virus etc), and also to develop a model that will help detect this disease early through mobile application. The focus of this review is to encourage medical institutions to adopt the health android app that can help patients in self-managing behavioral activities such as physical activities, using symptoms to determine the stage(early or critical) of the disease and drug suggestions with research evaluation using the app, this could help patients monitor and manage their health conditions.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"301 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122014703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051295
I. Yau, S. Sani, A. D. Usman, A. Tekanyi, A. M. Abba, D. Gambo
The use of Plastic Optical Fiber (POF) cable has the potential to enhance broadband transmission, particularly in the indoor access network. The POF cable offers a cost-effective solution because it is much easier to install and less expensive than glass optical fiber. The biggest drawback of the POF cable is intermodal dispersion, which reduces the link bandwidth of a 100 m length of cable to approximately 40 MHz. Numerous studies are being conducted to enhance the bandwidth-length product of POF. This work seeks to minimize the intermodal dispersion of the cable by finding optimal materials for the core and cladding of the cable with the improved bandwidth-length product. The results obtained indicate that the optimal core and cladding materials have refractive indices of 1.4865 and 1.4756, respectively. The intermodal dispersion per unit length is found to be 36.169 ps/m. The bandwidth of 100 m of the improved POF cable is therefore 121.65 MHz. A RoF communication system based on the developed POF is designed using the Optisystem16 software tool. The Bit Error Rate (BER) performance of the system in terms of quality factor is evaluated. A maximum achievable POF cable length of 117 m is obtained for a transmission data rate of 1 Gbps with an acceptable quality factor of 7.0.
{"title":"Enhancing the Transmission Performance of Step Index Plastic Optical Fiber","authors":"I. Yau, S. Sani, A. D. Usman, A. Tekanyi, A. M. Abba, D. Gambo","doi":"10.1109/ITED56637.2022.10051295","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051295","url":null,"abstract":"The use of Plastic Optical Fiber (POF) cable has the potential to enhance broadband transmission, particularly in the indoor access network. The POF cable offers a cost-effective solution because it is much easier to install and less expensive than glass optical fiber. The biggest drawback of the POF cable is intermodal dispersion, which reduces the link bandwidth of a 100 m length of cable to approximately 40 MHz. Numerous studies are being conducted to enhance the bandwidth-length product of POF. This work seeks to minimize the intermodal dispersion of the cable by finding optimal materials for the core and cladding of the cable with the improved bandwidth-length product. The results obtained indicate that the optimal core and cladding materials have refractive indices of 1.4865 and 1.4756, respectively. The intermodal dispersion per unit length is found to be 36.169 ps/m. The bandwidth of 100 m of the improved POF cable is therefore 121.65 MHz. A RoF communication system based on the developed POF is designed using the Optisystem16 software tool. The Bit Error Rate (BER) performance of the system in terms of quality factor is evaluated. A maximum achievable POF cable length of 117 m is obtained for a transmission data rate of 1 Gbps with an acceptable quality factor of 7.0.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127491052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051219
Marcus Musa Magaji, Abayomi Jegede, Nentawe Gurumdimma, M. Onoja, G. Aimufua, A. Oloyede
Network security personnel are expected to provide uninterrupted services by handling attacks irrespective of the modus operandi. Multiple defensive approaches to prevent, curtail, or mitigate an attack are the primary responsibilities of a security personnel. Considering the fact that, predicting security attacks is an additional technique currently used by most organizations to accurately measure the security risks related to overall system performance, several approaches have been used to predict network security attacks. However, high predicting accuracy and difficulty in analyzing very large amount of dataset and getting a reliable dataset seem to be the major constraints. The uncertain behavior would be subjected to verification and validation by the network administrator. KDDD CUPP 99 dataset and NSL KDD dataset were both used in the research. NSL KDD provides 0.997 average micro and macro accuracy, having average LogLoss of 0.16 and average LogLossReduction of 0.976. Log-Loss Reduction ranges from infinity to 1, where 1 and 0 represent perfect prediction and mean prediction respectively. Log-Loss reduction should be as close to 1 as possible for a good model. Log-Loss in the classification is an evaluation metrics that characterized the accuracy of a classifier. Log-loss is a measure of the performance of a classifier where the prediction input is a probability value between “0.00 to 1.00”. It should be as close to zero as possible. This paper proposes a FastTree Model for predicting network security incidents. Therefore, ML.NET Framework and FastTree Regression Technique have a high prediction accuracy and ability to analyze large datasets of normal, abnormal and uncertain behaviors.
{"title":"Fast Tree Model for Predicting Network Security Incidents","authors":"Marcus Musa Magaji, Abayomi Jegede, Nentawe Gurumdimma, M. Onoja, G. Aimufua, A. Oloyede","doi":"10.1109/ITED56637.2022.10051219","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051219","url":null,"abstract":"Network security personnel are expected to provide uninterrupted services by handling attacks irrespective of the modus operandi. Multiple defensive approaches to prevent, curtail, or mitigate an attack are the primary responsibilities of a security personnel. Considering the fact that, predicting security attacks is an additional technique currently used by most organizations to accurately measure the security risks related to overall system performance, several approaches have been used to predict network security attacks. However, high predicting accuracy and difficulty in analyzing very large amount of dataset and getting a reliable dataset seem to be the major constraints. The uncertain behavior would be subjected to verification and validation by the network administrator. KDDD CUPP 99 dataset and NSL KDD dataset were both used in the research. NSL KDD provides 0.997 average micro and macro accuracy, having average LogLoss of 0.16 and average LogLossReduction of 0.976. Log-Loss Reduction ranges from infinity to 1, where 1 and 0 represent perfect prediction and mean prediction respectively. Log-Loss reduction should be as close to 1 as possible for a good model. Log-Loss in the classification is an evaluation metrics that characterized the accuracy of a classifier. Log-loss is a measure of the performance of a classifier where the prediction input is a probability value between “0.00 to 1.00”. It should be as close to zero as possible. This paper proposes a FastTree Model for predicting network security incidents. Therefore, ML.NET Framework and FastTree Regression Technique have a high prediction accuracy and ability to analyze large datasets of normal, abnormal and uncertain behaviors.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129424558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051337
Wasiu Akanji, O. Okey, Saheed Adelanwa, Oluwafunsho Odesanya, T. Olaleye, Mary Amusu, Akinfolarin Akinrinlola, Abiodun Oladejo
Image steganalysis have been a prominent study in digital forensics and the data science use case of artificial intelligence has been widely adopted in conceptual frameworks. In existing studies, deep learners gain prominence for intrusion detection systems while other dissimilar modules are used for feature extraction. Hence, this study rather employs deep learners as image embedding networks aimed at feature extraction for a predictive analytics of image steganalysis. The extracted numeric image descriptors trains three learner algorithms for pattern recognition using a 10 fold cross-validation system. Experimental result indicates the ensemble of Random forest algorithm and SqueezeNet image embedder as the best for steganalysis in digital forensics while the size of the training set turns out to be insignificant for the supervised machine learning study.
{"title":"A blind steganalysis-based predictive analytics of numeric image descriptors for digital forensics with Random Forest & SqueezeNet","authors":"Wasiu Akanji, O. Okey, Saheed Adelanwa, Oluwafunsho Odesanya, T. Olaleye, Mary Amusu, Akinfolarin Akinrinlola, Abiodun Oladejo","doi":"10.1109/ITED56637.2022.10051337","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051337","url":null,"abstract":"Image steganalysis have been a prominent study in digital forensics and the data science use case of artificial intelligence has been widely adopted in conceptual frameworks. In existing studies, deep learners gain prominence for intrusion detection systems while other dissimilar modules are used for feature extraction. Hence, this study rather employs deep learners as image embedding networks aimed at feature extraction for a predictive analytics of image steganalysis. The extracted numeric image descriptors trains three learner algorithms for pattern recognition using a 10 fold cross-validation system. Experimental result indicates the ensemble of Random forest algorithm and SqueezeNet image embedder as the best for steganalysis in digital forensics while the size of the training set turns out to be insignificant for the supervised machine learning study.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130572146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051347
A. Musa, M. Adio, N. Faruk
Evolution of technology has been rapid in recent years and with the wide acceptance of 5G/6G networks, Internet of Things (IoT), Machine Learning (ML), blockchain technologies, there must be a methodology of interconnecting them across large proximity or regions to centralized servers. Existing mobile technologies are not suitable to ensure communications between these devices, and this has necessitated the need to create Low Power Wide Area Network (LPWAN). The LPWAN, assuming a set of networks, is considered to have subsets including Sigfox, LoRaWAN, NB-IoT and others. For any real system, there is always collision between packets upon arrival, thereby subtracting and extracting the weaker ones. In this work, a Repetitive Interference Mitigation algorithm is proposed, with focus on power differences between User Equipment (UE) and guard subcarriers. This process ensures decryption of the packets that arrive at the same time. Usage of just one guard subcarriers and multiple iterations of the developed technique is suitable to ensure a very good system performance and has a throughput of 36% as compared with other methods.
{"title":"Development of Interference Mitigation Technique for Low Power Wide Area Network","authors":"A. Musa, M. Adio, N. Faruk","doi":"10.1109/ITED56637.2022.10051347","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051347","url":null,"abstract":"Evolution of technology has been rapid in recent years and with the wide acceptance of 5G/6G networks, Internet of Things (IoT), Machine Learning (ML), blockchain technologies, there must be a methodology of interconnecting them across large proximity or regions to centralized servers. Existing mobile technologies are not suitable to ensure communications between these devices, and this has necessitated the need to create Low Power Wide Area Network (LPWAN). The LPWAN, assuming a set of networks, is considered to have subsets including Sigfox, LoRaWAN, NB-IoT and others. For any real system, there is always collision between packets upon arrival, thereby subtracting and extracting the weaker ones. In this work, a Repetitive Interference Mitigation algorithm is proposed, with focus on power differences between User Equipment (UE) and guard subcarriers. This process ensures decryption of the packets that arrive at the same time. Usage of just one guard subcarriers and multiple iterations of the developed technique is suitable to ensure a very good system performance and has a throughput of 36% as compared with other methods.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124997412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051589
C. Nwankwo, Francis Uwadia, W. Nwankwo, Wifred Adigwe, P. Chinedu, Emmanuel Ojei
In recent times, cybercrimes, kidnapping, and ritual killings are being enabled through the use and abuse of social media technologies and students are becoming cheap targets. Consequently, this study seeks to investigate the imperative of electronic communication styles among students via social media channels vis-a-vis the users' resilience before and during communication on social media to ensure that the message is routed to the intended recipient. In this study, we adopted the case study approach and 3500 students were drawn from different academic programmes in a known tertiary institution in Southern Nigeria. Validly completed questionnaires from 1000 students were analyzed. Findings revealed that 96% of the students who use social media are not concerned with any form of security screening before sending messages on social media networks via their smartphones.
{"title":"Privacy and Security of Content: A Study of User-resilience and Pre-checks on Social Media","authors":"C. Nwankwo, Francis Uwadia, W. Nwankwo, Wifred Adigwe, P. Chinedu, Emmanuel Ojei","doi":"10.1109/ITED56637.2022.10051589","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051589","url":null,"abstract":"In recent times, cybercrimes, kidnapping, and ritual killings are being enabled through the use and abuse of social media technologies and students are becoming cheap targets. Consequently, this study seeks to investigate the imperative of electronic communication styles among students via social media channels vis-a-vis the users' resilience before and during communication on social media to ensure that the message is routed to the intended recipient. In this study, we adopted the case study approach and 3500 students were drawn from different academic programmes in a known tertiary institution in Southern Nigeria. Validly completed questionnaires from 1000 students were analyzed. Findings revealed that 96% of the students who use social media are not concerned with any form of security screening before sending messages on social media networks via their smartphones.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116972017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051199
S. A. Salihu, I. D. Oladipo, Abdul Afeez Wojuade, M. Abdulraheem, Abdulrauph Babatunde, A. Ajiboye, G. B. Balogun
Phishing is one of the types of cybercrime in which the attacker poses as a trustworthy entity with a view to obtaining sensitive information or data from the victim, this occurs usually through email. In the process, the victim may release information such as login credentials, credit card details, and other personally identifiable information that normally should not be revealed. The existing approaches used for phishing detection, therefore, need to be enhanced to effectively detect phishing. This study proposed a novel method for detecting phishing based on some heuristic features by extracting some relevant attributes, filtering these attributes, and classifying the same according to their impact on a website. The data explored for this study was retrieved from PhishTank and Alexa, which was later preprocessed for smooth model creation in python. The model created was evaluated and consistently gives a true positive rate of 85% based on the threshold set and an accuracy of 95.52%. The resulting output of this study has shown its reliability in the detection of phishing and could serve as a good benchmark for similar studies.
{"title":"Detection of Phishing URLs Using Heuristics-Based Approach","authors":"S. A. Salihu, I. D. Oladipo, Abdul Afeez Wojuade, M. Abdulraheem, Abdulrauph Babatunde, A. Ajiboye, G. B. Balogun","doi":"10.1109/ITED56637.2022.10051199","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051199","url":null,"abstract":"Phishing is one of the types of cybercrime in which the attacker poses as a trustworthy entity with a view to obtaining sensitive information or data from the victim, this occurs usually through email. In the process, the victim may release information such as login credentials, credit card details, and other personally identifiable information that normally should not be revealed. The existing approaches used for phishing detection, therefore, need to be enhanced to effectively detect phishing. This study proposed a novel method for detecting phishing based on some heuristic features by extracting some relevant attributes, filtering these attributes, and classifying the same according to their impact on a website. The data explored for this study was retrieved from PhishTank and Alexa, which was later preprocessed for smooth model creation in python. The model created was evaluated and consistently gives a true positive rate of 85% based on the threshold set and an accuracy of 95.52%. The resulting output of this study has shown its reliability in the detection of phishing and could serve as a good benchmark for similar studies.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"239 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133638343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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