Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051492
Cecilia Ajowho Adenusi, Olufunke Rebecca Vincent, Abayomi-Alli A., Olaniyi Mathew Olayiwola, Bakare Olawunmi Shamsudeen, Sayikanmi Titilayo Mary
Researchers and investors have been paying close attention to the application of Artificial Intelligence models to the economics, agriculture and other fields in recent years. This study uses a Multilayer Perceptron Artificial Neural Network to anticipate the effect of covid-19 on crude-oil prices, continuing the deep learning trend and also applied the use of time series model known as Autoregressive Integrated Moving Average (ARIMA) to validate the result gotten from MLP-ANN. The results produced accurately predicted crude oil prices, and covid-19 data was also analyzed, as well as the association between crude-oil prices and covid-19. Because of the substantial causative association between the coronavirus (number of confirmed cases), crude oil prices, this study is intriguing. Ten years forecast was done using both MLP-ANN and ARIMA and from result gotten, MLP-ANN has accuracy of 96% while ARIMA has 39% accuracy.
{"title":"PREDICTING THE UPSHOT OF COVID-19 ON CRUDE-OIL PRICES IN NIGERIA USING MLPARIMA MODEL","authors":"Cecilia Ajowho Adenusi, Olufunke Rebecca Vincent, Abayomi-Alli A., Olaniyi Mathew Olayiwola, Bakare Olawunmi Shamsudeen, Sayikanmi Titilayo Mary","doi":"10.1109/ITED56637.2022.10051492","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051492","url":null,"abstract":"Researchers and investors have been paying close attention to the application of Artificial Intelligence models to the economics, agriculture and other fields in recent years. This study uses a Multilayer Perceptron Artificial Neural Network to anticipate the effect of covid-19 on crude-oil prices, continuing the deep learning trend and also applied the use of time series model known as Autoregressive Integrated Moving Average (ARIMA) to validate the result gotten from MLP-ANN. The results produced accurately predicted crude oil prices, and covid-19 data was also analyzed, as well as the association between crude-oil prices and covid-19. Because of the substantial causative association between the coronavirus (number of confirmed cases), crude oil prices, this study is intriguing. Ten years forecast was done using both MLP-ANN and ARIMA and from result gotten, MLP-ANN has accuracy of 96% while ARIMA has 39% accuracy.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"54 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":"133804780","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}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051577
H. E. Amhenrior, I. Oloma, Braimoh A. Ikharo
This paper presents a GSM and Wi-Fi based Low Cost Single phase Smart Prepaid Energy Meter with some functionalities such as keypad and screen implemented in embedded software. The design was borne out of the desire to ensure that smart prepaid meters are made available at a reduced cost. The methodology involves hardware and software. The hardware made use of integrated circuits (ICs) and discrete components. At the heart of the design is Atmega644P microcontroller which was interfaced to ADE7755 energy meter IC, GSM800L modem, Wi-Fi module, tamper switch, buzzer and other components. The microcontroller is used for monitoring and controlling the activities of the meter especially the ADE7755 used in producing pulses from consumption which are then measured and recorded by the microcontroller as Energy measurement. SIM800L was used to achieve communication between the meter and the web server. The Wi-Fi is used to access the meter for recharging. The software aspect involved the programming of the microcontroller using C++ and the use of a webpage with an associated web server for meter administration and control as well as meter token recharge validation. The prototype performed satisfactorily under different loads. Other functionalities such as tampering detection, token recharge, meter registration and meter information viewing on the webpage were successfully executed. Again, the cost of the meter is less than the cost of commercially available meters in Nigeria by N18,313.39. The GSM and Wi-Fi low cost smart energy meter designed and implemented worked satisfactorily and it is effective.
{"title":"Design and Implementation of a GSM and Wi-Fi Based Low Cost Smart Prepaid Energy Meter","authors":"H. E. Amhenrior, I. Oloma, Braimoh A. Ikharo","doi":"10.1109/ITED56637.2022.10051577","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051577","url":null,"abstract":"This paper presents a GSM and Wi-Fi based Low Cost Single phase Smart Prepaid Energy Meter with some functionalities such as keypad and screen implemented in embedded software. The design was borne out of the desire to ensure that smart prepaid meters are made available at a reduced cost. The methodology involves hardware and software. The hardware made use of integrated circuits (ICs) and discrete components. At the heart of the design is Atmega644P microcontroller which was interfaced to ADE7755 energy meter IC, GSM800L modem, Wi-Fi module, tamper switch, buzzer and other components. The microcontroller is used for monitoring and controlling the activities of the meter especially the ADE7755 used in producing pulses from consumption which are then measured and recorded by the microcontroller as Energy measurement. SIM800L was used to achieve communication between the meter and the web server. The Wi-Fi is used to access the meter for recharging. The software aspect involved the programming of the microcontroller using C++ and the use of a webpage with an associated web server for meter administration and control as well as meter token recharge validation. The prototype performed satisfactorily under different loads. Other functionalities such as tampering detection, token recharge, meter registration and meter information viewing on the webpage were successfully executed. Again, the cost of the meter is less than the cost of commercially available meters in Nigeria by N18,313.39. The GSM and Wi-Fi low cost smart energy meter designed and implemented worked satisfactorily and it is effective.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"14 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":"133683565","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/ITED56637.2022.10051575
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/ITED56637.2022.10051416
O. Boyinbode, Fiyinfoluwa G. Oyesanmi, Olumide Obe, O.F. Boyinbode
The lingering cases of building collapse in Nigeria and the attending rate of mortality call for urgent attention. Advances in Internet of Things (IoT) technology can be leveraged to facilitate the autonomous monitoring of buildings. In this paper, an IoT model was implemented using piezo electronic transducers and programmable microcontrollers to monitor structural health as well as a warning system to notify users when structural integrity becomes threatened. The data obtained from the sensors are stored on the cloud and can be used to develop a robust building information system.
{"title":"Implementation of Internet of Things for Structural Health Monitoring in Nigeria","authors":"O. Boyinbode, Fiyinfoluwa G. Oyesanmi, Olumide Obe, O.F. Boyinbode","doi":"10.1109/ITED56637.2022.10051416","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051416","url":null,"abstract":"The lingering cases of building collapse in Nigeria and the attending rate of mortality call for urgent attention. Advances in Internet of Things (IoT) technology can be leveraged to facilitate the autonomous monitoring of buildings. In this paper, an IoT model was implemented using piezo electronic transducers and programmable microcontrollers to monitor structural health as well as a warning system to notify users when structural integrity becomes threatened. The data obtained from the sensors are stored on the cloud and can be used to develop a robust building information system.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"8 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":"130321909","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/ITED56637.2022.10051271
T. M. Okediran, O. R. Vincent, A. O. Agbeyangi, A. Abayomi-Alli, O. Adeniran
House numbering is the act of assigning a unique number to each building in a street or area in order to make it easier to locate a specific building. Due to poor town and regional planning, street naming and house numbering are major challenges in Nigeria. The disadvantage is being unable to identify a specific house in a location. The purpose of this study is to use the Internet of Things (IoT) as a solution to address the issue of house numbering, specifically in the Ojo Local Government Area of Lagos State, by identifying houses on the street, numbering them, classifying the type of building, and storing the data in a database. The study employs a machine learning technique, the k-nearest neighbor classifier, to train and program the IoT device, with fifty houses serving as a case study. The work was tested using fifty houses to name the street, number the houses, and categorize them into five major groups. The use of Google Maps aided in determining the name and location of a street. The success rate was as high as 0.97 for training and testing data, indicating that the technique used is adequate to address street name and house numbering problems.
{"title":"Solving the House Numbering Problem in Nigeria: Internet of Things (IoT) As An Emerging Solution","authors":"T. M. Okediran, O. R. Vincent, A. O. Agbeyangi, A. Abayomi-Alli, O. Adeniran","doi":"10.1109/ITED56637.2022.10051271","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051271","url":null,"abstract":"House numbering is the act of assigning a unique number to each building in a street or area in order to make it easier to locate a specific building. Due to poor town and regional planning, street naming and house numbering are major challenges in Nigeria. The disadvantage is being unable to identify a specific house in a location. The purpose of this study is to use the Internet of Things (IoT) as a solution to address the issue of house numbering, specifically in the Ojo Local Government Area of Lagos State, by identifying houses on the street, numbering them, classifying the type of building, and storing the data in a database. The study employs a machine learning technique, the k-nearest neighbor classifier, to train and program the IoT device, with fifty houses serving as a case study. The work was tested using fifty houses to name the street, number the houses, and categorize them into five major groups. The use of Google Maps aided in determining the name and location of a street. The success rate was as high as 0.97 for training and testing data, indicating that the technique used is adequate to address street name and house numbering problems.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"1 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":"129662759","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/ITED56637.2022.10051602
Ralivat Haruna, A. Obiniyi, Muhammed Abdulkarim, A.A. Afolorunsho
As technology advances, the volume of textual material produced on the web has been steadily rising. It can take a lot of time and effort to extract useful information from textual data. Automatic text summarizing aims to create concise summaries that retain the most important parts of the source document. Transformer-based architectures have demonstrated excellently in Natural Language Processing (NLP), particularly when it comes to summarizing textual content. This paper presents a thorough analysis of the most recent advancements in transformer topologies for automatic text summarization, with a focus on the Bidirectional Autoregressive Transformer (BART). This paper highlights future directions for research on transformer-like models for autonomous text summarization, such as BART and BERT.
{"title":"Automatic Summarization of Scientific Documents Using Transformer Architectures: A Review","authors":"Ralivat Haruna, A. Obiniyi, Muhammed Abdulkarim, A.A. Afolorunsho","doi":"10.1109/ITED56637.2022.10051602","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051602","url":null,"abstract":"As technology advances, the volume of textual material produced on the web has been steadily rising. It can take a lot of time and effort to extract useful information from textual data. Automatic text summarizing aims to create concise summaries that retain the most important parts of the source document. Transformer-based architectures have demonstrated excellently in Natural Language Processing (NLP), particularly when it comes to summarizing textual content. This paper presents a thorough analysis of the most recent advancements in transformer topologies for automatic text summarization, with a focus on the Bidirectional Autoregressive Transformer (BART). This paper highlights future directions for research on transformer-like models for autonomous text summarization, such as BART and BERT.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"76 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":"127237901","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/ITED56637.2022.10051458
Donatus Uwadiae Irughe, Wilson Nwankwo, C. Nwankwo, Francis Uwadia
The rate of cyber-attacks on enterprise network is increasing daily, accounting for losses in billions of dollars yearly. Professor Soren Morgan once asserted that if it were measured as a country, then the damages from cybercrime alone estimated at $6 trillion globally in 2021, would be the world's third-largest economy after the U.S. and China. This study examines the implications of cyber resilience on the security of enterprise network operations with a view to ascertaining how organizations in different economic sectors adopt cyber resilience strategies towards protecting their network resources against data and privacy violations. Twenty organizations in three different sectors were studied. The findings showed that organizations in the two sectors: oil and gas, and finance and banking, demonstrate better cyber security infrastructure and the adoption of cyber resilience strategies respectively, whereas, organizations in the higher education sector did not show remarkable commitment to the adoption of good cyber security practices. Overall, all organizations appear rather slow in the implementation of multi-level resilience strategies.
{"title":"Resilience and Security on Enterprise Networks: A Multi-Sector Study","authors":"Donatus Uwadiae Irughe, Wilson Nwankwo, C. Nwankwo, Francis Uwadia","doi":"10.1109/ITED56637.2022.10051458","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051458","url":null,"abstract":"The rate of cyber-attacks on enterprise network is increasing daily, accounting for losses in billions of dollars yearly. Professor Soren Morgan once asserted that if it were measured as a country, then the damages from cybercrime alone estimated at $6 trillion globally in 2021, would be the world's third-largest economy after the U.S. and China. This study examines the implications of cyber resilience on the security of enterprise network operations with a view to ascertaining how organizations in different economic sectors adopt cyber resilience strategies towards protecting their network resources against data and privacy violations. Twenty organizations in three different sectors were studied. The findings showed that organizations in the two sectors: oil and gas, and finance and banking, demonstrate better cyber security infrastructure and the adoption of cyber resilience strategies respectively, whereas, organizations in the higher education sector did not show remarkable commitment to the adoption of good cyber security practices. Overall, all organizations appear rather slow in the implementation of multi-level resilience strategies.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"12 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":"127709165","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/ITED56637.2022.10051478
Hakeem Babalola Akande, O. Abikoye, O. Akande, R. Jimoh
Metaheuristic algorithms such as Ant Colony Optimization (ACO) algorithm and Bat Optimization Algorithm (BOA) have been widely employed in solving different optimization problems in several fields. ACO is modelled based on the social behaviour of ants that look for appropriate answers to a given optimization issue by recasting it as the case of locating the least expensive path on a weighted graph. A set of parameters linked to graph components (either nodes or edges) whose values are changed by the ants during runtime constitute the pheromone model, which biases the stochastic solution generation process. However, the effectiveness of ACO declines as the quantity of packets rises, making them ineffective for reducing traffic congestion. As more packets are transmitted, their strength decreases, causing packet congestion, rendering them useless for reducing packet traffic congestion. On the contrary, BOA which was modelled after the behavior of bats has also been employed in fixing network routing issues by listening to every sound in a space and taking note of what is going on around it. In order to further improve ACO algorithm and decrease packet traffic congestion, packet loss, and the time it takes a packet to reach its destination in a network system, this study employs the strength of BOA. Results obtained revealed the prowess of BOA in improving the performance of ACO for network packet routing.
{"title":"Improving Optimization Prowess of Ant Colony Algorithm Using Bat Inspired Algorithm","authors":"Hakeem Babalola Akande, O. Abikoye, O. Akande, R. Jimoh","doi":"10.1109/ITED56637.2022.10051478","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051478","url":null,"abstract":"Metaheuristic algorithms such as Ant Colony Optimization (ACO) algorithm and Bat Optimization Algorithm (BOA) have been widely employed in solving different optimization problems in several fields. ACO is modelled based on the social behaviour of ants that look for appropriate answers to a given optimization issue by recasting it as the case of locating the least expensive path on a weighted graph. A set of parameters linked to graph components (either nodes or edges) whose values are changed by the ants during runtime constitute the pheromone model, which biases the stochastic solution generation process. However, the effectiveness of ACO declines as the quantity of packets rises, making them ineffective for reducing traffic congestion. As more packets are transmitted, their strength decreases, causing packet congestion, rendering them useless for reducing packet traffic congestion. On the contrary, BOA which was modelled after the behavior of bats has also been employed in fixing network routing issues by listening to every sound in a space and taking note of what is going on around it. In order to further improve ACO algorithm and decrease packet traffic congestion, packet loss, and the time it takes a packet to reach its destination in a network system, this study employs the strength of BOA. Results obtained revealed the prowess of BOA in improving the performance of ACO for network packet routing.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"25 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":"131237337","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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