Pub Date : 2024-06-08DOI: 10.5815/ijitcs.2024.03.07
Dr. Bala Dhandayuthapani V
Python is popular in artificial intelligence (AI) and machine learning (ML) due to its versatility, adaptability, rich libraries, and active community. The existing Python interoperability in Java was investigated using socket programming on a non-graphical user interface (GUI). Python's data analysis library modules such as numpy, pandas, and scipy, together with visualization library modules such as Matplotlib and Seaborn, and Scikit-learn for machine-learning, aim to integrate into Java graphical user interface (GUI) applications such as Java applets, Java Swing, and Java FX. The substantial method used in the integration process is TCP socket programming, which makes instruction and data transfers to provide interoperability between Python and Java GUIs. This empirical research integrates Python data analysis and visualization graphs into Java applications and does not require any additional libraries or third-party libraries. The experimentation confirmed the advantages and challenges of this integration with a concrete solution. The intended audience for this research extends to software developers, data analysts, and scientists, recognizing Python's broad applicability to artificial intelligence (AI) and machine learning (ML). The integration of data analysis and visualization and machine-learning functionalities within the Java GUI. It emphasizes the self-sufficiency of the integration process and suggests future research directions, including comparative analysis with Java's native capabilities, interactive data visualization using libraries like Altair, Bokeh, Plotly, and Pygal, performance and security considerations, and no-code and low-code implementations.
{"title":"Python Data Analysis and Visualization in Java GUI Applications Through TCP Socket Programming","authors":"Dr. Bala Dhandayuthapani V","doi":"10.5815/ijitcs.2024.03.07","DOIUrl":"https://doi.org/10.5815/ijitcs.2024.03.07","url":null,"abstract":"Python is popular in artificial intelligence (AI) and machine learning (ML) due to its versatility, adaptability, rich libraries, and active community. The existing Python interoperability in Java was investigated using socket programming on a non-graphical user interface (GUI). Python's data analysis library modules such as numpy, pandas, and scipy, together with visualization library modules such as Matplotlib and Seaborn, and Scikit-learn for machine-learning, aim to integrate into Java graphical user interface (GUI) applications such as Java applets, Java Swing, and Java FX. The substantial method used in the integration process is TCP socket programming, which makes instruction and data transfers to provide interoperability between Python and Java GUIs. This empirical research integrates Python data analysis and visualization graphs into Java applications and does not require any additional libraries or third-party libraries. The experimentation confirmed the advantages and challenges of this integration with a concrete solution. The intended audience for this research extends to software developers, data analysts, and scientists, recognizing Python's broad applicability to artificial intelligence (AI) and machine learning (ML). The integration of data analysis and visualization and machine-learning functionalities within the Java GUI. It emphasizes the self-sufficiency of the integration process and suggests future research directions, including comparative analysis with Java's native capabilities, interactive data visualization using libraries like Altair, Bokeh, Plotly, and Pygal, performance and security considerations, and no-code and low-code implementations.","PeriodicalId":130361,"journal":{"name":"International Journal of Information Technology and Computer Science","volume":" 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370032","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 : 2024-04-08DOI: 10.5815/ijitcs.2024.02.01
Matthew Willetts, Anthony S. Atkins
Small and medium-sized enterprises (SMEs) are the backbone of the global economy, constituting 90% of all businesses. Despite being widely adopted by large businesses who have reported numerous benefits including increased profitability and increased efficiency and a survey in 2017 of 50 Fortune 1000 and leading firms’ executives indicated that 48.4% of respondents confirmed they are achieving measurable results from their Big Data investments, with 80.7% confirming that they have generated business. Big Data Analytics is adopted by only 10% of SMEs. The paper outlines a review of Big Data Maturity Models and discusses their positive features and limitations. Previous research has analysed the barriers to adoption of Big Data Analytics in SMEs and a scoring tool has been developed to help SMEs adopt Big Data Analytics. The paper demonstrates that the scoring tool could be translated and compared to a Maturity Model to provide a visual representation of Big Data Analytics maturity and help SMEs to understand where they are on the journey. The paper outlines a case study to show a comparison to provide intuitive visual model to assist top management to improve their competitive advantage.
{"title":"Big Data Analytics Maturity Model for SMEs","authors":"Matthew Willetts, Anthony S. Atkins","doi":"10.5815/ijitcs.2024.02.01","DOIUrl":"https://doi.org/10.5815/ijitcs.2024.02.01","url":null,"abstract":"Small and medium-sized enterprises (SMEs) are the backbone of the global economy, constituting 90% of all businesses. Despite being widely adopted by large businesses who have reported numerous benefits including increased profitability and increased efficiency and a survey in 2017 of 50 Fortune 1000 and leading firms’ executives indicated that 48.4% of respondents confirmed they are achieving measurable results from their Big Data investments, with 80.7% confirming that they have generated business. Big Data Analytics is adopted by only 10% of SMEs. The paper outlines a review of Big Data Maturity Models and discusses their positive features and limitations. Previous research has analysed the barriers to adoption of Big Data Analytics in SMEs and a scoring tool has been developed to help SMEs adopt Big Data Analytics. The paper demonstrates that the scoring tool could be translated and compared to a Maturity Model to provide a visual representation of Big Data Analytics maturity and help SMEs to understand where they are on the journey. The paper outlines a case study to show a comparison to provide intuitive visual model to assist top management to improve their competitive advantage.","PeriodicalId":130361,"journal":{"name":"International Journal of Information Technology and Computer Science","volume":"148 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140731197","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 : 2024-04-08DOI: 10.5815/ijitcs.2024.02.04
I. Gusti, Agung Surya, Pramana Wijaya, Gusti Made, Arya Sasmita, Putu Agus, Eka Pratama
Education is a field that utilizes information technology to support academic and operational activities. One of the technologies widely used in the education sector is web-based applications. Web-based technologies are vulnerable to exploitation by attackers, which highlights the importance of ensuring strong security measures in web-based systems. As an educational organization, Udayana University utilizes a web-based application called OASE. OASE, being a web-based system, requires thorough security verification. Penetration testing is conducted to assess the security of OASE. This testing can be performed using the ISSAF and OSSTMM frameworks. The penetration testing based on the ISSAF framework consists of 9 steps, while the OSSTMM framework consists of 7 steps for assessment. The results of the OASE penetration testing revealed several system vulnerabilities. Throughout the ISSAF phases, only 4 vulnerabilities and 3 information-level vulnerabilities were identified in the final testing results of OASE. Recommendations for addressing these vulnerabilities are provided as follows. Implement a Web Application Firewall (WAF) to reduce the risk of common web attacks in the OASE web application. input and output validation to prevent the injection of malicious scripts addressing the stored XSS vulnerability. Update the server software regularly and directory permission checks to eliminate unnecessary information files and prevent unauthorized access. Configure a content security policy on the web server to ensure mitigation and prevent potential exploitation by attackers.
{"title":"Web Application Penetration Testing on Udayana University's OASE E-learning Platform Using Information System Security Assessment Framework (ISSAF) and Open Source Security Testing Methodology Manual (OSSTMM)","authors":"I. Gusti, Agung Surya, Pramana Wijaya, Gusti Made, Arya Sasmita, Putu Agus, Eka Pratama","doi":"10.5815/ijitcs.2024.02.04","DOIUrl":"https://doi.org/10.5815/ijitcs.2024.02.04","url":null,"abstract":"Education is a field that utilizes information technology to support academic and operational activities. One of the technologies widely used in the education sector is web-based applications. Web-based technologies are vulnerable to exploitation by attackers, which highlights the importance of ensuring strong security measures in web-based systems. As an educational organization, Udayana University utilizes a web-based application called OASE. OASE, being a web-based system, requires thorough security verification. Penetration testing is conducted to assess the security of OASE. This testing can be performed using the ISSAF and OSSTMM frameworks. The penetration testing based on the ISSAF framework consists of 9 steps, while the OSSTMM framework consists of 7 steps for assessment. The results of the OASE penetration testing revealed several system vulnerabilities. Throughout the ISSAF phases, only 4 vulnerabilities and 3 information-level vulnerabilities were identified in the final testing results of OASE. Recommendations for addressing these vulnerabilities are provided as follows. Implement a Web Application Firewall (WAF) to reduce the risk of common web attacks in the OASE web application. input and output validation to prevent the injection of malicious scripts addressing the stored XSS vulnerability. Update the server software regularly and directory permission checks to eliminate unnecessary information files and prevent unauthorized access. Configure a content security policy on the web server to ensure mitigation and prevent potential exploitation by attackers.","PeriodicalId":130361,"journal":{"name":"International Journal of Information Technology and Computer Science","volume":"86 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140729286","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 : 2024-04-08DOI: 10.5815/ijitcs.2024.02.02
Zhengbing Hu, I. Dychka, K. Potapova, Vasyl Meliukh
Sentiment analysis is a critical component in natural language processing applications, particularly for text classification. By employing state-of-the-art techniques such as ensemble methods, transfer learning and deep learning architectures, our methodology significantly enhances the robustness and precision of sentiment predictions. We systematically investigate the impact of various NLP models, including recurrent neural networks and transformer-based architectures, on sentiment classification tasks. Furthermore, we introduce a novel ensemble method that combines the strengths of multiple classifiers to improve the predictive ability of the system. The results demonstrate the potential of integrating state-of-the-art Natural Language Processing (NLP) models with ensemble classifiers to advance sentiment analysis. This lays the foundation for a more advanced comprehension of textual sentiments in diverse applications.
{"title":"Augmenting Sentiment Analysis Prediction in Binary Text Classification through Advanced Natural Language Processing Models and Classifiers","authors":"Zhengbing Hu, I. Dychka, K. Potapova, Vasyl Meliukh","doi":"10.5815/ijitcs.2024.02.02","DOIUrl":"https://doi.org/10.5815/ijitcs.2024.02.02","url":null,"abstract":"Sentiment analysis is a critical component in natural language processing applications, particularly for text classification. By employing state-of-the-art techniques such as ensemble methods, transfer learning and deep learning architectures, our methodology significantly enhances the robustness and precision of sentiment predictions. We systematically investigate the impact of various NLP models, including recurrent neural networks and transformer-based architectures, on sentiment classification tasks. Furthermore, we introduce a novel ensemble method that combines the strengths of multiple classifiers to improve the predictive ability of the system. The results demonstrate the potential of integrating state-of-the-art Natural Language Processing (NLP) models with ensemble classifiers to advance sentiment analysis. This lays the foundation for a more advanced comprehension of textual sentiments in diverse applications.","PeriodicalId":130361,"journal":{"name":"International Journal of Information Technology and Computer Science","volume":"140 S261","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140731216","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}
In the lightning-quick world of software development, it is essential to find the most effective and efficient development methodology. This thesis represents "Scrum Spiral" which is an improved hybrid software development model that combines the features of Scrum and Spiral approach to enhance the software development process. This thesis aims to identify the usefulness of "ScrumSpiral" methodology and compare it with other hybrid software development models to encourage its use in software development projects. To develop this hybrid model, we did extensive research on the software engineering domain and decided to create a hybrid model by using Scrum and Spiral, named "Scrum Spiral" which is suitable for complicated projects and also for those projects whose requirements are not fixed. Traditional software development models face numerous challenges in rapidly changing markets. By developing this kind of hybrid model, we want to overcome these kinds of limitations and present the software development community with a novel concept for better project results. Final outcome of this thesis was that we developed a model that should be able to complete the project according to the expected schedule, satisfy customer requirements, and obtain productivity through team coordination. The significance of the hybrid model "Scrum Spiral" is reflected in its ability to offer flexibility towards various size projects, proactive risk management to identify all risks before developing the system, and result in higher-quality outcomes for those projects whose requirements are not properly described initially in the project.
{"title":"ScrumSpiral: An Improved Hybrid Software Development Model","authors":"Tapu Biswas, Farhan Sadik Ferdous, Zinniya Taffannum Pritee, Akinul Islam Jony","doi":"10.5815/ijitcs.2024.02.05","DOIUrl":"https://doi.org/10.5815/ijitcs.2024.02.05","url":null,"abstract":"In the lightning-quick world of software development, it is essential to find the most effective and efficient development methodology. This thesis represents \"Scrum Spiral\" which is an improved hybrid software development model that combines the features of Scrum and Spiral approach to enhance the software development process. This thesis aims to identify the usefulness of \"ScrumSpiral\" methodology and compare it with other hybrid software development models to encourage its use in software development projects. To develop this hybrid model, we did extensive research on the software engineering domain and decided to create a hybrid model by using Scrum and Spiral, named \"Scrum Spiral\" which is suitable for complicated projects and also for those projects whose requirements are not fixed. Traditional software development models face numerous challenges in rapidly changing markets. By developing this kind of hybrid model, we want to overcome these kinds of limitations and present the software development community with a novel concept for better project results. Final outcome of this thesis was that we developed a model that should be able to complete the project according to the expected schedule, satisfy customer requirements, and obtain productivity through team coordination. The significance of the hybrid model \"Scrum Spiral\" is reflected in its ability to offer flexibility towards various size projects, proactive risk management to identify all risks before developing the system, and result in higher-quality outcomes for those projects whose requirements are not properly described initially in the project.","PeriodicalId":130361,"journal":{"name":"International Journal of Information Technology and Computer Science","volume":"77 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140729260","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 : 2024-04-08DOI: 10.5815/ijitcs.2024.02.03
I. M. Oyelade, O.K. Boyinbode, O. Adewale, E. Ibam
Farmland security in Nigeria is still a major challenge and existing methods such as building brick fences around the farmland, installing electric fences, setting up deterrent plants with spikey branches or those that have displeasing scents are no longer suitable for farmland security. This paper presents an IoT based farmland intrusion detection model using sensors and computer vision techniques. Passive Infrared (PIR) sensors and camera sensors are mounted in strategic positions on the farm. The PIR sensor senses motion by the radiation of body heat and sends a message to the raspberry pi to trigger the camera to take a picture of the scene. An improved Faster Region Based Convolutional Neural Network is developed and used for object detection and One-shot learning algorithm for face recognition in the case of a person. At the end of the detection and recognition stage, details of intrusion are sent to the farm owner through text message and email notification. The raspberry pi also turns on the wade off system to divert an intruding animal away. The model achieved an improved accuracy of 92.5% compared to previous methods and effectively controlled illegal entry into a farmland.
尼日利亚的农田安全仍然是一项重大挑战，现有的方法，如在农田周围建造砖砌围栏、安装电栅栏、种植带有尖刺或气味令人讨厌的植物等，已不再适用于农田安全。本文利用传感器和计算机视觉技术提出了一种基于物联网的农田入侵检测模型。被动红外（PIR）传感器和摄像头传感器安装在农场的战略位置。PIR 传感器通过体热辐射感应运动，并向 raspberry pi 发送信息，触发摄像头拍摄场景。开发并使用改进的基于区域的更快卷积神经网络进行物体检测，并使用单次学习算法进行人脸识别。在检测和识别阶段结束后，入侵的详细信息会通过短信和电子邮件通知农场主。树莓派还会打开涉水关闭系统，将入侵动物引开。与以前的方法相比，该模型的准确率提高了 92.5%，有效控制了非法进入农田的行为。
{"title":"Farmland Intrusion Detection using Internet of Things and Computer Vision Techniques","authors":"I. M. Oyelade, O.K. Boyinbode, O. Adewale, E. Ibam","doi":"10.5815/ijitcs.2024.02.03","DOIUrl":"https://doi.org/10.5815/ijitcs.2024.02.03","url":null,"abstract":"Farmland security in Nigeria is still a major challenge and existing methods such as building brick fences around the farmland, installing electric fences, setting up deterrent plants with spikey branches or those that have displeasing scents are no longer suitable for farmland security. This paper presents an IoT based farmland intrusion detection model using sensors and computer vision techniques. Passive Infrared (PIR) sensors and camera sensors are mounted in strategic positions on the farm. The PIR sensor senses motion by the radiation of body heat and sends a message to the raspberry pi to trigger the camera to take a picture of the scene. An improved Faster Region Based Convolutional Neural Network is developed and used for object detection and One-shot learning algorithm for face recognition in the case of a person. At the end of the detection and recognition stage, details of intrusion are sent to the farm owner through text message and email notification. The raspberry pi also turns on the wade off system to divert an intruding animal away. The model achieved an improved accuracy of 92.5% compared to previous methods and effectively controlled illegal entry into a farmland.","PeriodicalId":130361,"journal":{"name":"International Journal of Information Technology and Computer Science","volume":"181 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140731039","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 : 2024-02-08DOI: 10.5815/ijitcs.2024.01.01
M. S. Kumar, K. G. Reddy, Rakesh Kumar Donthi
Cloud fog computing is a new paradigm that combines cloud computing and fog computing to boost resource efficiency and distributed system performance. Task scheduling is crucial in cloud fog computing because it decides the way computer resources are divided up across tasks. Our study suggests that the Shark Search Krill Herd Optimization (SSKHOA) method be incorporated into cloud fog computing's task scheduling. To enhance both the global and local search capabilities of the optimization process, the SSKHOA algorithm combines the shark search algorithm and the krill herd algorithm. It quickly explores the solution space and finds near-optimal work schedules by modelling the swarm intelligence of krill herds and the predator-prey behavior of sharks. In order to test the efficacy of the SSKHOA algorithm, we created a synthetic cloud fog environment and performed some tests. Traditional task scheduling techniques like LTRA, DRL, and DAPSO were used to evaluate the findings. The experimental results demonstrate that the SSKHOA outperformed the baseline algorithms in terms of task success rate increased 34%, reduced the execution time by 36%, and reduced makespan time by 54% respectively.
{"title":"SSKHOA: Hybrid Metaheuristic Algorithm for Resource Aware Task Scheduling in Cloud-fog Computing","authors":"M. S. Kumar, K. G. Reddy, Rakesh Kumar Donthi","doi":"10.5815/ijitcs.2024.01.01","DOIUrl":"https://doi.org/10.5815/ijitcs.2024.01.01","url":null,"abstract":"Cloud fog computing is a new paradigm that combines cloud computing and fog computing to boost resource efficiency and distributed system performance. Task scheduling is crucial in cloud fog computing because it decides the way computer resources are divided up across tasks. Our study suggests that the Shark Search Krill Herd Optimization (SSKHOA) method be incorporated into cloud fog computing's task scheduling. To enhance both the global and local search capabilities of the optimization process, the SSKHOA algorithm combines the shark search algorithm and the krill herd algorithm. It quickly explores the solution space and finds near-optimal work schedules by modelling the swarm intelligence of krill herds and the predator-prey behavior of sharks. In order to test the efficacy of the SSKHOA algorithm, we created a synthetic cloud fog environment and performed some tests. Traditional task scheduling techniques like LTRA, DRL, and DAPSO were used to evaluate the findings. The experimental results demonstrate that the SSKHOA outperformed the baseline algorithms in terms of task success rate increased 34%, reduced the execution time by 36%, and reduced makespan time by 54% respectively.","PeriodicalId":130361,"journal":{"name":"International Journal of Information Technology and Computer Science","volume":" 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139793181","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 : 2024-02-08DOI: 10.5815/ijitcs.2024.01.05
Shakir A. Mehdiyev, Mammad A. Hashimovv
This article explores the multifaceted challenges inherent in ensuring the cybersecurity of critical infrastructures, i.e., a linchpin of modern society and the economy, spanning pivotal sectors such as energy, transportation, and finance. In the era of accelerating digitalization and escalating dependence on information technology, safeguarding these infrastructures against evolving cyber threats becomes not just crucial but imperative. The examination unfolds by dissecting the vulnerabilities that plague critical infrastructures, probing into the diverse spectrum of threats they confront in the contemporary cybersecurity landscape. Moreover, the article meticulously outlines innovative security strategies designed to fortify these vital systems against malicious intrusions. A distinctive aspect of this work is the nuanced case study presented within the oil and gas sector, strategically chosen to illustrate the vulnerability of critical infrastructures to cyber threats. By examining this sector in detail, the article aims to shed light on industry-specific challenges and potential solutions, thereby enhancing our understanding of cybersecurity dynamics within critical infrastructures. This article contributes a comprehensive analysis of the challenges faced by critical infrastructures in the face of cyber threats, offering contemporary security strategies and leveraging a focused case study to deepen insights into the nuanced vulnerabilities within the oil and gas sector.
{"title":"Analysis of Threats and Cybersecurity in the Oil and Gas Sector within the Context of Critical Infrastructure","authors":"Shakir A. Mehdiyev, Mammad A. Hashimovv","doi":"10.5815/ijitcs.2024.01.05","DOIUrl":"https://doi.org/10.5815/ijitcs.2024.01.05","url":null,"abstract":"This article explores the multifaceted challenges inherent in ensuring the cybersecurity of critical infrastructures, i.e., a linchpin of modern society and the economy, spanning pivotal sectors such as energy, transportation, and finance. In the era of accelerating digitalization and escalating dependence on information technology, safeguarding these infrastructures against evolving cyber threats becomes not just crucial but imperative. The examination unfolds by dissecting the vulnerabilities that plague critical infrastructures, probing into the diverse spectrum of threats they confront in the contemporary cybersecurity landscape. Moreover, the article meticulously outlines innovative security strategies designed to fortify these vital systems against malicious intrusions. A distinctive aspect of this work is the nuanced case study presented within the oil and gas sector, strategically chosen to illustrate the vulnerability of critical infrastructures to cyber threats. By examining this sector in detail, the article aims to shed light on industry-specific challenges and potential solutions, thereby enhancing our understanding of cybersecurity dynamics within critical infrastructures. This article contributes a comprehensive analysis of the challenges faced by critical infrastructures in the face of cyber threats, offering contemporary security strategies and leveraging a focused case study to deepen insights into the nuanced vulnerabilities within the oil and gas sector.","PeriodicalId":130361,"journal":{"name":"International Journal of Information Technology and Computer Science","volume":"14 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139854293","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 : 2024-02-08DOI: 10.5815/ijitcs.2024.01.01
M. S. Kumar, K. G. Reddy, Rakesh Kumar Donthi
Cloud fog computing is a new paradigm that combines cloud computing and fog computing to boost resource efficiency and distributed system performance. Task scheduling is crucial in cloud fog computing because it decides the way computer resources are divided up across tasks. Our study suggests that the Shark Search Krill Herd Optimization (SSKHOA) method be incorporated into cloud fog computing's task scheduling. To enhance both the global and local search capabilities of the optimization process, the SSKHOA algorithm combines the shark search algorithm and the krill herd algorithm. It quickly explores the solution space and finds near-optimal work schedules by modelling the swarm intelligence of krill herds and the predator-prey behavior of sharks. In order to test the efficacy of the SSKHOA algorithm, we created a synthetic cloud fog environment and performed some tests. Traditional task scheduling techniques like LTRA, DRL, and DAPSO were used to evaluate the findings. The experimental results demonstrate that the SSKHOA outperformed the baseline algorithms in terms of task success rate increased 34%, reduced the execution time by 36%, and reduced makespan time by 54% respectively.
{"title":"SSKHOA: Hybrid Metaheuristic Algorithm for Resource Aware Task Scheduling in Cloud-fog Computing","authors":"M. S. Kumar, K. G. Reddy, Rakesh Kumar Donthi","doi":"10.5815/ijitcs.2024.01.01","DOIUrl":"https://doi.org/10.5815/ijitcs.2024.01.01","url":null,"abstract":"Cloud fog computing is a new paradigm that combines cloud computing and fog computing to boost resource efficiency and distributed system performance. Task scheduling is crucial in cloud fog computing because it decides the way computer resources are divided up across tasks. Our study suggests that the Shark Search Krill Herd Optimization (SSKHOA) method be incorporated into cloud fog computing's task scheduling. To enhance both the global and local search capabilities of the optimization process, the SSKHOA algorithm combines the shark search algorithm and the krill herd algorithm. It quickly explores the solution space and finds near-optimal work schedules by modelling the swarm intelligence of krill herds and the predator-prey behavior of sharks. In order to test the efficacy of the SSKHOA algorithm, we created a synthetic cloud fog environment and performed some tests. Traditional task scheduling techniques like LTRA, DRL, and DAPSO were used to evaluate the findings. The experimental results demonstrate that the SSKHOA outperformed the baseline algorithms in terms of task success rate increased 34%, reduced the execution time by 36%, and reduced makespan time by 54% respectively.","PeriodicalId":130361,"journal":{"name":"International Journal of Information Technology and Computer Science","volume":"99 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139853093","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 : 2024-02-08DOI: 10.5815/ijitcs.2024.01.03
M. H. Rahman, M. Naderuzzaman, M. A. Kashem, B. M. Salahuddin, Z. Mahmud
The regular utilization of web-based applications is crucial in our everyday life. The Model View Controller (MVC) architecture serves as a structured programming design that developers utilize to create user interfaces. This pattern is commonly applied by application software developers to construct web-based applications. The use of a MVC framework of PHP Scripting language is often essential for application software development. There is a significant argument regarding the most suitable PHP MVC such as Codeigniter & Laravel and Phalcon frameworks since not all frameworks cater to everyone's needs. It's a fact that not all MVC frameworks are created equal and different frameworks can be combined for specific scenarios. Selecting the appropriate MVC framework can pose a challenge at times. In this context, our paper focuses on conducting a comparative analysis of different PHP frameworks. The widely used PHP MVC frameworks are picked to compare the performance on basic Operation of Relational databases and different type of Application software to calculate execution time. In this experiment a large (Big Data) dataset was used. The Mean values of insert operation in MySQL database of Codeigniter, Laravel, Phalcon were 149.64, 149.99, 145.48 and PostgreSQL database`s 48.259, 49.39, 45.87 respectively. The Mean values of Update operation in MySQL database of Codeigniter, Laravel, Phalcon were 149.64, 158.39, 207.82 and PostgreSQL database`s 48.24, 49.39, 46.64 respectively. The Mean values of Select operation in MySQL database of Codeigniter, Laravel, Phalcon were 1.60, 3.23, 0.98 and PostgreSQL database`s 1.95, 4.57, 2.36 respectively. The Mean values of Delete operation in MySQL database of Codeigniter, Laravel, Phalcon were 150.27, 156.99, 149.63 and PostgreSQL database`s 42.95, 48.25, 42.07 respectively. The findings from our experiment can be advantageous for web application developers to choose proper MVC frameworks with their integrated development environment (IDE). This result will be helpful for small, medium & large-scale organization in choosing the appropriate PHP Framework.
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