Securing privacy in machine learning via collaborative data sharing is essential for organizations seeking to harness collective data while upholding confidentiality. This becomes especially vital when protecting sensitive information across the entire machine learning pipeline, from model training to inference. This paper presents an innovative framework utilizing Representation Learning via autoencoders to generate privacy-preserving embedded data. As a result, organizations can distribute these representations, enhancing the performance of machine learning models in situations where multiple data sources converge for a unified predictive task downstream.
{"title":"Fostering Privacy in Collaborative Data Sharing via Auto-encoder Latent Space Embedding","authors":"Vinayak Raja, Bhuvi Chopra","doi":"10.60087/jaigs.v4i1.129","DOIUrl":"https://doi.org/10.60087/jaigs.v4i1.129","url":null,"abstract":"Securing privacy in machine learning via collaborative data sharing is essential for organizations seeking to harness collective data while upholding confidentiality. This becomes especially vital when protecting sensitive information across the entire machine learning pipeline, from model training to inference. This paper presents an innovative framework utilizing Representation Learning via autoencoders to generate privacy-preserving embedded data. As a result, organizations can distribute these representations, enhancing the performance of machine learning models in situations where multiple data sources converge for a unified predictive task downstream.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"106 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140985846","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 : 2024-05-05DOI: 10.60087/jaigs.vol4.issue1.p120
Venkata dinesh Reddy kalli
The surge in artificial intelligence (AI) applications across diverse fields owes much to advancements in deep learning and computational processing speed. In medicine, AI's reach extends to medical image analysis and genomic data interpretation. More recently, AI's role in analyzing minimally invasive surgery (MIS) videos has gained traction, with a growing body of research focusing on organ and anatomy identification, instrument recognition, procedure recognition, surgical phase delineation, surgery duration prediction, optimal incision line identification, and surgical education. Concurrently, the development of autonomous surgical robots, exemplified by the Smart Tissue Autonomous Robot (STAR) and RAVEN systems, has shown promising strides. Notably, STAR is currently employed in laparoscopic imaging to discern the surgical site from laparoscopic images and is undergoing trials for an automated suturing system, albeit in animal models. This review contemplates the prospect of fully autonomous surgical robots in the future.
{"title":"Advancements in Deep Learning for Minimally Invasive Surgery: A Journey through Surgical System Evolution","authors":"Venkata dinesh Reddy kalli","doi":"10.60087/jaigs.vol4.issue1.p120","DOIUrl":"https://doi.org/10.60087/jaigs.vol4.issue1.p120","url":null,"abstract":"The surge in artificial intelligence (AI) applications across diverse fields owes much to advancements in deep learning and computational processing speed. In medicine, AI's reach extends to medical image analysis and genomic data interpretation. More recently, AI's role in analyzing minimally invasive surgery (MIS) videos has gained traction, with a growing body of research focusing on organ and anatomy identification, instrument recognition, procedure recognition, surgical phase delineation, surgery duration prediction, optimal incision line identification, and surgical education. Concurrently, the development of autonomous surgical robots, exemplified by the Smart Tissue Autonomous Robot (STAR) and RAVEN systems, has shown promising strides. Notably, STAR is currently employed in laparoscopic imaging to discern the surgical site from laparoscopic images and is undergoing trials for an automated suturing system, albeit in animal models. This review contemplates the prospect of fully autonomous surgical robots in the future.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"286 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141012332","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}
Abstract — As organizations increasingly adopt microservices architectures for building scalable and resilient applications, ensuring the security of these distributed systems becomes paramount. In this empirical study, we conduct a comprehensive comparative analysis to assess the efficacy of three leading security scanning tools, namely Veracode, Snyk, and Checkmarx, in identifying and remedying security vulnerabilities within microservices applications deployed on the AWS and Azure cloud platforms. The study aims to provide nuanced insights into the performance, usability, and integration capabilities of these tools, offering valuable guidance to organizations striving to fortify their microservices-based infrastructures. By meticulously evaluating scanning capabilities, vulnerability detection accuracy, remediation guidance comprehensiveness, CI/CD pipeline integration proficiency, and overall ease of use, our research sheds light on the relative strengths and weaknesses of each tool in the context of modern cloud-native application security. Through meticulously designed experiments utilizing realistic microservices application scenarios encompassing diverse vulnerability types, including injection attacks, authentication bypasses, and insecure configurations, we present a thorough examination of the tools' capabilities and limitations. The findings from our study contribute to the evolving discourse on microservices security, emphasizing the critical importance of selecting appropriate security scanning solutions tailored to the unique requirements and constraints of cloud-based microservices architectures. By leveraging the insights gleaned from our comparative analysis, organizations can make well-informed decisions regarding tool selection and deployment strategies, thereby bolstering the resilience of their microservices ecosystems against an ever-expanding threat landscape.
{"title":"Microservices Security Vulnerability Remediation approach using Veracode and Checkmarx","authors":"Amarjeet Singh","doi":"10.60087/jaigs.v4i1.128","DOIUrl":"https://doi.org/10.60087/jaigs.v4i1.128","url":null,"abstract":"Abstract — As organizations increasingly adopt microservices architectures for building scalable and resilient applications, ensuring the security of these distributed systems becomes paramount. In this empirical study, we conduct a comprehensive comparative analysis to assess the efficacy of three leading security scanning tools, namely Veracode, Snyk, and Checkmarx, in identifying and remedying security vulnerabilities within microservices applications deployed on the AWS and Azure cloud platforms. \u0000The study aims to provide nuanced insights into the performance, usability, and integration capabilities of these tools, offering valuable guidance to organizations striving to fortify their microservices-based infrastructures. By meticulously evaluating scanning capabilities, vulnerability detection accuracy, remediation guidance comprehensiveness, CI/CD pipeline integration proficiency, and overall ease of use, our research sheds light on the relative strengths and weaknesses of each tool in the context of modern cloud-native application security. Through meticulously designed experiments utilizing realistic microservices application scenarios encompassing diverse vulnerability types, including injection attacks, authentication bypasses, and insecure configurations, we present a thorough examination of the tools' capabilities and limitations. The findings from our study contribute to the evolving discourse on microservices security, emphasizing the critical importance of selecting appropriate security scanning solutions tailored to the unique requirements and constraints of cloud-based microservices architectures. By leveraging the insights gleaned from our comparative analysis, organizations can make well-informed decisions regarding tool selection and deployment strategies, thereby bolstering the resilience of their microservices ecosystems against an ever-expanding threat landscape.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"241 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141012759","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 : 2024-05-03DOI: 10.60087/jaigs.vol4.issue1.p110
Sohana Akter
Educational international organizations have forecasted a radical transformation in how students learn, the content they learn, and the requisite skills in the near future. The advent of smart technologies is poised to revolutionize learning conditions, ushering in opportunities for transformative learning experiences and fostering more conscious, self-directed, and self-motivated learning endeavors. Meta-learning encompasses a suite of cognitive meta-processes through which learners consciously construct and manage personal learning models. It involves a progression of meta-skills that evolve hierarchically, facilitating the attainment of advanced levels of comprehension termed meta-comprehension. This article delves into the concept of meta-learning and delineates the meta-levels of learning through the lens of metacognition. Additionally, it explores the potential of smart technologies to serve as fertile ground for implementing meta-learning training strategies. The findings of this study contribute to a novel theoretical framework for meta-learning, bolstered by smart devices capable of supporting future meta-learners, or more aptly, meta-thinkers, in transcending conventional realms of knowledge and ascending to higher meta-levels of human intelligence.
{"title":"Exploring Meta-Learning: Unveiling Progress and Obstacles - A Comprehensive Examination","authors":"Sohana Akter","doi":"10.60087/jaigs.vol4.issue1.p110","DOIUrl":"https://doi.org/10.60087/jaigs.vol4.issue1.p110","url":null,"abstract":"Educational international organizations have forecasted a radical transformation in how students learn, the content they learn, and the requisite skills in the near future. The advent of smart technologies is poised to revolutionize learning conditions, ushering in opportunities for transformative learning experiences and fostering more conscious, self-directed, and self-motivated learning endeavors. Meta-learning encompasses a suite of cognitive meta-processes through which learners consciously construct and manage personal learning models. It involves a progression of meta-skills that evolve hierarchically, facilitating the attainment of advanced levels of comprehension termed meta-comprehension. This article delves into the concept of meta-learning and delineates the meta-levels of learning through the lens of metacognition. Additionally, it explores the potential of smart technologies to serve as fertile ground for implementing meta-learning training strategies. The findings of this study contribute to a novel theoretical framework for meta-learning, bolstered by smart devices capable of supporting future meta-learners, or more aptly, meta-thinkers, in transcending conventional realms of knowledge and ascending to higher meta-levels of human intelligence.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"14 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141017126","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 : 2024-04-23DOI: 10.60087/jaigs.vol4.issue1.p12
Md.mafiqul Islam
This paper explores the utilization of cognitive modeling to address the influence of antiblackness and racism on the design and development of AI systems. Through the lens of the ACT-R/Φ cognitive architecture and ConceptNet, an existing knowledge graph system, we investigate this issue from cognitive, sociocultural, and physiological perspectives. We propose an approach that not only examines how antiblackness may permeate AI system design and development, particularly within the realm of software engineering, but also establishes links between antiblackness, human cognition, and computational cognitive modeling. We contend that overlooking sociocultural factors in cognitive architectures perpetuates a colorblind approach to modeling, obscuring the inherent sociocultural context that shapes human behavior and cognitive processes.
{"title":"Cognitive Frameworks for Mitigating Antiblack Bias: Advancing Ethical AI Design and Development","authors":"Md.mafiqul Islam","doi":"10.60087/jaigs.vol4.issue1.p12","DOIUrl":"https://doi.org/10.60087/jaigs.vol4.issue1.p12","url":null,"abstract":"This paper explores the utilization of cognitive modeling to address the influence of antiblackness and racism on the design and development of AI systems. Through the lens of the ACT-R/Φ cognitive architecture and ConceptNet, an existing knowledge graph system, we investigate this issue from cognitive, sociocultural, and physiological perspectives. We propose an approach that not only examines how antiblackness may permeate AI system design and development, particularly within the realm of software engineering, but also establishes links between antiblackness, human cognition, and computational cognitive modeling. We contend that overlooking sociocultural factors in cognitive architectures perpetuates a colorblind approach to modeling, obscuring the inherent sociocultural context that shapes human behavior and cognitive processes.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"135 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140668623","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 : 2024-04-23DOI: 10.60087/jaigs.vol4.issue1.p44
Amizur Nachshoni
This research investigates the influence of school principals' motivation on teachers, recognizing motivation as a complex process driving human behavior towards goals. Motivation's significance lies in its role in energizing individuals towards their aspirations. The study highlights two key motivations: the critical role of motivated teachers in education and the principal's leadership impact on teacher motivation. Literature underscores motivation's multifaceted nature and its link to organizational climate, rewards, and management practices. Challenges include establishing causality between principal actions and teacher motivation amid diverse educational contexts. Despite hurdles, insights gleaned shed light on the principal's influence and teacher motivation levels.
{"title":"The Impact of Principal on Teacher Motivation in Secondary Schools","authors":"Amizur Nachshoni","doi":"10.60087/jaigs.vol4.issue1.p44","DOIUrl":"https://doi.org/10.60087/jaigs.vol4.issue1.p44","url":null,"abstract":"This research investigates the influence of school principals' motivation on teachers, recognizing motivation as a complex process driving human behavior towards goals. Motivation's significance lies in its role in energizing individuals towards their aspirations. The study highlights two key motivations: the critical role of motivated teachers in education and the principal's leadership impact on teacher motivation. Literature underscores motivation's multifaceted nature and its link to organizational climate, rewards, and management practices. Challenges include establishing causality between principal actions and teacher motivation amid diverse educational contexts. Despite hurdles, insights gleaned shed light on the principal's influence and teacher motivation levels.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"140 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140668558","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 : 2024-04-23DOI: 10.60087/jaigs.vol4.issue1.p56
José Gabriel Carrasco Ramírez
Deep learning methodologies have significantly advanced the fields of computer vision and machine learning, enhancing performance across various tasks like classification, regression, and detection. In remote sensing for Earth observation, deep neural networks have propelled state-of-the-art results. However, a major drawback is their dependence on large annotated datasets, necessitating extensive human effort, especially in specialized domains like medical imaging or remote sensing. To mitigate this reliance on annotations, several self-supervised representation learning techniques have emerged, aiming to learn unsupervised image representations applicable to downstream tasks such as image classification, object detection, or semantic segmentation. Consequently, self-supervised learning approaches have gained traction in remote sensing. This article surveys the foundational principles of various self-supervised methods, focusing on scene classification tasks. We elucidate key contributions, analyze experimental setups, and synthesize findings from each study. Furthermore, we conduct comprehensive experiments on two public scene classification datasets to evaluate and benchmark different self-supervised models
{"title":"Advancements in Self-Supervised Learning for Remote Sensing Scene Classification: Present Innovations and Future Outlooks","authors":"José Gabriel Carrasco Ramírez","doi":"10.60087/jaigs.vol4.issue1.p56","DOIUrl":"https://doi.org/10.60087/jaigs.vol4.issue1.p56","url":null,"abstract":"Deep learning methodologies have significantly advanced the fields of computer vision and machine learning, enhancing performance across various tasks like classification, regression, and detection. In remote sensing for Earth observation, deep neural networks have propelled state-of-the-art results. However, a major drawback is their dependence on large annotated datasets, necessitating extensive human effort, especially in specialized domains like medical imaging or remote sensing. To mitigate this reliance on annotations, several self-supervised representation learning techniques have emerged, aiming to learn unsupervised image representations applicable to downstream tasks such as image classification, object detection, or semantic segmentation. Consequently, self-supervised learning approaches have gained traction in remote sensing. This article surveys the foundational principles of various self-supervised methods, focusing on scene classification tasks. We elucidate key contributions, analyze experimental setups, and synthesize findings from each study. Furthermore, we conduct comprehensive experiments on two public scene classification datasets to evaluate and benchmark different self-supervised models","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"75 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140667604","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 : 2024-04-23DOI: 10.60087/jaigs.vol4.issue1.p26
José Gabriel Carrasco Ramírez
This paper explores the feasibility of constructing interpretable artificial intelligence (AI) systems rooted in active inference and the free energy principle. Initially, we offer a concise introduction to active inference, emphasizing its relevance to modeling decision-making, introspection, and the generation of both overt and covert actions. Subsequently, we delve into how active inference can serve as a foundation for designing explainable AI systems. Specifically, it enables us to capture essential aspects of "introspective" processes and generate intelligible models of decision-making mechanisms. We propose an architectural framework for explainable AI systems employing active inference. Central to this framework is an explicit hierarchical generative model that enables the AI system to monitor and elucidate the factors influencing its decisions. Importantly, this model's structure is designed to be understandable and verifiable by human users. We elucidate how this architecture can amalgamate diverse data sources to make informed decisions in a transparent manner, mirroring aspects of human consciousness and introspection. Finally, we examine the implications of our findings for future AI research and discuss potential ethical considerations associated with developing AI systems with (apparent) introspective capabilities.
{"title":"Crafting explainable artificial intelligence through active inference: A model for transparent introspection and decision-making","authors":"José Gabriel Carrasco Ramírez","doi":"10.60087/jaigs.vol4.issue1.p26","DOIUrl":"https://doi.org/10.60087/jaigs.vol4.issue1.p26","url":null,"abstract":"This paper explores the feasibility of constructing interpretable artificial intelligence (AI) systems rooted in active inference and the free energy principle. Initially, we offer a concise introduction to active inference, emphasizing its relevance to modeling decision-making, introspection, and the generation of both overt and covert actions. Subsequently, we delve into how active inference can serve as a foundation for designing explainable AI systems. Specifically, it enables us to capture essential aspects of \"introspective\" processes and generate intelligible models of decision-making mechanisms. We propose an architectural framework for explainable AI systems employing active inference. Central to this framework is an explicit hierarchical generative model that enables the AI system to monitor and elucidate the factors influencing its decisions. Importantly, this model's structure is designed to be understandable and verifiable by human users. We elucidate how this architecture can amalgamate diverse data sources to make informed decisions in a transparent manner, mirroring aspects of human consciousness and introspection. Finally, we examine the implications of our findings for future AI research and discuss potential ethical considerations associated with developing AI systems with (apparent) introspective capabilities.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"62 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140666767","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 : 2024-04-19DOI: 10.60087/jaigs.vol03.issue01.p255
Md.mafiqul Islam
This research delves into the intricate influence of Artificial Intelligence (AI) on community development across vital sectors such as healthcare, education, environmental sustainability, and community empowerment. Its core aim is to comprehensively analyze how individuals in underserved communities perceive and experience the use of AI technologies. To achieve this, a mixed-methods approach is adopted, combining quantitative surveys for statistical insights with qualitative narratives for nuanced perspectives. Engaging 120 participants from diverse backgrounds and age groups, the research methodology incorporates Likert scales and regression analysis for data interpretation. The study reveals a prevalent positive outlook on AI's impact across various domains, particularly highlighting its significant effects on healthcare, education, and environmental sustainability. Integration of qualitative narratives enriches the findings, offering depth and context to statistical analyses. Its novelty lies in the comprehensive examination of AI's influence on community development, seamlessly blending quantitative and qualitative dimensions. By providing nuanced insights into AI's multifaceted role in community contexts, the research significantly contributes to the field. Ultimately, the study underscores the importance of responsible AI deployment, aligned with community values, to navigate the evolving technological landscape and foster sustainable community development.
{"title":"Utilizing AI for Social Good: Tackling Global Issues and Fostering Inclusive Solutions","authors":"Md.mafiqul Islam","doi":"10.60087/jaigs.vol03.issue01.p255","DOIUrl":"https://doi.org/10.60087/jaigs.vol03.issue01.p255","url":null,"abstract":"This research delves into the intricate influence of Artificial Intelligence (AI) on community development across vital sectors such as healthcare, education, environmental sustainability, and community empowerment. Its core aim is to comprehensively analyze how individuals in underserved communities perceive and experience the use of AI technologies. To achieve this, a mixed-methods approach is adopted, combining quantitative surveys for statistical insights with qualitative narratives for nuanced perspectives. Engaging 120 participants from diverse backgrounds and age groups, the research methodology incorporates Likert scales and regression analysis for data interpretation. The study reveals a prevalent positive outlook on AI's impact across various domains, particularly highlighting its significant effects on healthcare, education, and environmental sustainability. Integration of qualitative narratives enriches the findings, offering depth and context to statistical analyses. Its novelty lies in the comprehensive examination of AI's influence on community development, seamlessly blending quantitative and qualitative dimensions. By providing nuanced insights into AI's multifaceted role in community contexts, the research significantly contributes to the field. Ultimately, the study underscores the importance of responsible AI deployment, aligned with community values, to navigate the evolving technological landscape and foster sustainable community development.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":" 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140685324","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 : 2024-04-12DOI: 10.60087/jaigs.vol03.issue01.p233
Fnu Jimmy
The proliferation of internet usage has surged dramatically, prompting individuals and businesses to conduct myriad transactions online rather than in physical spaces. The onset of the COVID-19 pandemic has further propelled this trend. Consequently, traditional forms of crime have migrated to the digital realm alongside the widespread adoption of digital technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and crypto currencies, amplifying security concerns in cyberspace. Notably, cybercriminals have begun offering cyber attacks as a service, automating attacks to magnify their impact. These attackers exploit vulnerabilities across hardware, software, and communication layers, perpetrating various forms of cyber attacks including distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware attacks. The sophistication of these attacks renders conventional protection systems, such as firewalls, intrusion detection systems, antivirus software, and access control lists, ineffective in detection. Consequently, there is an urgent imperative to devise innovative and pragmatic solutions to thwart cyber attacks. This paper elucidates the primary drivers behind cyber attacks, surveys recent attack instances, patterns, and detection methodologies, and explores contemporary technical and non-technical strategies for preemptively identifying and mitigating attacks. Leveraging cutting-edge technologies like machine learning, deep learning, cloud platforms, big data analytics, and blockchain holds promise in combating present and future cyber threats. These technological interventions can aid in malware detection, intrusion detection, spam filtering, DNS attack classification, fraud detection, identification of covert channels, and discernment of advanced persistent threats. Nonetheless, it's crucial to acknowledge that some promising solutions, notably machine learning and deep learning, are susceptible to evasion techniques, necessitating careful consideration when formulating defenses against sophisticated cyber attacks.
{"title":"Cyber security Vulnerabilities and Remediation Through Cloud Security Tools","authors":"Fnu Jimmy","doi":"10.60087/jaigs.vol03.issue01.p233","DOIUrl":"https://doi.org/10.60087/jaigs.vol03.issue01.p233","url":null,"abstract":"The proliferation of internet usage has surged dramatically, prompting individuals and businesses to conduct myriad transactions online rather than in physical spaces. The onset of the COVID-19 pandemic has further propelled this trend. Consequently, traditional forms of crime have migrated to the digital realm alongside the widespread adoption of digital technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and crypto currencies, amplifying security concerns in cyberspace. Notably, cybercriminals have begun offering cyber attacks as a service, automating attacks to magnify their impact. These attackers exploit vulnerabilities across hardware, software, and communication layers, perpetrating various forms of cyber attacks including distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware attacks. The sophistication of these attacks renders conventional protection systems, such as firewalls, intrusion detection systems, antivirus software, and access control lists, ineffective in detection. Consequently, there is an urgent imperative to devise innovative and pragmatic solutions to thwart cyber attacks. This paper elucidates the primary drivers behind cyber attacks, surveys recent attack instances, patterns, and detection methodologies, and explores contemporary technical and non-technical strategies for preemptively identifying and mitigating attacks. Leveraging cutting-edge technologies like machine learning, deep learning, cloud platforms, big data analytics, and blockchain holds promise in combating present and future cyber threats. These technological interventions can aid in malware detection, intrusion detection, spam filtering, DNS attack classification, fraud detection, identification of covert channels, and discernment of advanced persistent threats. Nonetheless, it's crucial to acknowledge that some promising solutions, notably machine learning and deep learning, are susceptible to evasion techniques, necessitating careful consideration when formulating defenses against sophisticated cyber attacks.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"17 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140711738","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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