{"title":"Bibliometric Analysis of Electric Vehicle Adoption Research: Trends, Implications, and Future Directions","authors":"Edi Purwanto, Agustinus Purna Irawan","doi":"10.18280/ijsse.130503","DOIUrl":"https://doi.org/10.18280/ijsse.130503","url":null,"abstract":"ABSTRACT","PeriodicalId":37802,"journal":{"name":"International Journal of Safety and Security Engineering","volume":"114 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135137261","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}
Hery Leo Sianturi, Adi Susilo, Juliany N. Mohamad, Redi K. Pingak
ABSTRACT
{"title":"Analysis of Landslide Using Resistivity Method in the Avalanche Area of Tolnaku, Kupang Regency, East Nusa Tenggara, Indonesia","authors":"Hery Leo Sianturi, Adi Susilo, Juliany N. Mohamad, Redi K. Pingak","doi":"10.18280/ijsse.130516","DOIUrl":"https://doi.org/10.18280/ijsse.130516","url":null,"abstract":"ABSTRACT","PeriodicalId":37802,"journal":{"name":"International Journal of Safety and Security Engineering","volume":"114 44","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135136227","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}
{"title":"Enhancing IoT Security with Trust-Based Mechanism for Mitigating Black Hole Attacks","authors":"Mahalakshmi Govindaraj, Suresh Arumugam","doi":"10.18280/ijsse.130515","DOIUrl":"https://doi.org/10.18280/ijsse.130515","url":null,"abstract":"ABSTRACT","PeriodicalId":37802,"journal":{"name":"International Journal of Safety and Security Engineering","volume":"112 45","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135136924","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}
Marina Vladimirovna Grafkina, Evgeniya Yurevna Sviridova, Elena Viktorovna Goryacheva
ABSTRACT
{"title":"Reducing Occupational Risks in Industrial Processes: Analysis and Recommendations for Improving Safety in Production Equipment and Facilities","authors":"Marina Vladimirovna Grafkina, Evgeniya Yurevna Sviridova, Elena Viktorovna Goryacheva","doi":"10.18280/ijsse.130502","DOIUrl":"https://doi.org/10.18280/ijsse.130502","url":null,"abstract":"ABSTRACT","PeriodicalId":37802,"journal":{"name":"International Journal of Safety and Security Engineering","volume":"114 33","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135137239","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}
Joanie B. Houinsou, Kokou M. Assogba, Roland C. Houessouvo
ABSTRACT
{"title":"Identification of Human Survivors in Natural Disasters Through Body Odor Analysis","authors":"Joanie B. Houinsou, Kokou M. Assogba, Roland C. Houessouvo","doi":"10.18280/ijsse.130508","DOIUrl":"https://doi.org/10.18280/ijsse.130508","url":null,"abstract":"ABSTRACT","PeriodicalId":37802,"journal":{"name":"International Journal of Safety and Security Engineering","volume":"92 25","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135092065","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}
Erni Suharini, None Supriyadi, Mohammad Syifauddin, Ervando Tommy Al-Hanif, Edi Kurniawan, Satya Budi Nugraha
ABSTRACT
{"title":"An Evaluation of Community Adoption of the InaRISK BNPB Platform for Disaster Management: An Application of the Technology Acceptance Model (TAM)","authors":"Erni Suharini, None Supriyadi, Mohammad Syifauddin, Ervando Tommy Al-Hanif, Edi Kurniawan, Satya Budi Nugraha","doi":"10.18280/ijsse.130409","DOIUrl":"https://doi.org/10.18280/ijsse.130409","url":null,"abstract":"ABSTRACT","PeriodicalId":37802,"journal":{"name":"International Journal of Safety and Security Engineering","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135386541","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}
{"title":"Capitalizing on Blockchain Technology for Efficient Crowdfunding: An Exploration of Ethereum's Smart Contracts","authors":"Cynthia Jayapal, Arputha Rathina Xavier, Poonguzhali Arunachalam","doi":"10.18280/ijsse.130415","DOIUrl":"https://doi.org/10.18280/ijsse.130415","url":null,"abstract":"ABSTRACT","PeriodicalId":37802,"journal":{"name":"International Journal of Safety and Security Engineering","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135386730","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}
{"title":"Implementing A Seismic Sensor-Driven Disaster Management System for Efficient Tsunami Early Warnings in the Arabian Peninsula","authors":"Minu Theresa Mathew, Zeyad Ismail","doi":"10.18280/ijsse.130416","DOIUrl":"https://doi.org/10.18280/ijsse.130416","url":null,"abstract":"ABSTRACT","PeriodicalId":37802,"journal":{"name":"International Journal of Safety and Security Engineering","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135387686","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}
Olisaemeka F. Isife, Kennedy Okokpujie, Imhade P. Okokpujie, Roselyn E. Subair, Akingunsoye Adenugba Vincent, Morayo E. Awomoyi
With the exponential surge in the number of internet-connected devices, the attack surface for potential cyber threats has correspondingly expanded. Such a landscape necessitates the evolution of intrusion detection systems to counter the increasingly sophisticated mechanisms employed by cyber attackers. Traditional machine learning methods, coupled with existing deep learning implementations, are observed to exhibit limited proficiency due to their reliance on outdated datasets. Their performance is further compromised by elevated false positive rates, decreased detection rates, and an inability to efficiently detect novel attacks. In an attempt to address these challenges, this study proposes a deep learning-based system specifically designed for the detection of malicious network traffic. Three distinct deep learning models were employed: Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). These models were trained using two contemporary benchmark intrusion detection datasets: the CICIDS 2017 and the Coburg Intrusion Detection Data Sets (CIDDS). A robust preprocessing procedure was conducted to merge these datasets based on common and essential features, creating a comprehensive dataset for model training. Two separate experimental setups were utilized to configure these models. Among the three models, the LSTM displayed superior performance in both experimental configurations. It achieved an accuracy of 98.09%, a precision of 98.14%, an F1-Score of 98.09%, a True Positive Rate (TPR) of 98.05%, a True Negative Rate (TNR) of 99.69%, a False Positive Rate (FPR) of 0.31%, and a False Negative Rate (FNR) of 1.95%.
{"title":"Development of a Malicious Network Traffic Intrusion Detection System Using Deep Learning","authors":"Olisaemeka F. Isife, Kennedy Okokpujie, Imhade P. Okokpujie, Roselyn E. Subair, Akingunsoye Adenugba Vincent, Morayo E. Awomoyi","doi":"10.18280/ijsse.130401","DOIUrl":"https://doi.org/10.18280/ijsse.130401","url":null,"abstract":"With the exponential surge in the number of internet-connected devices, the attack surface for potential cyber threats has correspondingly expanded. Such a landscape necessitates the evolution of intrusion detection systems to counter the increasingly sophisticated mechanisms employed by cyber attackers. Traditional machine learning methods, coupled with existing deep learning implementations, are observed to exhibit limited proficiency due to their reliance on outdated datasets. Their performance is further compromised by elevated false positive rates, decreased detection rates, and an inability to efficiently detect novel attacks. In an attempt to address these challenges, this study proposes a deep learning-based system specifically designed for the detection of malicious network traffic. Three distinct deep learning models were employed: Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). These models were trained using two contemporary benchmark intrusion detection datasets: the CICIDS 2017 and the Coburg Intrusion Detection Data Sets (CIDDS). A robust preprocessing procedure was conducted to merge these datasets based on common and essential features, creating a comprehensive dataset for model training. Two separate experimental setups were utilized to configure these models. Among the three models, the LSTM displayed superior performance in both experimental configurations. It achieved an accuracy of 98.09%, a precision of 98.14%, an F1-Score of 98.09%, a True Positive Rate (TPR) of 98.05%, a True Negative Rate (TNR) of 99.69%, a False Positive Rate (FPR) of 0.31%, and a False Negative Rate (FNR) of 1.95%.","PeriodicalId":37802,"journal":{"name":"International Journal of Safety and Security Engineering","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135420780","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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