Pub Date : 2022-09-28DOI: 10.1080/19361610.2022.2125253
Frank Musmar
{"title":"Communication in Global Jihad","authors":"Frank Musmar","doi":"10.1080/19361610.2022.2125253","DOIUrl":"https://doi.org/10.1080/19361610.2022.2125253","url":null,"abstract":"","PeriodicalId":44585,"journal":{"name":"Journal of Applied Security Research","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45382865","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 : 2022-09-22DOI: 10.1080/19361610.2022.2124328
Jamil R. Alzghoul, E. Abdallah, Abdel-hafiz S. Al-khawaldeh
{"title":"Fraud in Online Classified Ads: Strategies, Risks, and Detection Methods: A Survey","authors":"Jamil R. Alzghoul, E. Abdallah, Abdel-hafiz S. Al-khawaldeh","doi":"10.1080/19361610.2022.2124328","DOIUrl":"https://doi.org/10.1080/19361610.2022.2124328","url":null,"abstract":"","PeriodicalId":44585,"journal":{"name":"Journal of Applied Security Research","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45173914","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 : 2022-09-12DOI: 10.1080/19361610.2022.2113731
Enni Soerjati Priowirjanto, Eman Suparman, M. Amirulloh, Ema Rahmawati
{"title":"QR Codes to Prevent Copyright Infringement: Case Study of Trusmi Batik in Cirebon, Indonesia","authors":"Enni Soerjati Priowirjanto, Eman Suparman, M. Amirulloh, Ema Rahmawati","doi":"10.1080/19361610.2022.2113731","DOIUrl":"https://doi.org/10.1080/19361610.2022.2113731","url":null,"abstract":"","PeriodicalId":44585,"journal":{"name":"Journal of Applied Security Research","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48139478","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 : 2022-09-01DOI: 10.1080/19361610.2022.2116921
Jackie McNett, Josh McNett, Xiaoli Su
{"title":"IoT Security in Industry: A Threat Model of Existing and Future Network Infrastructure","authors":"Jackie McNett, Josh McNett, Xiaoli Su","doi":"10.1080/19361610.2022.2116921","DOIUrl":"https://doi.org/10.1080/19361610.2022.2116921","url":null,"abstract":"","PeriodicalId":44585,"journal":{"name":"Journal of Applied Security Research","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44296469","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 : 2022-08-29DOI: 10.1080/19361610.2022.2099707
N. Duraimutharasan
Abstract Hospital medical records with health examination findings can be integrated to assist in uncovering the link between aberrant test results and illness. It is possible to establish a disease-preventive knowledge center using these integrated data by performing associated rule mining on the results. In order to integrate data, sensitive patient information must be shared. Patients’ privacy may be violated by the disclosure of sensitive information. Thus, privacy-preserving associated rule mining in physically partitioned healthcare data is addressed in this article. The suggested technique is further evaluated in terms of data protection, transmission, and computing costs.
{"title":"Privacy Enhanced Healthcare Data Management Using Associative Data Mining Approaches","authors":"N. Duraimutharasan","doi":"10.1080/19361610.2022.2099707","DOIUrl":"https://doi.org/10.1080/19361610.2022.2099707","url":null,"abstract":"Abstract Hospital medical records with health examination findings can be integrated to assist in uncovering the link between aberrant test results and illness. It is possible to establish a disease-preventive knowledge center using these integrated data by performing associated rule mining on the results. In order to integrate data, sensitive patient information must be shared. Patients’ privacy may be violated by the disclosure of sensitive information. Thus, privacy-preserving associated rule mining in physically partitioned healthcare data is addressed in this article. The suggested technique is further evaluated in terms of data protection, transmission, and computing costs.","PeriodicalId":44585,"journal":{"name":"Journal of Applied Security Research","volume":"44 3","pages":"827 - 844"},"PeriodicalIF":1.3,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41285589","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 : 2022-08-26DOI: 10.1080/19361610.2022.2114744
M. Lokanan
{"title":"Predicting Money Laundering Using Machine Learning and Artificial Neural Networks Algorithms in Banks","authors":"M. Lokanan","doi":"10.1080/19361610.2022.2114744","DOIUrl":"https://doi.org/10.1080/19361610.2022.2114744","url":null,"abstract":"","PeriodicalId":44585,"journal":{"name":"Journal of Applied Security Research","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48572072","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 : 2022-08-24DOI: 10.1080/19361610.2022.2110637
Hemalatha Jeyaprakash, Balachander Chokkalingam, Vivek V, S. Mohan
Abstract Steganography is the concept of embedding or hiding secret information into a cover image by maintaining the visual quality. Various algorithms are designed to classify stego images but the race still continues between Steganographer and Steganalyser. Advances in deep learning provided a solution to detect stego images. In this article, we coin a new paradigm to detect stego image as a three-step process with the following repercussions: (1) employing preprocessing step to enhance the input image, (2 feature extraction using the Mustard honey bee optimization algorithm and, thus, the extracted features will be dimensionally reduced (3) by classification using HSVGG-based CNN. Experimentation carried out on ALASKA2 data set and the results were compared.
{"title":"Stego Detection: Image Steganalysis Using a Novel Hidden Stego Visual Geometry Group–Based CNN Classification","authors":"Hemalatha Jeyaprakash, Balachander Chokkalingam, Vivek V, S. Mohan","doi":"10.1080/19361610.2022.2110637","DOIUrl":"https://doi.org/10.1080/19361610.2022.2110637","url":null,"abstract":"Abstract Steganography is the concept of embedding or hiding secret information into a cover image by maintaining the visual quality. Various algorithms are designed to classify stego images but the race still continues between Steganographer and Steganalyser. Advances in deep learning provided a solution to detect stego images. In this article, we coin a new paradigm to detect stego image as a three-step process with the following repercussions: (1) employing preprocessing step to enhance the input image, (2 feature extraction using the Mustard honey bee optimization algorithm and, thus, the extracted features will be dimensionally reduced (3) by classification using HSVGG-based CNN. Experimentation carried out on ALASKA2 data set and the results were compared.","PeriodicalId":44585,"journal":{"name":"Journal of Applied Security Research","volume":"18 1","pages":"979 - 999"},"PeriodicalIF":1.3,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47867152","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 : 2022-08-22DOI: 10.1080/19361610.2022.2113730
Molly M. Dundon, S. Houck
Abstract This article explores how foreign enemies of the United States target American citizens with propaganda intended to fuel societal division. It reviews propaganda conceptually, discusses individual, group, and cultural factors that make the United States is uniquely vulnerable to false propaganda, and details the processes and mechanisms by which adversarial propaganda attempts to create false narratives and perpetuate half-truths in the digital domain. It concludes with a discussion on how to mitigate adversarial propaganda’s effects.
{"title":"Adversarial Propaganda: How Enemies Target the U.S. to Fuel Division","authors":"Molly M. Dundon, S. Houck","doi":"10.1080/19361610.2022.2113730","DOIUrl":"https://doi.org/10.1080/19361610.2022.2113730","url":null,"abstract":"Abstract This article explores how foreign enemies of the United States target American citizens with propaganda intended to fuel societal division. It reviews propaganda conceptually, discusses individual, group, and cultural factors that make the United States is uniquely vulnerable to false propaganda, and details the processes and mechanisms by which adversarial propaganda attempts to create false narratives and perpetuate half-truths in the digital domain. It concludes with a discussion on how to mitigate adversarial propaganda’s effects.","PeriodicalId":44585,"journal":{"name":"Journal of Applied Security Research","volume":"18 1","pages":"1051 - 1059"},"PeriodicalIF":1.3,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44824007","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 : 2022-08-18DOI: 10.1080/19361610.2022.2111184
Jairo Jesús Pinto Hidalgo, Jorge Antonio Silva Centeno
Abstract Cocaine production has reached record levels in recent years. Latin America and the Caribbean are the primary sources of all cocaine consumed globally, thus there are indications that cocaine production processes could spread to countries of transit and consumption, becoming a threat to the security of states. In this article, we address the challenge of detecting potential primary infrastructures to produce coca paste in the border region of Venezuela and Colombia. We use geospatial intelligence and artificial intelligence to detect these objects in remote sensing images and identify their geographic location. We generated a dataset of 16,778 training samples that we named CocaPaste-PI-DETECTION, constructed from PlanetScope satellite imagery rated at NIIRS level 3, ground truth data, and A1, A2, and B2 information sources. An advanced deep learning model, specialized for object detection tasks, was trained. A mean Average Precision (mAP) score of 90.07% was obtained, and we analyzed generalization capabilities and conducted different experiments that demonstrated how the proposed methodology could strengthen intervention strategies against drug trafficking.
近年来，可卡因的产量达到了创纪录的水平。拉丁美洲和加勒比是全球消费的所有可卡因的主要来源，因此有迹象表明，可卡因的生产过程可能蔓延到过境国和消费国，对各国的安全构成威胁。在这篇文章中，我们解决了在委内瑞拉和哥伦比亚边境地区发现潜在的生产古柯膏的初级基础设施的挑战。我们利用地理空间智能和人工智能在遥感图像中检测这些物体并确定其地理位置。我们生成了一个包含16,778个训练样本的数据集，我们将其命名为cocapast - pi - detection，该数据集由NIIRS 3级的PlanetScope卫星图像、地面真实数据以及A1、A2和B2信息源构建而成。训练了一个专门用于目标检测任务的高级深度学习模型。平均精度(mAP)得分为90.07%，我们分析了该方法的泛化能力，并进行了不同的实验，以证明该方法可以加强对毒品贩运的干预策略。
{"title":"Geospatial Intelligence and Artificial Intelligence for Detecting Potential Coca Paste Production Infrastructure in the Border Region of Venezuela and Colombia","authors":"Jairo Jesús Pinto Hidalgo, Jorge Antonio Silva Centeno","doi":"10.1080/19361610.2022.2111184","DOIUrl":"https://doi.org/10.1080/19361610.2022.2111184","url":null,"abstract":"Abstract Cocaine production has reached record levels in recent years. Latin America and the Caribbean are the primary sources of all cocaine consumed globally, thus there are indications that cocaine production processes could spread to countries of transit and consumption, becoming a threat to the security of states. In this article, we address the challenge of detecting potential primary infrastructures to produce coca paste in the border region of Venezuela and Colombia. We use geospatial intelligence and artificial intelligence to detect these objects in remote sensing images and identify their geographic location. We generated a dataset of 16,778 training samples that we named CocaPaste-PI-DETECTION, constructed from PlanetScope satellite imagery rated at NIIRS level 3, ground truth data, and A1, A2, and B2 information sources. An advanced deep learning model, specialized for object detection tasks, was trained. A mean Average Precision (mAP) score of 90.07% was obtained, and we analyzed generalization capabilities and conducted different experiments that demonstrated how the proposed methodology could strengthen intervention strategies against drug trafficking.","PeriodicalId":44585,"journal":{"name":"Journal of Applied Security Research","volume":"18 1","pages":"1000 - 1050"},"PeriodicalIF":1.3,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42803393","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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