E. Anupriya, N. Kumaresan, V. Suresh, S. Dhanasekaran, K. Ramprathap, P. Chinnasamy
{"title":"基于机器学习技术的社交网络欺诈账户检测","authors":"E. Anupriya, N. Kumaresan, V. Suresh, S. Dhanasekaran, K. Ramprathap, P. Chinnasamy","doi":"10.1109/ASSIC55218.2022.10088336","DOIUrl":null,"url":null,"abstract":"Nowadays, a person's impact is frequently determined by the number of followers he or she has on social media. To this aim, the prevalence of false accounts is one of the most pressing issues, with the potential to disrupt a wide range of real-world and economic activity. Bot followers are dangerous to social media as these could alter perceptions of popularity and influence, which can have a ample amount of impact on every sector. As a result, new approaches must be developed to enable the detection and classification of bogus accounts. This study gives novel method for distinguishing original profiles. The method uses information gathered automatically from huge data to characterize typical patterns of fake account.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fraud Account Detection on Social Network using Machine Learning Techniques\",\"authors\":\"E. Anupriya, N. Kumaresan, V. Suresh, S. Dhanasekaran, K. Ramprathap, P. Chinnasamy\",\"doi\":\"10.1109/ASSIC55218.2022.10088336\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays, a person's impact is frequently determined by the number of followers he or she has on social media. To this aim, the prevalence of false accounts is one of the most pressing issues, with the potential to disrupt a wide range of real-world and economic activity. Bot followers are dangerous to social media as these could alter perceptions of popularity and influence, which can have a ample amount of impact on every sector. As a result, new approaches must be developed to enable the detection and classification of bogus accounts. This study gives novel method for distinguishing original profiles. The method uses information gathered automatically from huge data to characterize typical patterns of fake account.\",\"PeriodicalId\":441406,\"journal\":{\"name\":\"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASSIC55218.2022.10088336\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASSIC55218.2022.10088336","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fraud Account Detection on Social Network using Machine Learning Techniques
Nowadays, a person's impact is frequently determined by the number of followers he or she has on social media. To this aim, the prevalence of false accounts is one of the most pressing issues, with the potential to disrupt a wide range of real-world and economic activity. Bot followers are dangerous to social media as these could alter perceptions of popularity and influence, which can have a ample amount of impact on every sector. As a result, new approaches must be developed to enable the detection and classification of bogus accounts. This study gives novel method for distinguishing original profiles. The method uses information gathered automatically from huge data to characterize typical patterns of fake account.