{"title":"基于BERT Transformer的基于图的Android恶意软件检测与分类","authors":"A. Saracino, Marco Simoni","doi":"10.1145/3600160.3605057","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a novel approach to Android malware analysis and categorization that leverages the power of BERT (Bidirectional Encoder Representations from Transformers) to classify API call sequences generated from Android API Call Graph. By utilizing the API Call Graph, our approach captures the intricate relationships and dependencies between API calls, enabling a deeper understanding of the behavior exhibited by Android malware. Our results show that our approach achieves high accuracy in classifying API call sequences as malicious or benign and the method provides a promising solution also for categorizing Android malware and can help mitigate the risks posed by malicious Android applications.","PeriodicalId":107145,"journal":{"name":"Proceedings of the 18th International Conference on Availability, Reliability and Security","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Graph-Based Android Malware Detection and Categorization through BERT Transformer\",\"authors\":\"A. Saracino, Marco Simoni\",\"doi\":\"10.1145/3600160.3605057\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a novel approach to Android malware analysis and categorization that leverages the power of BERT (Bidirectional Encoder Representations from Transformers) to classify API call sequences generated from Android API Call Graph. By utilizing the API Call Graph, our approach captures the intricate relationships and dependencies between API calls, enabling a deeper understanding of the behavior exhibited by Android malware. Our results show that our approach achieves high accuracy in classifying API call sequences as malicious or benign and the method provides a promising solution also for categorizing Android malware and can help mitigate the risks posed by malicious Android applications.\",\"PeriodicalId\":107145,\"journal\":{\"name\":\"Proceedings of the 18th International Conference on Availability, Reliability and Security\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 18th International Conference on Availability, Reliability and Security\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3600160.3605057\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 18th International Conference on Availability, Reliability and Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3600160.3605057","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Graph-Based Android Malware Detection and Categorization through BERT Transformer
In this paper, we propose a novel approach to Android malware analysis and categorization that leverages the power of BERT (Bidirectional Encoder Representations from Transformers) to classify API call sequences generated from Android API Call Graph. By utilizing the API Call Graph, our approach captures the intricate relationships and dependencies between API calls, enabling a deeper understanding of the behavior exhibited by Android malware. Our results show that our approach achieves high accuracy in classifying API call sequences as malicious or benign and the method provides a promising solution also for categorizing Android malware and can help mitigate the risks posed by malicious Android applications.