Benni Purnama, D. Stiawan, Darmawijoyo Hanapi, E. Winanto, R. Budiarto, Mohd Yazid Bin Idris
{"title":"基于多层感知器(MLP)的n-gram效应恶意软件检测","authors":"Benni Purnama, D. Stiawan, Darmawijoyo Hanapi, E. Winanto, R. Budiarto, Mohd Yazid Bin Idris","doi":"10.23919/eecsi53397.2021.9624273","DOIUrl":null,"url":null,"abstract":"Malware is a threat that can compromise cyber security. Currently, the development of malware is becoming increasingly complex and difficult to detect. One way to improve detection accuracy is to implement the n-gram feature extraction. n-gram is one of method to analyze malware, by capturing the frequency of string/opcode which often appear from malware. This work aims to improve the performance of malware detection by evaluating the best number of n-grams to extract the opcode. Selection of n number in n-gram process will be very influencing in malware classification result. This research work investigates the effect the n value of n-gram on the accuracy detection by varying the value n = 1 to n = 5. The best accuracy detection in the experiments using Multilayer Perceptron (MLP) classifier reaches 89 percent.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"n-gram Effect in Malware Detection Using Multilayer Perceptron (MLP)\",\"authors\":\"Benni Purnama, D. Stiawan, Darmawijoyo Hanapi, E. Winanto, R. Budiarto, Mohd Yazid Bin Idris\",\"doi\":\"10.23919/eecsi53397.2021.9624273\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Malware is a threat that can compromise cyber security. Currently, the development of malware is becoming increasingly complex and difficult to detect. One way to improve detection accuracy is to implement the n-gram feature extraction. n-gram is one of method to analyze malware, by capturing the frequency of string/opcode which often appear from malware. This work aims to improve the performance of malware detection by evaluating the best number of n-grams to extract the opcode. Selection of n number in n-gram process will be very influencing in malware classification result. This research work investigates the effect the n value of n-gram on the accuracy detection by varying the value n = 1 to n = 5. The best accuracy detection in the experiments using Multilayer Perceptron (MLP) classifier reaches 89 percent.\",\"PeriodicalId\":259450,\"journal\":{\"name\":\"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/eecsi53397.2021.9624273\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/eecsi53397.2021.9624273","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
n-gram Effect in Malware Detection Using Multilayer Perceptron (MLP)
Malware is a threat that can compromise cyber security. Currently, the development of malware is becoming increasingly complex and difficult to detect. One way to improve detection accuracy is to implement the n-gram feature extraction. n-gram is one of method to analyze malware, by capturing the frequency of string/opcode which often appear from malware. This work aims to improve the performance of malware detection by evaluating the best number of n-grams to extract the opcode. Selection of n number in n-gram process will be very influencing in malware classification result. This research work investigates the effect the n value of n-gram on the accuracy detection by varying the value n = 1 to n = 5. The best accuracy detection in the experiments using Multilayer Perceptron (MLP) classifier reaches 89 percent.