Automatic malware mutant detection and group classification based on the n-gram and clustering coefficient

التفاصيل البيبلوغرافية
العنوان: Automatic malware mutant detection and group classification based on the n-gram and clustering coefficient
المؤلفون: Youngsang Shin, Jin Kwak, Bomin Choi, Taejin Lee
المصدر: The Journal of Supercomputing.
بيانات النشر: Springer Nature
مصطلحات موضوعية: 021110 strategic, defence & security studies, Software_OPERATINGSYSTEMS, Computer science, 0211 other engineering and technologies, 02 engineering and technology, computer.software_genre, Computer security, Theoretical Computer Science, Identification (information), ComputingMilieux_MANAGEMENTOFCOMPUTINGANDINFORMATIONSYSTEMS, n-gram, Hardware and Architecture, 0202 electrical engineering, electronic engineering, information engineering, Malware, 020201 artificial intelligence & image processing, Data mining, Asprox botnet, computer, Software, Clustering coefficient, Information Systems
الوصف: The majority of recent cyber incidents have been caused by malware. According to a report by Symantec, an average of one million malicious codes is found daily. Automated static and dynamic analysis technologies are generally applied to cope with this, but most of the new malicious codes are the mutants of existing malware. In this paper, we present technology that automatically detects the n-gram and clustering coefficient-based malware mutants and that automatically groups the different types of malware. We verified our system by applying more than 2600 malicious codes. Our proposed technology does more than just respond to malware as it can also provide the ground for the effective analysis of new malware, the trend analysis of a malware group, the automatic identification of specific malware, and the analysis of the estimated trend of an attacker.
اللغة: English
تدمد: 0920-8542
DOI: 10.1007/s11227-015-1594-6
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b0dbe0063d4191429dae353b2c6cb8f5
Rights: OPEN
رقم الانضمام: edsair.doi.dedup.....b0dbe0063d4191429dae353b2c6cb8f5
قاعدة البيانات: OpenAIRE
الوصف
تدمد:09208542
DOI:10.1007/s11227-015-1594-6