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Besides, similarity-based classifications exhibit insufficiency because the mutation and fuzzification of malware exacerbate classification difficulties. In order to improve malware classification speed and attend to mutation, this research proposes the ameliorated progressive classification that integrates static analysis and improved k-means algorithm. This proposed classification aims at assisting network administrators to have a malware classification preprocess and make efficient malware classifications upon the capture of new malware, thus enhancing the defense against malware.<\/jats:p>","DOI":"10.4018\/ijamc.2018070101","type":"journal-article","created":{"date-parts":[[2018,4,16]],"date-time":"2018-04-16T13:02:16Z","timestamp":1523883736000},"page":"1-12","source":"Crossref","is-referenced-by-count":0,"title":["Advancing Malware Classification With an Evolving Clustering Method"],"prefix":"10.4018","volume":"9","author":[{"given":"Chia-Mei","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Information Management, National Sun Yat-sen University, Kaohsiung, Taiwan"}]},{"given":"Shi-Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Information Management, National Sun Yat-sen University, Kaohsiung, Taiwan"}]}],"member":"2432","reference":[{"key":"IJAMC.2018070101-0","doi-asserted-by":"crossref","unstructured":"Agrawal, H., Bahler, L., Micallef, J., Snyder, S., & Virodov, A. 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