恶意软件
计算机科学
杠杆(统计)
恶意软件分析
计算机安全
样品(材料)
数据科学
数据挖掘
人工智能
色谱法
化学
作者
Manuel Egele,Theodoor Scholte,Engin Kirda,Christopher Kruegel
标识
DOI:10.1145/2089125.2089126
摘要
Anti-virus vendors are confronted with a multitude of potentially malicious samples today. Receiving thousands of new samples every day is not uncommon. The signatures that detect confirmed malicious threats are mainly still created manually, so it is important to discriminate between samples that pose a new unknown threat and those that are mere variants of known malware. This survey article provides an overview of techniques based on dynamic analysis that are used to analyze potentially malicious samples. It also covers analysis programs that leverage these It also covers analysis programs that employ these techniques to assist human analysts in assessing, in a timely and appropriate manner, whether a given sample deserves closer manual inspection due to its unknown malicious behavior.
科研通智能强力驱动
Strongly Powered by AbleSci AI