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基于污点和概率的逃逸恶意软件多路径探索

Multi-path exploration guided by taint and probability against evasive malware
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摘要 Static analysis is often impeded by malware obfuscation techniques,such as encryption and packing,whereas dynamic analysis tends to be more resistant to obfuscation by leveraging concrete execution information.Unfortunately,malware can employ evasive techniques to detect the analysis environment and alter its behavior accordingly.While known evasive techniques can be explicitly dismantled,the challenge lies in generically dismantling evasions without full knowledge of their conditions or implementations,such as logic bombs that rely on uncertain conditions,let alone unsupported evasive techniques,which contain evasions without corresponding dismantling strategies and those leveraging unknown implementations.In this paper,we present Antitoxin,a prototype for automatically exploring evasive malware.Antitoxin utilizes multi-path exploration guided by taint analysis and probability calculations to effectively dismantle evasive techniques.The probabilities of branch execution are derived from dynamic coverage,while taint analysis helps identify paths associated with evasive techniques that rely on uncertain conditions.Subsequently,Antitoxin prioritizes branches with lower execution probabilities and those influenced by taint analysis for multi-path exploration.This is achieved through forced execution,which forcefully sets the outcomes of branches on selected paths.Additionally,Antitoxin employs active anti-evasion countermeasures to dismantle known evasive techniques,thereby reducing exploration overhead.Furthermore,Antitoxin provides valuable insights into sensitive behaviors,facilitating deeper manual analysis.Our experiments on a set of highly evasive samples demonstrate that Antitoxin can effectively dismantle evasive techniques in a generic manner.The probability calculations guide the multi-path exploration of evasions without requiring prior knowledge of their conditions or implementations,enabling the dismantling of unsupported techniques such as C2 and significantly improving efficiency compared to linear exploration when dealing with complex control flows.Additionally,taint analysis can accurately identify branches related to logic bombs,facilitating preferential exploration. Static analysis is often impeded by malware obfuscation techniques,such as encryption and packing,whereas dynamic analysis tends to be more resistant to obfuscation by leveraging concrete execution information.Unfortunately,malware can employ evasive techniques to detect the analysis environment and alter its behavior accordingly.While known evasive techniques can be explicitly dismantled,the challenge lies in generically dismantling evasions without full knowledge of their conditions or implementations,such as logic bombs that rely on uncertain conditions,let alone unsupported evasive techniques,which contain evasions without corresponding dismantling strategies and those leveraging unknown implementations.In this paper,we present Antitoxin,a prototype for automatically exploring evasive malware.Antitoxin utilizes multi-path exploration guided by taint analysis and probability calculations to effectively dismantle evasive techniques.The probabilities of branch execution are derived from dynamic coverage,while taint analysis helps identify paths associated with evasive techniques that rely on uncertain conditions.Subsequently,Antitoxin prioritizes branches with lower execution probabilities and those influenced by taint analysis for multi-path exploration.This is achieved through forced execution,which forcefully sets the outcomes of branches on selected paths.Additionally,Antitoxin employs active anti-evasion countermeasures to dismantle known evasive techniques,thereby reducing exploration overhead.Furthermore,Antitoxin provides valuable insights into sensitive behaviors,facilitating deeper manual analysis.Our experiments on a set of highly evasive samples demonstrate that Antitoxin can effectively dismantle evasive techniques in a generic manner.The probability calculations guide the multi-path exploration of evasions without requiring prior knowledge of their conditions or implementations,enabling the dismantling of unsupported techniques such as C2 and significantly improving efficiency compared to linear exploration when dealing with complex control flows.Additionally,taint analysis can accurately identify branches related to logic bombs,facilitating preferential exploration.
作者 徐钫洲 张网 羌卫中 金海 Fangzhou Xu;Wang Zhang;Weizhong Qiang;Hai Jin(National Engineering Research Center for Big Data Technology and System,Wuhan 430074,China;Services Computing Technology and System Lab,Cluster and Grid Computing Lab,Wuhan 430074,China;Hubei Key Laboratory of Distributed System Security,Hubei Engineering Research Center on Big Data Security,Wuhan 430074,China;School of Cyber Science and Engineering,Huazhong University of Science and Technology,Wuhan 430074,China;School of Computer Science and Technology,Huazhong University of Science and Technology,Wuhan 430074,China;Jinyinhu Laboratory,Wuhan 430040,China)
出处 《Security and Safety》 2023年第3期83-106,共24页 一体化安全(英文)
基金 supported in part by the National Natural Science Foundation of China(Grant No.62272181)
关键词 Malware analysis dynamic binary instrumentation forced execution taint analysis evasion detection Malware analysis dynamic binary instrumentation forced execution taint analysis evasion detection
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