As one of the most effective techniques for finding software vulnerabilities,fuzzing has become a hot topic in software security.It feeds potentially syntactically or semantically malformed test data to a target progr...As one of the most effective techniques for finding software vulnerabilities,fuzzing has become a hot topic in software security.It feeds potentially syntactically or semantically malformed test data to a target program to mine vulnerabilities and crash the system.In recent years,considerable efforts have been dedicated by researchers and practitioners towards improving fuzzing,so there aremore and more methods and forms,whichmake it difficult to have a comprehensive understanding of the technique.This paper conducts a thorough survey of fuzzing,focusing on its general process,classification,common application scenarios,and some state-of-the-art techniques that have been introduced to improve its performance.Finally,this paper puts forward key research challenges and proposes possible future research directions that may provide new insights for researchers.展开更多
With the prevalence of machine learning in malware defense,hackers have tried to attack machine learning models to evade detection.It is generally difficult to explore the details of malware detection models,hackers c...With the prevalence of machine learning in malware defense,hackers have tried to attack machine learning models to evade detection.It is generally difficult to explore the details of malware detection models,hackers can adopt fuzzing attack to manipulate the features of the malware closer to benign programs on the premise of retaining their functions.In this paper,attack and defense methods on malware detection models based on machine learning algorithms were studied.Firstly,we designed a fuzzing attack method by randomly modifying features to evade detection.The fuzzing attack can effectively descend the accuracy of machine learning model with single feature.Then an adversarial malware detection model MaliFuzz is proposed to defend fuzzing attack.Different from the ordinary single feature detection model,the combined features by static and dynamic analysis to improve the defense ability are used.The experiment results show that the adversarial malware detection model with combined features can deal with the attack.The methods designed in this paper have great significance in improving the security of malware detection models and have good application prospects.展开更多
基金supported in part by the National Natural Science Foundation of China under Grants 62273272,62303375,and 61873277in part by the Key Research and Development Program of Shaanxi Province under Grant 2023-YBGY-243+1 种基金in part by the Natural Science Foundation of Shaanxi Province under Grant 2020JQ-758in part by the Youth Innovation Team of Shaanxi Universities,and in part by the Special Fund for Scientific and Technological Innovation Strategy of Guangdong Province under Grant 2022A0505030025.
文摘As one of the most effective techniques for finding software vulnerabilities,fuzzing has become a hot topic in software security.It feeds potentially syntactically or semantically malformed test data to a target program to mine vulnerabilities and crash the system.In recent years,considerable efforts have been dedicated by researchers and practitioners towards improving fuzzing,so there aremore and more methods and forms,whichmake it difficult to have a comprehensive understanding of the technique.This paper conducts a thorough survey of fuzzing,focusing on its general process,classification,common application scenarios,and some state-of-the-art techniques that have been introduced to improve its performance.Finally,this paper puts forward key research challenges and proposes possible future research directions that may provide new insights for researchers.
文摘With the prevalence of machine learning in malware defense,hackers have tried to attack machine learning models to evade detection.It is generally difficult to explore the details of malware detection models,hackers can adopt fuzzing attack to manipulate the features of the malware closer to benign programs on the premise of retaining their functions.In this paper,attack and defense methods on malware detection models based on machine learning algorithms were studied.Firstly,we designed a fuzzing attack method by randomly modifying features to evade detection.The fuzzing attack can effectively descend the accuracy of machine learning model with single feature.Then an adversarial malware detection model MaliFuzz is proposed to defend fuzzing attack.Different from the ordinary single feature detection model,the combined features by static and dynamic analysis to improve the defense ability are used.The experiment results show that the adversarial malware detection model with combined features can deal with the attack.The methods designed in this paper have great significance in improving the security of malware detection models and have good application prospects.
