基于遺傳算法的惡意代碼對抗樣本生成方法
doi: 10.11999/JEIT191059
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中國科學(xué)院大學(xué)計算機(jī)科學(xué)與技術(shù)學(xué)院 北京 100190
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2.
中國科學(xué)院軟件研究所可信計算與信息保障實(shí)驗(yàn)室 北京 100190
基金項(xiàng)目: 國家自然科學(xué)基金(61902384, U1836117, U1836113)
Method for Generating Malicious Code Adversarial Samples Based on Genetic Algorithm
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School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100190, China
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Trusted Computing and Information Assurance Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
Funds: The National Natural Science Foundation of China (61902384, U1836117, U1836113)
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摘要: 機(jī)器學(xué)習(xí)已經(jīng)廣泛應(yīng)用于惡意代碼檢測中，并在惡意代碼檢測產(chǎn)品中發(fā)揮重要作用。構(gòu)建針對惡意代碼檢測機(jī)器學(xué)習(xí)模型的對抗樣本，是發(fā)掘惡意代碼檢測模型缺陷，評估和完善惡意代碼檢測系統(tǒng)的關(guān)鍵。該文提出一種基于遺傳算法的惡意代碼對抗樣本生成方法，生成的樣本在有效對抗基于機(jī)器學(xué)習(xí)的惡意代碼檢測模型的同時，確保了惡意代碼樣本的可執(zhí)行和惡意行為的一致性，有效提升了生成對抗樣本的真實(shí)性和模型對抗評估的準(zhǔn)確性。實(shí)驗(yàn)表明，該文提出的對抗樣本生成方法使MalConv惡意代碼檢測模型的檢測準(zhǔn)確率下降了14.65%；并可直接對VirusTotal中4款基于機(jī)器學(xué)習(xí)的惡意代碼檢測商用引擎形成有效的干擾，其中，Cylance的檢測準(zhǔn)確率只有53.55%。
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關(guān)鍵詞:
- 惡意代碼檢測 /
- 機(jī)器學(xué)習(xí) /
- 對抗樣本
Abstract: Machine learning is widely used in malicious code detection and plays an important role in malicious code detection products. Constructing adversarial samples for malicious code detection machine learning models is the key to discovering defects in malicious code detection models, evaluating and improving malicious code detection systems. This paper proposes a method for generating malicious code adversarial samples based on genetic algorithms. The generated samples combat effectively the malicious code detection model based on machine learning, while ensuring the consistency of the executable and malicious behavior of malicious code samples, and improving effectively the authenticity of the generated adversarial samples and the accuracy of the model adversarial evaluation are presented. The experiments show that the proposed method of generating adversarial samples reduces the detection accuracy of the MalConv malicious code detection model by 14.65%, and can directly interfere with four commercial machine-based malicious code detection engines in VirusTotal. Among them, the accuracy rate of Cylance detection is only 53.55%.-
Key words:
- Malware detection /
- Machine learning /
- Adversarial sample
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表 1 PE文件改寫原子操作
改寫模塊 改寫內(nèi)容 PE頭文件 PE標(biāo)志位修改 PE文件校驗(yàn)和修改 節(jié)表 導(dǎo)入表添加冗余導(dǎo)入函數(shù) 節(jié)表模塊重命名 節(jié)表冗余信息填充 節(jié)表新模塊添加 PE文件 加殼、脫殼操作 
下載: 導(dǎo)出CSV
表 2 實(shí)驗(yàn)數(shù)據(jù)統(tǒng)計信息
樣本 訓(xùn)練集 測試集 良性樣本 7059 784 惡意樣本 6593 732 總數(shù) 13652 1516
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表 3 惡意代碼檢測引擎檢測結(jié)果
評測樣本集 良性樣本誤報 惡意樣本誤報 誤報樣本綜述 模型檢測準(zhǔn)確率(%) 原始樣本集 7 10 17 98.88 初代對抗樣本集 37 9 46 96.97 優(yōu)化后的對抗樣本集 228 11 239 84.23
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表 4 廠商產(chǎn)品的檢測成功率
惡意代碼檢測引擎 誤報樣本數(shù) 檢測逃逸率(%) Cylance 111 46.45 Endgame 43 17.99 Sophos ML 50 20.92 Trapmine 35 14.64
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