Page 206 - 《软件学报》2026年第6期
P. 206
陈永威 等: 基于异质图匹配网络的恶意软件相似性度量方法 2525
[13] Xu XJ, Liu C, Feng Q, Yin H, Song L, Song D. Neural network-based graph embedding for cross-platform binary code similarity
detection. In: Proc. of the 2017 ACM SIGSAC Conf. on Computer and Communications Security. Dallas: ACM, 2017. 363–376. [doi: 10.
1145/3133956.3134018]
[14] Puodzius C, Zendra O, Heuser A, Noureddine L. Accurate and robust malware analysis through similarity of external calls dependency
graphs (ECDG). In: Proc. of the 16th Int’l Conf. on Availability, Reliability and Security. Vienna: ACM, 2021. 57. [doi: 10.1145/
3465481.3470115]
[15] Sun HQ, Shu H, Kang F, Guang Y. ModDiff: Modularity similarity-based malware homologation detection. Electronics, 2023, 12(10):
2258. [doi: 10.3390/electronics12102258]
[16] Wang M, Interrante-Grant A, Whelan R, Leek T. COBRA-GCN: Contrastive learning to optimize binary representation analysis with
graph convolutional networks. In: Proc. of the 19th Int’l Conf. on Detection of Intrusions and Malware, and Vulnerability Assessment.
Cagliari: Springer, 2022. 53–74. [doi: 10.1007/978-3-031-09484-2_4]
[17] Chen YH, Chen JL, Deng RF. Similarity-based malware classification using graph neural networks. Applied Sciences, 2022, 12(21):
10837. [doi: 10.3390/app122110837]
[18] Schlichtkrull M, Kipf TN, Bloem P, van den Berg R, Titov I, Welling M. Modeling relational data with graph convolutional networks. In:
Proc. of the 15th Int’l Conf. on the Semantic Web. Heraklion: Springer, 2018. 593–607. [doi: 10.1007/978-3-319-93417-4_38]
[19] Zhang CX, Song DJ, Huang C, Swami A, Chawla NV. Heterogeneous graph neural network. In: Proc. of the 25th ACM SIGKDD Int’l
Conf. on Knowledge Discovery & Data Mining. Anchorage: ACM, 2019. 793–803. [doi: 10.1145/3292500.3330961]
[20] Li YJ, Gu CJ, Dullien T, Vinyals O, Kohli P. Graph matching networks for learning the similarity of graph structured objects. In: Proc. of
the 36th Int’l Conf. on Machine Learning. Long Beach: PMLR, 2019. 3835–3845.
[21] Bai YS, Ding H, Gu K, Sun YZ, Wang W. Learning-based efficient graph similarity computation via multi-scale convolutional set
matching. In: Proc. of the 34th AAAI Conf. on Artificial Intelligence. New York: AAAI, 2020. 3219–3226. [doi: 10.1609/aaai.v34i04.
5720]
[22] Ling X, Wu LF, Wang SZ, Ma TF, Xu FL, Liu AX, Wu CM, Ji SL. Multilevel graph matching networks for deep graph similarity
learning. IEEE Trans. on Neural Networks and Learning Systems, 2023, 34(2): 799–813. [doi: 10.1109/TNNLS.2021.3102234]
[23] Zhao S, Ma XB, Zou W, Bai B. DeepCG: Classifying metamorphic malware through deep learning of call graphs. In: Proc. of the 15th
EAI Int’l Conf. on Security and Privacy in Communication Networks. Orlando: Springer, 2019. 171–190. [doi: 10.1007/978-3-030-37228-
6_9]
[24] Kim D, Kim E, Cha SK, Son S, Kim Y. Revisiting binary code similarity analysis using interpretable feature engineering and lessons
learned. IEEE Trans. on Software Engineering, 2023, 49(4): 1661–1682. [doi: 10.1109/TSE.2022.3187689]
[25] Zhu SF, Shi JJ, Yang LM, Qin BQ, Zhang ZY, Song LH, Wang G. Measuring and modeling the label dynamics of online anti-malware
engines. In: Proc. of the 29th USENIX Security Symp. USENIX, 2020. 2361–2378.
[26] Sebastián S, Caballero J. AVclass2: Massive malware tag extraction from AV labels. In: Proc. of the 36th Annual Computer Security
Applications Conf. Austin: ACM, 2020. 42–53. [doi: 10.1145/3427228.3427261]
[27] Yang LM, Ciptadi A, Laziuk I, Ahmadzadeh A, Wang G. BODMAS: An open dataset for learning based temporal analysis of PE
malware. In: Proc. of the 2021 IEEE Security and Privacy Workshops (SPW). San Francisco: IEEE, 2021. 78–84. [doi: 10.1109/
SPW53761.2021.00020]
[28] Ding SHH, Fung BCM, Charland P. Asm2Vec: Boosting static representation robustness for binary clone search against code obfuscation
and compiler optimization. In: Proc. of the 2019 IEEE Symp. on Security and Privacy (SP). San Francisco: IEEE, 2019. 472–489. [doi: 10.
1109/SP.2019.00003]
[29] Oliver J, Forman S, Cheng C. Using randomization to attack similarity digests. In: Proc. of the 2014 Int’l Conf. on Applications and
Techniques in Information Security. Melbourne: Springer, 2014. 199–210. [doi: 10.1007/978-3-662-45670-5_19]
[30] Andriesse D, Chen X, van der Veen V, Slowinska A, Bos H. An in-depth analysis of disassembly on full-scale x86/x64 binaries. In: Proc.
of the 25th USENIX Security Symp. Washington: USENIX, 2016. 583–600.
[31] Pang CB, Yu RT, Chen YH, Koskinen E, Portokalidis G, Mao B, Xu J. SoK: All you ever wanted to know about x86/x64 binary
disassembly but were afraid to ask. In: Proc. of the 2021 IEEE Symp. on Security and Privacy (SP). San Francisco: IEEE, 2021. 833–851.
[doi: 10.1109/SP40001.2021.00012]
附中文参考文献
[3] 谷勇浩, 王翼翡, 刘威歆, 吴铁军, 孟国柱. 基于多重异质图的恶意软件相似性度量方法. 软件学报, 2023, 34(7): 3188–3205. http://
www.jos.org.cn/1000-9825/6538.htm [doi: 10.13328/j.cnki.jos.006538]

