{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:33:14Z","timestamp":1760232794577,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T00:00:00Z","timestamp":1669939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1836112","61876134","2020YFB1805400","2021YFB3100700"],"award-info":[{"award-number":["U1836112","61876134","2020YFB1805400","2021YFB3100700"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["U1836112","61876134","2020YFB1805400","2021YFB3100700"],"award-info":[{"award-number":["U1836112","61876134","2020YFB1805400","2021YFB3100700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Binary code similarity measurement is a popular research area in binary analysis with the recent development of deep learning-based models. Current state-of-the-art methods often use the pre-trained language model (PTLM) to embed instructions into basic blocks as representations of nodes within a control flow graph (CFG). These methods will then use the graph neural network (GNN) to embed the whole CFG and measure the binary similarities between these code embeddings. However, these methods almost directly treat the assembly code as a natural language text and ignore its code-specific features when training PTLM. Moreover, They barely consider the direction of edges in the CFG or consider it less efficient. The weaknesses of the above approaches may limit the performances of previous methods. In this paper, we propose a novel method called function similarity using code-specific PPTs and order-sensitive GNN (FUSION). Since the similarity of binary codes is a symmetric\/asymmetric problem, we were guided by the ideas of symmetry and asymmetry in our research. They measure the binary function similarity with two code-specific PTLM training strategies and an order-sensitive GNN, which, respectively, alleviate the aforementioned weaknesses. FUSION outperforms the state-of-the-art binary similarity methods by up to 5.4% in accuracy, and performs significantly better.<\/jats:p>","DOI":"10.3390\/sym14122549","type":"journal-article","created":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T02:18:06Z","timestamp":1669947486000},"page":"2549","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["FUSION: Measuring Binary Function Similarity with Code-Specific Embedding and Order-Sensitive GNN"],"prefix":"10.3390","volume":"14","author":[{"given":"Hao","family":"Gao","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, Wuhan University, Wuhan 430001, China"},{"name":"Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, Wuhan University, Wuhan 430001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, Wuhan University, Wuhan 430001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan 430001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lina","family":"Wang","sequence":"additional","affiliation":[{"name":"Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, Wuhan University, Wuhan 430001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fajiang","family":"Yu","sequence":"additional","affiliation":[{"name":"Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, Wuhan University, Wuhan 430001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Brumley, D., Poosankam, P., Song, D., and Zheng, J. (2008, January 18\u201321). Automatic patch-based exploit generation is possible: Techniques and implications. Proceedings of the 2008 IEEE Symposium on Security and Privacy (sp 2008), Oakland, CA, USA.","DOI":"10.1109\/SP.2008.17"},{"key":"ref_2","first-page":"8","article-title":"Scalable, behavior-based malware clustering","volume":"9","author":"Bayer","year":"2009","journal-title":"NDSS"},{"key":"ref_3","unstructured":"Jang, J., Woo, M., and Brumley, D. (2013, January 14\u201316). Towards automatic software lineage inference. Proceedings of the 22nd USENIX Security Symposium (USENIX Security 13), Washington, DC, USA."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Xu, X., Liu, C., Feng, Q., Yin, H., Song, L., and Song, D.X. (November, January 30). Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, TX, USA.","DOI":"10.1145\/3133956.3134018"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Duan, Y., Li, X., Wang, J., and Yin, H. (2020, January 23\u201326). Deepbindiff: Learning program-wide code representations for binary diffing. Proceedings of the Network and Distributed System Security Symposium, San Diego, CA, USA.","DOI":"10.14722\/ndss.2020.24311"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3428293","article-title":"Neural reverse engineering of stripped binaries using augmented control flow graphs","volume":"4","author":"David","year":"2020","journal-title":"Proc. Acm Program. Lang."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Massarelli, L., Luna, G.A.D., Petroni, F., Querzoni, L., and Baldoni, R. (2019, January 24\u201327). Investigating Graph Embedding Neural Networks with Unsupervised Features Extraction for Binary Analysis. Proceedings of the 2019 Workshop on Binary Analysis Research, San Diego, CA, USA.","DOI":"10.14722\/bar.2019.23020"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ding, S.H.H., Fung, B.C.M., and Charland, P. (2019, January 19\u201323). Asm2Vec: Boosting Static Representation Robustness for Binary Clone Search against Code Obfuscation and Compiler Optimization. Proceedings of the 2019 IEEE Symposium on Security and Privacy (SP), Francisco, CA, USA.","DOI":"10.1109\/SP.2019.00003"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zuo, F., Li, X., Zhang, Z., Young, P., Luo, L., and Zeng, Q. (2019). Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs. arXiv.","DOI":"10.14722\/ndss.2019.23492"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yu, Z., Cao, R., Tang, Q., Nie, S., Huang, J., and Wu, S. (2020, January 7\u201312). Order Matters: Semantic-Aware Neural Networks for Binary Code Similarity Detection. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i01.5466"},{"key":"ref_11","unstructured":"Pei, K., Xuan, Z., Yang, J., Jana, S.S., and Ray, B. (2020). Trex: Learning Execution Semantics from Micro-Traces for Binary Similarity. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, X., Yu, Q., and Yin, H. (2021, January 15\u201319). PalmTree: Learning an Assembly Language Model for Instruction Embedding. Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, Virtual Event, Republic of Korea.","DOI":"10.1145\/3460120.3484587"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gui, Y., Wan, Y., Zhang, H., Huang, H., Sui, Y., Xu, G., Shao, Z., and Jin, H. (2022). Cross-Language Binary-Source Code Matching with Intermediate Representations. arXiv.","DOI":"10.1109\/SANER53432.2022.00077"},{"key":"ref_14","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Feng, Q., Zhou, R., Xu, C., Cheng, Y., Testa, B., and Yin, H. (2016, January 24\u201328). Scalable Graph-based Bug Search for Firmware Images. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, Vienna, Austria.","DOI":"10.1145\/2976749.2978370"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, B., Huo, W., Zhang, C., Li, W., Li, F., Piao, A., and Zou, W. (2018, January 3\u20137). \u03b1 Diff: Cross-Version Binary Code Similarity Detection with DNN. Proceedings of the 2018 33rd IEEE\/ACM International Conference on Automated Software Engineering (ASE), Montpellier, France.","DOI":"10.1145\/3238147.3238199"},{"key":"ref_17","unstructured":"Li, Y., Gu, C., Dullien, T., Vinyals, O., and Kohli, P. (2019, January 10\u201315). Graph Matching Networks for Learning the Similarity of Graph Structured Objects. Proceedings of the ICML International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_18","unstructured":"Ling, X., Wu, L., Wang, S., Ma, T., Xu, F., Wu, C., and Ji, S. (2020). Hierarchical Graph Matching Networks for Deep Graph Similarity Learning. arXiv."},{"key":"ref_19","unstructured":"Yu, Z., Zheng, W., Wang, J., Tang, Q., Nie, S., and Wu, S. (2020, January 6\u201312). CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code Matching. Proceedings of the NeurIPS 2020, Vancouver, BC, Canada."},{"key":"ref_20","unstructured":"Bruna, J., Zaremba, W., Szlam, A.D., and LeCun, Y. (2014). Spectral Networks and Locally Connected Networks on Graphs. CoRR."},{"key":"ref_21","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio\u2019, P., and Bengio, Y. (2018). Graph Attention Networks. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shoshitaishvili, Y., Wang, R., Salls, C., Stephens, N., Polino, M., Dutcher, A., Grosen, J., Feng, S., Hauser, C., and Kruegel, C. (2016, January 22\u201326). SoK: (State of) The Art of War: Offensive Techniques in Binary Analysis. Proceedings of the IEEE Symposium on Security and Privacy, San Jose, CA, USA.","DOI":"10.1109\/SP.2016.17"},{"key":"ref_23","first-page":"309","article-title":"SAFE: Self-Attentive Function Embeddings for Binary Similarity","volume":"Volume 11543","author":"Massarelli","year":"2019","journal-title":"Proceedings of the Detection of Intrusions and Malware, and Vulnerability Assessment\u201416th International Conference, DIMVA 2019"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106320","DOI":"10.1016\/j.infsof.2020.106320","article-title":"Semantically find similar binary codes with mixed key instruction sequence","volume":"125","author":"Li","year":"2020","journal-title":"Inf. Softw. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Luo, Z., Wang, B., Tang, Y., and Xie, W. (2019). Semantic-Based Representation Binary Clone Detection for Cross-Architectures in the Internet of Things. Appl. Sci., 9.","DOI":"10.3390\/app9163283"},{"key":"ref_26","unstructured":"Hamilton, W., Ying, Z., and Leskovec, J. (2017, January 4\u20139). Inductive representation learning on large graphs. Proceedings of the 2017 Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_27","unstructured":"Marcelli, A., Graziano, M., Ugarte-Pedrero, X., Fratantonio, Y., Mansouri, M., and Balzarotti, D. (2022, January 10\u201312). How Machine Learning Is Solving the Binary Function Similarity Problem. 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