Abstract
We designed an innovative method, namely iBase, which automatically infers the image base address of an ARM32 binary by statistically, structurally, and semantically correlating the absolute and the relative addresses contained in the binary. iBase exploits ARM32’s architecture features, and hence it is immune to variances introduced by software development and compilation. In addition, iBase is parameter-free and it requires no manual configuration. We implemented iBase and performed evaluation using 20 ARM32 binaries. Our evaluation results have shown that iBase successfully detects base addresses for all of them and outperforms start-of-the-art tools including Ghidra and Radare2.
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References
Costin, A., Zaddach, J., Francillon, A., Balzarotti, D.: A large-scale analysis of the security of embedded firmwares. In: 23rd \(\{\)USENIX\(\}\) Security Symposium (\(\{\)USENIX\(\}\) Security 2014), pp. 95–110 (2014)
Basnight, Z.H.: Firmware counterfeiting and modification attacks on programmable logic controllers. Technical report, Air Force Inst of Tech Wright-Patterson AFB OH Graduate School of ... (2013)
Skochinsky, I.: Intro to embedded reverse engineering for pc reversers. In: REcon Conference, Montreal, Canada (2010)
Basnight, Z.: Firmware counterfeiting and modification attacks on programmable logic controllers. Master’s thesis, Air Force Institute of Technology (2013)
Zhu, R., Tan, Y., Zhang, Q., Wu, F., Zheng, J., Xue, Y.: Determining image base of firmware files for arm devices. IEICE Trans. Inf. Syst. 99(2), 351–359 (2016)
Zhu, R., Zhang, B., Tan, Y., Wan, Y., Wang, J.: Determining the image base of arm firmware by matching function addresses. Wirel. Commun. Mob. Comput. 2021 (2021)
Eagle, C., Nance, K.: The Ghidra Book: The Definitive Guide. No Starch Press, San Francisco (2020)
Ni, Z., Li, B., Sun, X., Chen, T., Tang, B., Shi, X.: Analyzing bug fix for automatic bug cause classification. J. Syst. Softw. 163, 110538 (2020)
Slowinska, A., Stancescu, T., Bos, H.: Howard: a dynamic excavator for reverse engineering data structures. In: NDSS (2011)
Du, Y., et al.: Learning symbolic operators: a neurosymbolic solution for autonomous disassembly of electric vehicle battery. arXiv preprint arXiv:2206.03027 (2022)
Zheng, Y., Davanian, A., Yin, H., Song, C., Zhu, H., Sun, L.: FIRM-AFL: high-throughput greybox fuzzing of IoT firmware via augmented process emulation. In: USENIX Security Symposium, pp. 1099–1114 (2019)
Kruegel, C., Robertson, W., Valeur, F., Vigna, G.: Static disassembly of obfuscated binaries. In: USENIX security Symposium, vol. 13, p. 18 (2004)
Shin, E.C.R., Song, D., Moazzezi, R.: Recognizing functions in binaries with neural networks. In: 24th \(\{\)USENIX\(\}\) Security Symposium (\(\{\)USENIX\(\}\) Security 2015), pp. 611–626 (2015)
ARM paging (2019). https://wiki.osdev.org/ARM_Paging
Identifying outliers: IQR method (2022). https://online.stat.psu.edu/stat200/lesson/3/3.2
ARM. ARM Architecture Reference Manual (2005)
Valvano, J., Yerraballi, R.: Embedded Systems - Shape The World, 5th edn. Jonathan Valvano, Texas (2014)
Platoformio (2014). https://docs.platformio.org/en/latest/
Ben-Gal, I.: Outlier detection. In: Data Mining and Knowledge Discovery Handbook, pp. 131–146 (2005)
Pang, G., Shen, C., Cao, L., Van Den Hengel, A.: Deep learning for anomaly detection: a review. ACM Comput. Surv. (CSUR) 54(2), 1–38 (2021)
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Chong, D., Zhang, J., Boland, N., Chen, L. (2024). Automatically Inferring Image Base Addresses of ARM32 Binaries Using Architecture Features. In: Wang, G., Wang, H., Min, G., Georgalas, N., Meng, W. (eds) Ubiquitous Security. UbiSec 2023. Communications in Computer and Information Science, vol 2034. Springer, Singapore. https://doi.org/10.1007/978-981-97-1274-8_29
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DOI: https://doi.org/10.1007/978-981-97-1274-8_29
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