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Formal Semantics Extraction from Natural Language Specifications for ARM

  • Anh V. VuEmail author
  • Mizuhito Ogawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11800)

Abstract

This paper proposes a method to systematically extract the formal semantics of ARM instructions from their natural language specifications. Although ARM is based on RISC architecture and the number of instructions is relatively small, an abundance of variations diversely exist under various series including Cortex-A, Cortex-M, and Cortex-R. Thus, the semi-automatic semantics formalisation of rather simple instructions results in reducing tedious human efforts for tool developments e.g., the symbolic execution. We concentrate on six variations: M0, M0+, M3, M4, M7, and M33 of ARM Cortex-M series, aiming at covering IoT malware. Our systematic approach consists of the semantics interpretation by applying translation rules, augmented by the sentences similarity analysis to recognise the modification of flags. Among 1039 collected specifications, the formal semantics of 662 instructions have been successfully extracted by using only 228 manually prepared rules. They are utilised afterwards to preliminarily build a dynamic symbolic execution tool for Cortex-M called Corana. We experimentally observe that Corana is capable of effectively tracing IoT malware under the presence of obfuscation techniques like indirect jumps, as well as correctly detecting dead conditional branches, which are regarded as opaque predicates.

Keywords

Semantics formalisation Dynamic symbolic execution Iot malware analysis Natural language processing ARM Cortex-M 

Notes

Acknowledgments

We are grateful to Nao Hirokawa, Le Minh Nguyen, and the anonymous reviewers of FM’19 for their insightful feedback and invaluable comments. We sincerely thank Xuan Tung Vu, Thi Hai Yen Vuong, and Lam Hoang Yen Nguyen for their constructive discussions, as well as Thu Trang Hoang for her sharp comments on some grammatical issues. This study is partially supported by JSPS KAKENHI Grant-in-Aid for Scientific Research (B) 19H04083.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Japan Advanced Institute of Science and TechnologyNomiJapan

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