Enhancing Symbolic Execution by Machine Learning Based Solver Selection

Machine learning steered symbolic execution framework for complex software code

Formal Aspects of Computing ◽

10.1007/s00165-021-00538-3 ◽

2021 ◽

Author(s):

Lei Bu ◽

Yongjuan Liang ◽

Zhunyi Xie ◽

Hong Qian ◽

Yi-Qi Hu ◽

...

Keyword(s):

Machine Learning ◽

Symbolic Execution ◽

Software Code

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On Characteristics of Symbolic Execution in the Problem of Assessing the Quality of Obfuscating Transformations

Modeling and Analysis of Information Systems ◽

10.18255/1818-1015-2021-1-38-51 ◽

2021 ◽

Vol 28 (1) ◽

pp. 38-51

Author(s):

Petr D. Borisov ◽

Yury V. Kosolapov

Keyword(s):

Machine Learning ◽

Dynamic Characteristics ◽

Symbolic Execution ◽

Specific Class ◽

Static Characteristics ◽

Learning Methods ◽

Static And Dynamic Characteristics ◽

Machine Learning Methods ◽

Relevant Task

Obfuscation is used to protect programs from analysis and reverse engineering. There are theoretically effective and resistant obfuscation methods, but most of them are not implemented in practice yet. The main reasons are large overhead for the execution of obfuscated code and the limitation of application only to a specific class of programs. On the other hand, a large number of obfuscation methods have been developed that are applied in practice. The existing approaches to the assessment of such obfuscation methods are based mainly on the static characteristics of programs. Therefore, the comprehensive (taking into account the dynamic characteristics of programs) justification of their effectiveness and resistance is a relevant task. It seems that such a justification can be made using machine learning methods, based on feature vectors that describe both static and dynamic characteristics of programs. In this paper, it is proposed to build such a vector on the basis of characteristics of two compared programs: the original and obfuscated, original and deobfuscated, obfuscated and deobfuscated. In order to obtain the dynamic characteristics of the program, a scheme based on a symbolic execution is constructed and presented in this paper. The choice of the symbolic execution is justified by the fact that such characteristics can describe the difficulty of comprehension of the program in dynamic analysis. The paper proposes two implementations of the scheme: extended and simplified. The extended scheme is closer to the process of analyzing a program by an analyst, since it includes the steps of disassembly and translation into intermediate code, while in the simplified scheme these steps are excluded. In order to identify the characteristics of symbolic execution that are suitable for assessing the effectiveness and resistance of obfuscation based on machine learning methods, experiments with the developed schemes were carried out. Based on the obtained results, a set of suitable characteristics is determined.

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Symbolic execution of complex program driven by machine learning based constraint solving

Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering - ASE 2016 ◽

10.1145/2970276.2970364 ◽

2016 ◽

Cited By ~ 8

Author(s):

Xin Li ◽

Yongjuan Liang ◽

Hong Qian ◽

Yi-Qi Hu ◽

Lei Bu ◽

...

Keyword(s):

Machine Learning ◽

Symbolic Execution ◽

Constraint Solving ◽

Complex Program

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SADPonzi: Detecting and Characterizing Ponzi Schemes in Ethereum Smart Contracts

Proceedings of the ACM on Measurement and Analysis of Computing Systems ◽

10.1145/3460093 ◽

2021 ◽

Vol 5 (2) ◽

pp. 1-30

Author(s):

Weimin Chen ◽

Xinran Li ◽

Yuting Sui ◽

Ningyu He ◽

Haoyu Wang ◽

...

Keyword(s):

Machine Learning ◽

Semantic Information ◽

Symbolic Execution ◽

Experimental Result ◽

Smart Contracts ◽

Smart Contract ◽

Detection Approach ◽

Ponzi Scheme ◽

Feasible Path ◽

Definition Of

Ponzi schemes are financial scams that lure users under the promise of high profits. With the prosperity of Bitcoin and blockchain technologies, there has been growing anecdotal evidence that this classic fraud has emerged in the blockchain ecosystem. Existing studies have proposed machine-learning based approaches for detecting Ponzi schemes, i.e., either based on the operation codes (opcodes) of the smart contract binaries or the transaction patterns of addresses. However, state-of-the-art approaches face several major limitations, including lacking interpretability and high false positive rates. Moreover, machine-learning based methods are susceptible to evasion techniques, and transaction-based techniques do not work on smart contracts that have a small number of transactions. These limitations render existing methods for detecting Ponzi schemes ineffective. In this paper, we propose SADPonzi, a semantic-aware detection approach for identifying Ponzi schemes in Ethereum smart contracts. Specifically, by strictly following the definition of Ponzi schemes, we propose a heuristic-guided symbolic execution technique to first generate the semantic information for each feasible path in smart contracts and then identify investor-related transfer behaviors and the distribution strategies adopted. Experimental result on a well-labelled benchmark suggests that SADPonzi can achieve 100% precision and recall, outperforming all existing machine-learning based techniques. We further apply SADPonzi to all 3.4 million smart contracts deployed by EOAs in Ethereum and identify 835 Ponzi scheme contracts, with over 17 million US Dollars invested by victims. Our observations confirm the urgency of identifying and mitigating Ponzi schemes in the blockchain ecosystem.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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