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OS-in-the-Loop Concolic Testing for Multitask Embedded Software
http://doi.org/10.5626/JOK.2023.50.2.140
Verification of multitask embedded software still depends on manual effort because of its hardware-dependent structure and complicated multitasking works. We present the OSin-the-loop(OiL) concolic test, the new approach for automatic and efficient verification of multitask embedded software. Given the OS model, hardware stubs that replace platform-dependent code, and annotated application code with explicit context switch control logic, it provides a hardwareindependent environment and automatically checks properties with the concolic test. In the application of our approach on a representative multitask embedded software, Object-Follower and concolic testing tool CROWN 2.0, when there is an OS model, our method achieves fewer false alarms from 91.67% to 5.13% than without OS.
Smart Contract Weakness Analyzer Based on Concolic Testing
http://doi.org/10.5626/JOK.2021.48.6.668
Ethereum is a blockchain-based cryptocurrency platform that provides a Turing complete language, Solidity, which can be used to develop smart contracts for various applications. This paper present an analyzer that finds security weaknesses in smart contracts using the concolic testing framework. Concolic testing, which combines symbolic execution and testing, is more efficient than symbolic execution while retaining no false positiveness which is absent in static analysis. Also, the analyzer reflects actual execution context to the maximum extent possible using the Ethereum execution environment, the Geth testnet. The analyzer detects integer overflow and unhandled exception weakness. Also, this paper presents performance test results in comparison with a well known smart contract symbolic execution framework, Manticore.
Relation Extraction based on Neural-Symbolic Structure
http://doi.org/10.5626/JOK.2021.48.5.533
Deep learning has been continually demonstrating excellent performance in the field of natural language processing. However, enormous training data and long training time are required to achieve good performance. Herein, we propose a method that exceeds deep learning performance in a small learning data environment by using a neural-symbolic method for the relationship extraction problem. We have designed a structure that uses the inconsistency between the rule results and deep learning results. In addition, logical rule filtering has been proposed to improve the convergence speed and a context has been added to improve the performance of the rule. The proposed method showed excellent performance for a small amount of training data, and we confirmed that fast performance convergence was achieved.
A Knowledge Completion Approach using Rule Generation based on Neuro-Symbolic Method
Jea-Seung Roh, Won-Chul Shin, Hyun-Kyu Park, Young-Tack Park
http://doi.org/10.5626/JOK.2021.48.4.425
A knowledge graph is a structured representation of real-world knowledge and is designed by collecting information from various sources. These knowledge graphs are networks that represent relationships between data and are applied in various fields of artificial intelligence; however, there exists problems related to incomplete knowledge due to the omission of entities or omission links between the entities. To resolve the problems, research on automatic knowledge completion techniques is necessitated. Consequently, various studies have been examined including embedding techniques, deep learning or symbolic rule inference using ontology. Although automatic knowledge completion can be efficiently performed through the above-mentioned methods, deep learning methods require a large amount of learning data due to data-driven processing methods, and there exist problems related to the results that are hard to explain. Futhermore, ontology-based methods require ontology and rules that are defined by the experts. To overcome this limitation, in this study, we propose an automatic knowledge completion method by explicitly extracting the implicit rules from the data based on the Neuro-Symbolic method. For rule extraction, we have implemented a symbolic unification based embedding path and defined a cost function for it to automatically generate the rules. Compared with the approaches presented in previous embedding studies, the proposed method demonstrates the superiority of the Neuro-Symbolic method concerning speed and performance. To assess the performance of the proposed method, for datasets like Nations, UMLS, and Kinship, experiments were conducted in comparison with the approach of the state-of-the-art knowledge completion studies. Consequently, an immense reduction in the training time and 37.5%p increase in the average performance were observed.
Bounded Search Strategies of Concolic Testing for Effective and Efficient Structural Coverage Achievement
http://doi.org/10.5626/JOK.2021.48.2.201
This paper proposes a loop-bounded search strategy for effective and efficient coverage achievement in concolic testing. In selecting a new path to explore, a loop-bounded search strategy limits the number of iterations in a loop to a certain loop-bound, so that the concolic testing is guided to explore various program behaviors within a limited range. In addition, to extend the range of path exploration gradually, this search strategy increments the loop-bound over test executions based on their coverage achievement rates. We implemented three versions of loop-bounded search strategies based on three existing concolic search strategies of CREST. The experiments with 4 real-world target programs (Vim, Grep, Busybox Awk, and Busybox Sed) showed that CREST achieves a higher branch coverage more quickly when the loop-bounded search strategies are applied.
Automatic Test Case Generation through Concolic Testing to Improve SW Quality of Defense Weapon System
Kunwoo Park, Joohyun Lee, Hyunggon Song, Kyu Tae Cho, Yunho Kim, Moonzoo Kim
http://doi.org/10.5626/JOK.2019.46.9.926
To improve SW quality of defense weapon system, automatic and systematic generation of test cases is necessary; however, that is not the case in the traditional practice of labor-intensive and manual SW testing. The paper applies concolic testing to the defense weapon system SW, effectively generates test cases that achieve high coverage, and discovers defects which contributes to the improvement in SW quality. Also, two methods are proposed using 4 search strategies in concolic testing and using LIA logic, to increase the efficiency of concolic testing for a program with high complexity. In addition, a symbolic modeling method is proposed as an example to extend concolic testing for practitioners.
A Study on Two-dimensional Array-based Technology to Identify Obfuscatied Malware
Seonbin Hwang, Hogyeong Kim, Junho Hwang, Taejin Lee
http://doi.org/10.5626/JOK.2018.45.8.769
More than 1.6 milion types of malware are emerging on average per day, and most cyber attackes are generated by malware. Moreover, malware obfuscation techniques are becoming more intelligent through packing or encryption to prevent reverse engineering analysis. In the case of static analysis, there is a limit to the analysis when the analytical file becomes obfuscated, and a countermeasure is needed. In this paper, we propose an approach based on String, Symbol, and Entropy as a way to identify malware even during obfuscation. Two-dimensional arrays were applied for fixed feature-set processing as well as non-fixed feature-set processing, and 15,000 malware/benign samples were tested using the Deep Neural Network. This study is expected to operate in a complementary manner in conjunction with various malicious code detection methods in the future, and it is expected that it can be utilized in the analysis of obfuscated malware variants.
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