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JCR Impact Factor: 0.700
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

2023-Jun-28
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2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  2/2019 - 3

 HIGH-IMPACT PAPER 

Automatic Detection and Bypassing of Anti-Debugging Techniques for Microsoft Windows Environments

PARK, J. See more information about PARK, J. on SCOPUS See more information about PARK, J. on IEEExplore See more information about PARK, J. on Web of Science, JANG, Y.-H. See more information about  JANG, Y.-H. on SCOPUS See more information about  JANG, Y.-H. on SCOPUS See more information about JANG, Y.-H. on Web of Science, HONG, S. See more information about  HONG, S. on SCOPUS See more information about  HONG, S. on SCOPUS See more information about HONG, S. on Web of Science, PARK, Y. See more information about PARK, Y. on SCOPUS See more information about PARK, Y. on SCOPUS See more information about PARK, Y. on Web of Science
 
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Download PDF pdficon (1,313 KB) | Citation | Downloads: 2,967 | Views: 3,428

Author keywords
computer hacking, computer security, debugging, reverse engineering, software protection

References keywords
anti(10), link(9), software(7), malware(7), debugging(7), security(6), binary(5), analysis(5), ollydbg(4), code(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2019-05-31
Volume 19, Issue 2, Year 2019, On page(s): 23 - 28
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.02003
Web of Science Accession Number: 000475806300003
SCOPUS ID: 85066320679

Abstract
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In spite of recent remarkable advances in binary code analysis, adversaries are still using diverse anti-reversing techniques for obfuscating code and making analysis difficult. Unlike most of the previous work that relies on debugger-plugins for neutralizing anti-debugging techniques, we focus on the Pin, which is one of the most widely used DBI (Dynamic Binary Instrumentation) tools in 80x86 environments. In this paper, we present an automatic anti-debugging detection/bypassing scheme using the Pin. In order to evaluate the effectiveness of our algorithm, we conducted experiments on 17 most widely used (commercial) protectors, which results in bypassing all anti-debugging techniques automatically. Particularly, our experiment includes Safengine, which is one of the most complex commercial protectors and, to the best of our knowledge, it has not been successfully analyzed by academic researchers up to now. Also, experimental results show that the proposed scheme performs better than the most recent work, Apate.


References | Cited By

Cited-By Clarivate Web of Science

Web of Science® Times Cited: 9 [View]
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Cited-By SCOPUS

SCOPUS® Times Cited: 9
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Cited-By CrossRef

[1] Bypassing Heaven’s Gate Technique Using Black-Box Testing, Hwang, Seon-Jin, Utaliyeva, Assem, Kim, Jae-Seok, Choi, Yoon-Ho, Sensors, ISSN 1424-8220, Issue 23, Volume 23, 2023.
Digital Object Identifier: 10.3390/s23239417
[CrossRef]

[2] Detection Techniques for DBI Environment in Windows, Park, Seongwoo, Park, Yongsu, Electronics, ISSN 2079-9292, Issue 5, Volume 13, 2024.
Digital Object Identifier: 10.3390/electronics13050871
[CrossRef]

[3] A practical approach for finding anti-debugging routines in the Arm-Linux using hardware tracing, Park, Yeongung, Choi, Seokwoo, Choi, Un Yeong, Jin, Haimin, Nor, Nurul Harzira Mohamad, Park, Yongsu, Scientific Reports, ISSN 2045-2322, Issue 1, Volume 14, 2024.
Digital Object Identifier: 10.1038/s41598-024-65374-w
[CrossRef]

[4] Bypassing Anti-Analysis of Commercial Protector Methods Using DBI Tools, Lee, Young Bi, Suk, Jae Hyuk, Lee, Dong Hoon, IEEE Access, ISSN 2169-3536, Issue , 2021.
Digital Object Identifier: 10.1109/ACCESS.2020.3048848
[CrossRef]

[5] Pinicorn: Towards Automated Dynamic Analysis for Unpacking 32-Bit PE Malware, Lee, Gwangyeol, Kim, Minho, Yi, Jeong Hyun, Cho, Haehyun, Electronics, ISSN 2079-9292, Issue 11, Volume 13, 2024.
Digital Object Identifier: 10.3390/electronics13112081
[CrossRef]

[6] DBI, debuggers, VM: gotta catch them all, Plumerault, François, David, Baptiste, Journal of Computer Virology and Hacking Techniques, ISSN 2263-8733, Issue 2, Volume 17, 2021.
Digital Object Identifier: 10.1007/s11416-020-00371-x
[CrossRef]

[7] Malware Detection with Artificial Intelligence: A Systematic Literature Review, Gaber, Matthew G., Ahmed, Mohiuddin, Janicke, Helge, ACM Computing Surveys, ISSN 0360-0300, Issue 6, Volume 56, 2024.
Digital Object Identifier: 10.1145/3638552
[CrossRef]

[8] Large-Scale Analysis on Anti-Analysis Techniques in Real-World Malware, Kim, Minho, Cho, Haehyun, Yi, Jeong Hyun, IEEE Access, ISSN 2169-3536, Issue , 2022.
Digital Object Identifier: 10.1109/ACCESS.2022.3190978
[CrossRef]

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