Academic Journal

Advanced Adaptive Techniques for Securing Applications Against Dynamic Reverse Engineering Attacks.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Advanced Adaptive Techniques for Securing Applications Against Dynamic Reverse Engineering Attacks.
Συγγραφείς: Sabri, Khair Eddin
Πηγή: Engineering, Technology & Applied Science Research; Jun2026, Vol. 16 Issue 3, p35999-36005, 7p
Θεματικοί όροι: Reverse engineering, Security management, Computer performance
Περίληψη: Dynamic reverse engineering analyzes a program during execution to understand its behavior and modify its logic to bypass authentication mechanisms or extract sensitive information, with such analysis commonly relying on debugging and runtime instrumentation tools. However, many protection techniques usually use a single anti-debugging or anti-instrumentation mechanism, which can be easily bypassed once attackers identify the detection method. To combat this limitation, this paper proposes an adaptive framework for Windows that detects dynamic program analysis by combining multiple indicators. Specifically, each indicator is assigned a weight that reflects its reliability in identifying active analysis, while a confidence score is calculated from these weights to classify the risk level as low, medium, or high. The program then dynamically adapts its behavior according to the classified risk level. The framework incorporates additional indicators to detect modern instrumentation tools, such as Frida, which can often bypass traditional anti-debugging techniques. The proposed framework was implemented in C and evaluated under multiple scenarios, with experimental results showing that the framework effectively detects dynamic analysis, while performance evaluation indicates low runtime overhead. [ABSTRACT FROM AUTHOR]
Copyright of Engineering, Technology & Applied Science Research is the property of Engineering, Technology & Applied Science Research and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
Περιγραφή
ISSN:22414487
DOI:10.48084/etasr.18160