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Jia et al., 2023 - Google Patents

ERMDS: A obfuscation dataset for evaluating robustness of learning-based malware detection system

Jia et al., 2023

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Document ID
13761019740347016426
Author
Jia L
Yang Y
Tang B
Jiang Z
Publication year
Publication venue
BenchCouncil Transactions on Benchmarks, Standards and Evaluations

External Links

Snippet

Learning-based malware detection systems (LB-MDS) play a crucial role in defending computer systems from malicious attacks. Nevertheless, these systems can be vulnerable to various attacks, which can have significant consequences. Software obfuscation techniques …
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Classifications

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    • G06F21/55—Detecting local intrusion or implementing counter-measures
    • G06F21/56—Computer malware detection or handling, e.g. anti-virus arrangements
    • G06F21/562—Static detection
    • G06F21/563—Static detection by source code analysis
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    • G06F21/52—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity ; Preventing unwanted data erasure; Buffer overflow
    • G06F21/54—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity ; Preventing unwanted data erasure; Buffer overflow by adding security routines or objects to programs
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