English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recently, there has been a growing focus and interest in applying machinelearning (ML) to the field of cybersecurity, particularly in malware detectionand prevention. Several research works on malware analysis have been proposed,offering promising results for both academic and practical applications. Inthese works, the use of Generative Adversarial Networks (GANs) or ReinforcementLearning (RL) can aid malware creators in crafting metamorphic malware thatevades antivirus software. In this study, we propose a mutation system tocounteract ensemble learning-based detectors by combining GANs and an RL model,overcoming the limitations of the MalGAN model. Our proposed FeaGAN model isbuilt based on MalGAN by incorporating an RL model called the Deep Q-networkanti-malware Engines Attacking Framework (DQEAF). The RL model addresses threekey challenges in performing adversarial attacks on Windows Portable Executablemalware, including format preservation, executability preservation, andmaliciousness preservation. In the FeaGAN model, ensemble learning is utilizedto enhance the malware detector's evasion ability, with the generatedadversarial patterns. The experimental results demonstrate that 100\% of theselected mutant samples preserve the format of executable files, while certainsuccesses in both executability preservation and maliciousness preservation areachieved, reaching a stable success rate.

On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors