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

Machine-learning methods have already been exploited as useful tools fordetecting malicious executable files. They leverage data retrieved from malwaresamples, such as header fields, instruction sequences, or even raw bytes, tolearn models that discriminate between benign and malicious software. However,it has also been shown that machine learning and deep neural networks can befooled by evasion attacks (also referred to as adversarial examples), i.e.,small changes to the input data that cause misclassification at test time. Inthis work, we investigate the vulnerability of malware detection methods thatuse deep networks to learn from raw bytes. We propose a gradient-based attackthat is capable of evading a recently-proposed deep network suited to thispurpose by only changing few specific bytes at the end of each malware sample,while preserving its intrusive functionality. Promising results show that ouradversarial malware binaries evade the targeted network with high probability,even though less than 1% of their bytes are modified.

Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables