Stealing and evading malware classifiers and antivirus at low false positive conditions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00365936" target="_blank" >RIV/68407700:21230/23:00365936 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.cose.2023.103192" target="_blank" >https://doi.org/10.1016/j.cose.2023.103192</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cose.2023.103192" target="_blank" >10.1016/j.cose.2023.103192</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Stealing and evading malware classifiers and antivirus at low false positive conditions

Popis výsledku v původním jazyce

Model stealing attacks have been successfully used in many machine learning domains, but there is little understanding of how these attacks work against models that perform malware detection. Malware detection and, in general, security domains have unique conditions. In particular, there are very strong requirements for low false positive rates (FPR). Antivirus products (AVs) that use machine learning are very complex systems to steal, malware binaries continually change, and the whole environment is adversarial by nature. This study evaluates active learning model stealing attacks against publicly available stand-alone machine learning malware classifiers and also against antivirus products. The study proposes a new neural network architecture for surrogate models (dualFFNN) and a new model stealing attack that combines transfer and active learning for surrogate creation (FFNN-TL). We achieved good surrogates of the stand-alone classifiers with up to 99% agreement with the target models, using less than 4% of the original training dataset. Good surrogates of AV systems were also trained with up to 99% agreement and less than 4000 queries. The study uses the best surrogates to generate adversarial malware to evade the target models, both stand-alone and AVs (with and without an internet connection). Results show that surrogate models can generate adversarial malware that evades the targets but with a lower success rate than directly using the target models to generate adversarial malware. Using surrogates, however, is still a good option since using the AVs for malware generation is highly time-consuming and easily detected when the AVs are connected to the internet

Název v anglickém jazyce

Stealing and evading malware classifiers and antivirus at low false positive conditions

Popis výsledku anglicky

Model stealing attacks have been successfully used in many machine learning domains, but there is little understanding of how these attacks work against models that perform malware detection. Malware detection and, in general, security domains have unique conditions. In particular, there are very strong requirements for low false positive rates (FPR). Antivirus products (AVs) that use machine learning are very complex systems to steal, malware binaries continually change, and the whole environment is adversarial by nature. This study evaluates active learning model stealing attacks against publicly available stand-alone machine learning malware classifiers and also against antivirus products. The study proposes a new neural network architecture for surrogate models (dualFFNN) and a new model stealing attack that combines transfer and active learning for surrogate creation (FFNN-TL). We achieved good surrogates of the stand-alone classifiers with up to 99% agreement with the target models, using less than 4% of the original training dataset. Good surrogates of AV systems were also trained with up to 99% agreement and less than 4000 queries. The study uses the best surrogates to generate adversarial malware to evade the target models, both stand-alone and AVs (with and without an internet connection). Results show that surrogate models can generate adversarial malware that evades the targets but with a lower success rate than directly using the target models to generate adversarial malware. Using surrogates, however, is still a good option since using the AVs for malware generation is highly time-consuming and easily detected when the AVs are connected to the internet

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computers & Security

ISSN

0167-4048

e-ISSN

1872-6208

Svazek periodika

2023

Číslo periodika v rámci svazku

129

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

17

Strana od-do

1-17

Kód UT WoS článku

001042634700001

EID výsledku v databázi Scopus

2-s2.0-85150897082