Towards adversarial malware detection: lessons learned from PDF-based attacks

Maiorca D.
Primo
;
Biggio B.
Secondo
;
Giacinto G.
Ultimo
2019

Abstract

Malware still constitutes a major threat in the cybersecurity landscape, also due to the widespread use of infection vectors such as documents. These infection vectors hide embedded malicious code to the victim users, facilitating the use of social engineering techniques to infect their machines. Research showed that machine-learning algorithms provide effective detection mechanisms against such threats, but the existence of an arms race in adversarial settings has recently challenged such systems. In this work, we focus on malware embedded in PDF files as a representative case of such an arms race. We start by providing a comprehensive taxonomy of the different approaches used to generate PDF malware and of the corresponding learning-based detection systems. We then categorize threats specifically targeted against learning-based PDF malware detectors using a well-established framework in the field of adversarial machine learning. This framework allows us to categorize known vulnerabilities of learning-based PDF malware detectors and to identify novel attacks that may threaten such systems, along with the potential defense mechanisms that can mitigate the impact of such threats. We conclude the article by discussing how such findings highlight promising research directions towards tackling the more general challenge of designing robust malware detectors in adversarial settings.
Inglese
52
4
1
36
36
http://dl.acm.org/citation.cfm?id=J204
https://dl.acm.org/citation.cfm?doid=3359984.3332184
Esperti anonimi
internazionale
scientifica
Evasion attacks; Infection vectors; Javascript; Machine learning; PDF files; Vulnerabilities
no
Maiorca, D.; Biggio, B.; Giacinto, G.
1.1 Articolo in rivista
info:eu-repo/semantics/article
1 Contributo su Rivista::1.1 Articolo in rivista
262
3
partially_open
File in questo prodotto:
File Dimensione Formato  
a78-maiorca.pdf

Solo gestori archivio

Descrizione: articolo
Tipologia: versione editoriale
Dimensione 1.27 MB
Formato Adobe PDF
1.27 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
CSUR_IRIS.pdf

accesso aperto

Tipologia: versione post-print
Dimensione 952.96 kB
Formato Adobe PDF
952.96 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Questionario e social

Condividi su:
Impostazioni cookie