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Detailed Contact Information (corresponding author): Abdullah Al-Dujaili, [email protected] Open Questions & Future Vision Machine Learning and the Malware Arms Race Current Progress Project Overview Abdullah Al - Dujaili, Alex Huang, Erik Hemberg , Una - May O’Reilly Relevant Papers 1. Al-Dujaili, et al. "Adversarial Deep Learning for Robust Detection of Binary Encoded Malware." IEEE SPW, 2018. 2. Huang et al. "On Visual Hallmarks of Robustness to Adversarial Malware." IJCAI- IREDLIA, 2018.. #AI Research Week hosted by MIT-IBM Watson AI Lab • Malware detectors have been improved and automated with the help of machine learning (ML). • Unfortunately adversaries aiming to thwart them are also aided by ML (see Fig. 1). • Our project explores the malware arms race of artificially intelligent competitors for ways detectors can gain the upper hand. file_1.exe (PE) Fig. 1. An appropriate feature vector of a PE can be built based on the PE’s imported functions. A malware author can import additional functions without affecting the malicious functionality giving rise to a set of adversarial versions of the same PE. These adversarial versions can be found with the help of an ML algorithm. • Static Analysis vs. Dynamic Analysis • Representation Learning: Abstract Syntax Trees (ASTs) • Interpreted languages: PowerShell Scripts Fig. 3. In contrast to standard generalization, the decision landscape (input space) has a stronger association with the hardened model’s robust generalization, compared to the geometry of the loss landscape (parameter space). Fig. 2. SLEIPNIR: a saddle-point formulation for hardening ML models with binary feature space. Fig. 4. AST-based deep learning for malicious PowerShell detection. Table I. Performance Metrics Table II. Evasion Rates Courtesy of [Madry et al. , 2017]

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Detailed Contact Information(corresponding author): Abdullah Al-Dujaili, [email protected]

Open Questions & Future Vision

Machine Learning and the Malware Arms Race

Current Progress

Project Overview

Abdullah Al-Dujaili, Alex Huang, Erik Hemberg, Una-May O’Reilly

Relevant Papers1. Al-Dujaili, et al. "Adversarial Deep Learning for Robust Detection of Binary Encoded Malware." IEEE SPW, 2018.2. Huang et al. "On Visual Hallmarks of Robustness to Adversarial Malware." IJCAI-IREDLIA, 2018..

#AI Research Weekhosted by MIT-IBM Watson AI Lab

• Malware detectors have been improved and automated with the help of machine learning (ML).

• Unfortunately adversaries aiming to thwart them are also aided by ML (see Fig. 1).

• Our project explores the malware arms race of artificially intelligent competitors for ways detectors can gain the upper hand.

file_1.exe (PE)

Fig. 1. An appropriate feature vector of a PE can be builtbased on the PE’s imported functions. A malware authorcan import additional functions without affecting themalicious functionality giving rise to a set of adversarialversions of the same PE. These adversarial versions can befound with the help of an ML algorithm.

• Static Analysis vs. Dynamic Analysis• Representation Learning: Abstract

Syntax Trees (ASTs)• Interpreted languages: PowerShell

Scripts

Fig. 3. In contrast to standard generalization, the decisionlandscape (input space) has a stronger association withthe hardened model’s robust generalization, compared tothe geometry of the loss landscape (parameter space).

Fig. 2. SLEIPNIR: a saddle-pointformulation for hardening ML modelswith binary feature space.

Fig. 4. AST-based deep learning for malicious PowerShell detection.

Table I. Performance Metrics

Table II. Evasion Rates

Courtesy of [Madry et al. , 2017]