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Query-Efficient Black-Box Attack Against Sequence-Based Malware Classifiers
- Source :
- ACSAC
- Publication Year :
- 2020
- Publisher :
- ACM, 2020.
-
Abstract
- In this paper, we present a generic, query-efficient black-box attack against API call-based machine learning malware classifiers. We generate adversarial examples by modifying the malware's API call sequences and non-sequential features (printable strings), and these adversarial examples will be misclassified by the target malware classifier without affecting the malware's functionality. In contrast to previous studies, our attack minimizes the number of malware classifier queries required. In addition, in our attack, the attacker must only know the class predicted by the malware classifier; attacker knowledge of the malware classifier's confidence score is optional. We evaluate the attack effectiveness when attacks are performed against a variety of malware classifier architectures, including recurrent neural network (RNN) variants, deep neural networks, support vector machines, and gradient boosted decision trees. Our attack success rate is around 98% when the classifier's confidence score is known and 64% when just the classifier's predicted class is known. We implement four state-of-the-art query-efficient attacks and show that our attack requires fewer queries and less knowledge about the attacked model's architecture than other existing query-efficient attacks, making it practical for attacking cloud-based malware classifiers at a minimal cost.<br />Accepted as a conference paper at ACSAC 2020
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Computer Science - Cryptography and Security
Computer science
0211 other engineering and technologies
Cloud computing
02 engineering and technology
computer.software_genre
Machine learning
Machine Learning (cs.LG)
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Black box (phreaking)
021110 strategic, defence & security studies
Class (computer programming)
business.industry
Support vector machine
ComputingMethodologies_PATTERNRECOGNITION
Recurrent neural network
Malware
Alternating decision tree
Artificial intelligence
business
Cryptography and Security (cs.CR)
computer
Classifier (UML)
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Annual Computer Security Applications Conference
- Accession number :
- edsair.doi.dedup.....973804ca464067ba941c782ceaf7ee74