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The performance of a machine learning-based malware classifier depends on thelarge and updated training set used to induce its model. In order to maintainan up-to-date training set, there is a need to continuously collect benign andmalicious files from a wide range of sources, providing an exploitable targetto attackers. In this study, we show how an attacker can launch a sophisticatedand efficient poisoning attack targeting the dataset used to train a malwareclassifier. The attacker's ultimate goal is to ensure that the model induced bythe poisoned dataset will be unable to detect the attacker's malware yetcapable of detecting other malware. As opposed to other poisoning attacks inthe malware detection domain, our attack does not focus on malware families butrather on specific malware instances that contain an implanted trigger,reducing the detection rate from 99.23% to 0% depending on the amount ofpoisoning. We evaluate our attack on the EMBER dataset with a state-of-the-artclassifier and malware samples from VirusTotal for end-to-end validation of ourwork. We propose a comprehensive detection approach that could serve as afuture sophisticated defense against this newly discovered severe threat.

Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers