Is Feature Selection Secure against Training Data Poisoning?

Learning in adversarial settings is becoming animportant task for application domains where at-tackers may inject malicious data into the train-ing set to subvert normal operation of data-driventechnologies. Feature selection has been widelyused in machine learning for security applica-tions to improve generalization and computa-tional efficiency, although it is not clear whetherits use may be beneficial or even counterproduc-tive when training data are poisoned by intelli-gent attackers. In this work, we shed light onthis issue by providing a framework to investi-gate the robustness of popular feature selectionmethods, including LASSO, ridge regression andthe elastic net. Our results on malware detec-tion show that feature selection methods can besignificantly compromised under attack (we canreduce LASSO to almost random choices of fea-ture sets by careful insertion of less than 5% poi-soned training samples), highlighting the needfor specific countermeasures.