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In big data era, machine learning is one of fundamental techniques inintrusion detection systems (IDSs). However, practical IDSs generally updatetheir decision module by feeding new data then retraining learning models in aperiodical way. Hence, some attacks that comprise the data for training ortesting classifiers significantly challenge the detecting capability of machinelearning-based IDSs. Poisoning attack, which is one of the most recognizedsecurity threats towards machine learning-based IDSs, injects some adversarialsamples into the training phase, inducing data drifting of training data and asignificant performance decrease of target IDSs over testing data. In thispaper, we adopt the Edge Pattern Detection (EPD) algorithm to design a novelpoisoning method that attack against several machine learning algorithms usedin IDSs. Specifically, we propose a boundary pattern detection algorithm toefficiently generate the points that are near to abnormal data but consideredto be normal ones by current classifiers. Then, we introduce a Batch-EPDBoundary Pattern (BEBP) detection algorithm to overcome the limitation of thenumber of edge pattern points generated by EPD and to obtain more usefuladversarial samples. Based on BEBP, we further present a moderate but effectivepoisoning method called chronic poisoning attack. Extensive experiments onsynthetic and three real network data sets demonstrate the performance of theproposed poisoning method against several well-known machine learningalgorithms and a practical intrusion detection method named FMIFS-LSSVM-IDS.

BEBP: An Poisoning Method Against Machine Learning Based IDSs