  In big data era, machine learning is one of fundamental techniques in
intrusion detection systems (IDSs). However, practical IDSs generally update
their decision module by feeding new data then retraining learning models in a
periodical way. Hence, some attacks that comprise the data for training or
testing classifiers significantly challenge the detecting capability of machine
learning-based IDSs. Poisoning attack, which is one of the most recognized
security threats towards machine learning-based IDSs, injects some adversarial
samples into the training phase, inducing data drifting of training data and a
significant performance decrease of target IDSs over testing data. In this
paper, we adopt the Edge Pattern Detection (EPD) algorithm to design a novel
poisoning method that attack against several machine learning algorithms used
in IDSs. Specifically, we propose a boundary pattern detection algorithm to
efficiently generate the points that are near to abnormal data but considered
to be normal ones by current classifiers. Then, we introduce a Batch-EPD
Boundary Pattern (BEBP) detection algorithm to overcome the limitation of the
number of edge pattern points generated by EPD and to obtain more useful
adversarial samples. Based on BEBP, we further present a moderate but effective
poisoning method called chronic poisoning attack. Extensive experiments on
synthetic and three real network data sets demonstrate the performance of the
proposed poisoning method against several well-known machine learning
algorithms and a practical intrusion detection method named FMIFS-LSSVM-IDS.
