BEBP: An Poisoning Method Against Machine Learning Based IDSs

Understand

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.