Title: Handling Adversarial Concept Drift in Streaming Data,
Abstract: Classifiers operating in a dynamic, real world environment, are vulnerable to
adversarial activity, which causes the data distribution to change over time.
These changes are traditionally referred to as concept drift, and several
approaches have been developed in literature to deal with the problem of drift
handling and detection. However, most concept drift handling techniques,
approach it as a domain independent task, to make them applicable to a wide
gamut of reactive systems. These techniques were developed from an adversarial
agnostic perspective, where they are naive and assume that drift is a benign
change, which can be fixed by updating the model. However, this is not the case
when an active adversary is trying to evade the deployed classification system.
In such an environment, the properties of concept drift are unique, as the
drift is intended to degrade the system and at the same time designed to avoid
detection by traditional concept drift detection techniques. This special
category of drift is termed as adversarial drift, and this paper analyzes its
characteristics and impact, in a streaming environment. A novel framework for
dealing with adversarial concept drift is proposed, called the Predict-Detect
streaming framework. Experimental evaluation of the framework, on generated
adversarial drifting data streams, demonstrates that this framework is able to
provide reliable unsupervised indication of drift, and is able to recover from
drifts swiftly. While traditional partially labeled concept drift detection
methodologies fail to detect adversarial drifts, the proposed framework is able
to detect such drifts and operates with <6% labeled data, on average. Also, the
framework provides benefits for active learning over imbalanced data streams,
by innately providing for feature space honeypots, where minority class
adversarial samples may be captured.