While machine learning (ML) models are being increasingly trusted to make decisions in different and varying areas, the safety of systems using such models has become an increasing concern. In particular, ML models are often trained on data from potentially untrustworthy sources, providing adversaries with the opportunity to manipulate them by inserting carefully crafted samples into the training set. Recent work has shown that this type of attack, called a poisoning attack, allows adversaries to insert backdoors or trojans into the model, enabling malicious behavior with simple external backdoor triggers at inference time and only a blackbox perspective of the model itself. Detecting this type of attack is challenging because the unexpected behavior occurs only when a backdoor trigger, which is known only to the adversary, is present. Model users, either direct users of training data or users of pre-trained model from a catalog, may not guarantee the safe operation of their ML-based system. In this paper, we propose a novel approach to backdoor detection and removal for neural networks. Through extensive experimental results, we demonstrate its effectiveness for neural networks classifying text and images. To the best of our knowledge, this is the first methodology capable of detecting poisonous data crafted to insert backdoors and repairing the model that does not require a verified and trusted dataset.

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The demand for quickly delivering new applications is increasingly becoming a business imperative today. Application development is often done in an ad hoc manner, without standard frameworks or libraries, thus resulting in poor reuse of software assets. Web services have received much interest in industry due to their potential in facilitating seamless business-to-business or enterprise application integration. A web services composition tool can help automate the process, from creating business process functionality, to developing executable workflows, to deploying them on an execution environment. However, we find that the main approaches taken thus far to standardize and compose web services are piecemeal and insufficient. The business world has adopted a (distributed) programming approach in which web service instances are described using WSDL, composed into flows with a language like BPEL and invoked with the SOAP protocol. Academia has propounded the AI approach of formally representing web service capabilities in ontologies, and reasoning about their composition using goal-oriented inferencing techniques from planning. We present the first integrated work in composing web services end to end from specification to deployment by synergistically combining the strengths of the above approaches. We describe a prototype service creation environment along with a use-case scenario, and demonstrate how it can significantly speed up the time-tomarket for new services.

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