Argumentation-Based Adversarial Regression with Multiple Learners

Despite the extensive benefits of machine learning techniques in practice, several studies demonstrated that many approaches are vulnerable to attacks. These attacks generate adversarial data to manipulate learning models that result ambiguous decisions. In this paper, we propose a hybrid-reasoning framework that combines data-driven and non-monotonic reasoning, specifically formal argumentation and adversarial regression with multiple learners, to deal with ambiguous predictions of predictive models. The introduced hybrid-reasoning framework ensures three significant benefits. It (i) provides an argumentation-based aggregation function for combining multiple learners, (ii) reduces the effort to resolve conflicts in predictions, and (iii) cost-effective and robust training in adversarial regression solutions. To illustrate the introduced framework, we consider a benchmark of resource traces obtained from Yahoo's service cluster for anomaly detection under adversarial settings. The experimental analysis shows 3% more accuracy in prediction under argumentation-based adversarial settings.