Evade Hard Multiple Classifier Systems

Classifier Ensembles for Detecting Concept Change in Streaming

Multiple classifier systems are widely used in securityapplications like biometric personal authentication, spam filtering,and intrusion detection in computer networks. Several works exper-imentally showed their effectiveness in these tasks. However, theiruse in such applications is motivated only by intuitive and qualitativearguments. In this work we give a first possible f...

Workshop on Supervised and Unsupervised Ensemble Methods and their Applications

Multiple classifier systems are widely used in securityapplications like biometric personal authentication, spam filtering,and intrusion detection in computer networks. Several works exper-imentally showed their effectiveness in these tasks. However, theiruse in such applications is motivated only by intuitive and qualitativearguments. In this work we give a first possible f...

Adversarial Pattern Classification Using Multiple Classifiers and Randomisation

In many security applications a pattern recognition system faces an adversarial classification problem, in which an intelligent, adaptive adversary modifies patterns to evade the classifier. Several strategies have been recently proposed to make a classifier harder to evade, but they are based only on qualitative and intuitive arguments. In this work, we consider a strategy consisting in hiding...

One-and-a-Half-Class Multiple Classifier Systems for Secure Learning Against Evasion Attacks at Test Time

Pattern classifiers have been widely used in adversarial settings like spam and malware detection, although they have not been originally designed to cope with intelligent attackers that manipulate data at test time to evade detection. While a number of adversary-aware learning algorithms have been proposed, they are computationally demanding and aim to counter specific kinds of adversarial dat...

Highlighting Hard Patterns via AdaBoost Weights Evolution