Analysis on Security Evaluation of Pattern Classifiers under Attack
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 2
Abstract
Analysis on security evaluation of pattern classifiers under attack describes pattern classification systems that are security evaluation problems because of different attacks. Pattern Classification commonly used in adversarial applications, like biometric authentication, network intrusion detection, and spam filtering. In these applications data can be purposely manipulated by humans to undermine their operation. This adversarial scenario’s exploitation may sometimes affect their performance, systems may exhibit vulnerabilities and limit their practical utility. This adversarial scenario is not taken into account by classical design methods. These Applications have an intrinsic adversarial nature since the input data can be purposely manipulated by an intelligent and adaptive adversary to undermine classifier operation. This often gives rise to an arms race between the adversary and the classifier designer. The system evaluates at design phase the security of pattern classifiers, namely, the performance degradation under potential attacks they may incur during operation. A generalize framework is used for evaluation of classifier security that formalizes and generalizes the training and testing datasets, to discriminate between a “legitimate” and a “malicious” pattern class Training and Testing sets have been obtained from distribution using a classical reassembling technique like bootstrapping or cross validation. Security evaluation can be carried out by averaging the performance of the trained and tested data.
Authors and Affiliations
Rupali B. Navalkar, Prof. Rajeshri R. Shelke
Keywords
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- EP ID EP23132
- DOI http://doi.org/10.22214/ijraset.2017.2043
- Views 281
- Downloads 6
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