Design Pattern Classifiers under Attack for Security Evaluation using Multimodal System Download

Pattern classification is a branch of machine learning that focuses on recognition of patterns and regularities in data. This Pattern classification system are commonly used in adversarial applications, like biometric authentication, network intrusion detection, and spam filtering, in which data can be purposely manipulated by humans to undermine their operation. As this adversarial scenario is not taken into account by classical design methods, pattern classification systems may exhibit vulnerabilities, whose exploitation may severely affect their performance, and consequently limit their practical utility. Extending pattern classification theory and design methods to adversarial settings. Here, propose a framework for empirical evaluation of classifier security that formalizes and generalizes the main ideas proposed in the literature, and give examples of its use in real applications. Reported results show that security evaluation can provide a more complete understanding of the classifier's behaviour in adversarial environments, and lead to better design choices. This framework can be applied to different classifiers on one of the application from the spam filtering, biometric authentication and network intrusion detection. So in this propose an algorithm for the generation of training and testing sets to be used for security evaluation. Now result shows providing security to system using application as blogger for this applying spam filtering, biometric authentication methods. That shows pattern classification for detecting spam comments which easy to detect spam. Rupali Baliram Navalkar | Prof. Rajeshri R. Shelke "Design Pattern Classifiers under Attack for Security Evaluation using Multimodal System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd97.pdf