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Pawar, Kunjali
- Spam Filtering Security Evaluation Using MILR Classifier
Authors
1 Dr. D.Y. Patil School of Engineering and Technology, Pune, IN
2 Dr. D. Y. Patil School of Engineering and Technology, Pune, IN
Source
Automation and Autonomous Systems, Vol 8, No 3 (2016), Pagination: 57-60Abstract
Statistical spam filters are vulnerable to the adversarial attacks. An e-mail is classed as spam if a minimum of one instance within the corresponding bag is spam, and as legitimate if all the instances in its square measure legitimate. These systems based on the design methods and classical methods which do not take into account adversarial settings. In this paper, the security evaluation framework is proposed to avoid the detection in the Spam filtering with the help of Multiple Instance Logistic Regression i.e. MILR. In addition to define the model of Adversary with the guidelines for simulating attack scenarios The principal theme of the framework is to develop an enhanced model which anticipates the attacks by utilizing a data distribution.Keywords
Adversary, Multiple Instance Learning, Multiple Instance Logistic Regression (MILR), Spam Filtering.- A Framework for Spam Filtering Security Evaluation
Authors
Source
Software Engineering, Vol 8, No 6 (2016), Pagination: 140-144Abstract
The Pattern classification system is an annex of the Machine learning on which the focal point is the recognition of patterns in the data. In case adversarial applications use, for example Spam Filtering, the Network Intrusion Detection System (NIDS), Biometric Authentication, the pattern classification systems are used. Spam filtering is and adversary application in which data can be employed by humans to attenuate perspective operations. To appraise the security issue related Spam Filtering voluminous machine learning systems. We presented a framework for the experimental evaluation of the classifier security in an adversarial environments, that combines and constructs on the arms race and security by design, Adversary modelling and Data distribution under attack. Furthermore, we presented a MILR classifier with SVM and LR classifier for classification to categorize among legitimate and spam emails on the basis of their textual content.