f) 2020 - 82 Documents

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Author: search.filters.author.Vijaya, M SSubject: search.filters.subject.Machine learning

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    MALWARE FAMILY CLASSIFICATION MODEL USING USER DEFINED FEATURES AND REPRESENTATION LEARNING
    (Springer Link, 2020-11-20) Gayathri, T; Vijaya, M S
    Malware is very dangerous for system and network user. Malware identification is essential tasks in effective detecting and preventing the computer system from being infected, protecting it from potential information loss and system compromise. Commonly, there are 25 malware families exists. Traditional malware detection and anti-virus systems fail to classify the new variants of unknown malware into their corresponding families. With development of malicious code engineering, it is possible to understand the malware variants and their features for new malware samples which carry variability and polymorphism. The detection methods can hardly detect such variants but it is significant in the cyber security field to analyze and detect large-scale malware samples more efficiently. Hence it is proposed to develop an accurate malware family classification model contemporary deep learning technique. In this paper, malware family recognition is formulated as multi classification task and appropriate solution is obtained using representation learning based on binary array of malware executable files. Six families of malware have been considered here for building the models. The feature dataset with 690 instances is applied to deep neural network to build the classifier. The experimental results, based on a dataset of 6 classes of malware families and 690 malware files trained model provides an accuracy of over 86.8% in discriminating from malware families. The techniques provide better results for classifying malware into families.
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    MEASURING WEB CONTENT CREDIBILITY USING PREDICTIVE MODELS
    (Springer Link, 2020-01-30) Manjula, R; Vijaya, M S
    Web content credibility is a measure of believable and trustworthy of the web content that is perceived. Content can turn out to be unreliable if it is not up-to-date and it is not measured for quality or accuracy and therefore, web content credibility is important for the individuals to access the content or information. The analysis of content credibility is an important and challenging task as the content credibility is expressed on essential factors. This paper focus on building predictive models to discover and evaluate credibility of a web page content through machine learning technique. A corpus of 300 web page contents have been developed and the factors like Readability, Freshness, Duplicate Content are defined and captured to model the credibility of web content. Two different labeling such as binary labeling and numeric labeling are used for defining credibility. In case of binary labeling, the high and low credibility of web content are represented by 1 and 0, respectively, whereas in case of numeric labeling five-point scale rating is used to mark the content credibility. Accordingly, two independent datasets have been developed. Different regression algorithms such as Linear Regression, Logistic Regression, Support Vector Regression (SVR) are employed for building the predictive models. Various experiments have been carried out using two different datasets and the performance analysis shows that the Logistic Regression model outperforms well when compared to other prediction algorithms.