Abstract
Identification of individuals through the use of electronic footprints can be a security vulnerability and also a blessing in disguise. In this work, we have introduced how we can scale up and enhance representations using the generalized form of GAN architecture, capable of learning from generative features and differences in generalized representation. We applied the architecture for accurate person identification and person tracking using a series of browsing footprint patterns and, at the same time, can easily be extended for detection of anomaly, malware, and other browser-based phishing software activity detection. Our proposed GAN network is characterized for the detection of individuals through dissimilarity between real and generated samples and quantification of detected difference, both of which enhanced learning content. Our contributions are in the architectural definition of Generalized Attentive Sequential Generative Adversarial Network (GenAtSeq-GAN), identification of log characteristics for discrimination, and the procedure for scaling up the detection system. We achieved the effectiveness of 80.7% for GenAtSeq, 46 units higher than the older sequential GAN versions.
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Sur, C. GenAtSeq GAN with Heuristic Reforms for Knowledge Centric Network with Browsing Characteristics Learning, Individual Tracking and Malware Detection with Website2Vec. SN COMPUT. SCI. 1, 228 (2020). https://doi.org/10.1007/s42979-020-00234-8
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DOI: https://doi.org/10.1007/s42979-020-00234-8