SADI: A Novel Model to Study the Propagation of Social Worms in Hierarchical Networks - 2017 PROJECT TITLE : SADI: A Novel Model to Study the Propagation of Social Worms in Hierarchical Networks - 2017 ABSTRACT: As a lot of and a lot of people depend upon social networks for business and life, social worms represent one of the major security threats to our society. Trendy social worms exhibit two new features, message notification and therefore the temporal characteristic of human mobility. Message notification indicates a user will get a reminder once a brand new message comes to a social account. The temporal characteristic of human mobility indicates a user will operate corresponding pc in several locations with completely different resting time. Previous scholars have proposed some analytical models for the propagation dynamics of social worms. However, they did not think about the on top of 2 features and there's one critical downside unrealized, that is structural imperfection of network topology. Previous models have not taken under consideration the hierarchical topology structure, that results from a several -to-several relationship between users and hosts. To deal with these problems, we have a tendency to model propagation dynamics of social worms oriented hierarchical networks during this paper, and therefore the proposed model accurately describes the propagation behavior of social worms. We conduct both a theoretical analyses and intensive simulations to show our model will overcome inaccuracy in the amount of infected nodes and give a stronger approximation for the worm propagation. The results show that our model presented in this paper achieves a larger accuracy in characterizing the propagation of recent social worms. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest On the Soundness and Security of Privacy-Preserving SVM for Outsourcing Data Classification - 2017 Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection - 2017