Predicting the Spread of Malware Outbreaks Using Autoencoder Based Neutral Networks

Abstract

Malware Outbreaks are pervasive in today's digital world. However, there is a lack of awareness on part of general public on how to safeguard against such attacks and a need for increased cooperation between various national and international research as well as governmental organizations to combat the threat. On the positive side, cyber security websites, blogs and newsletters post articles outlining the working and spread of a malware outbreak and steps to recover from the same as well. In this project, an effective approach to predicting the spread of malware outbreaks is presented. The scope of the project is 15 Malware Outbreaks and the approach involves collecting these cyber aware articles from the web, assigning them to the 15 Malware Outbreaks using Topic Modeling and Similarity Analysis and along with Spread information of the Malware Outbreaks, this is input to auto encoder neural network for learning latent space representations which are further used to predict the spread of malware outbreak as either high or low spread outbreak, achieving a prediction accuracy of 75.56. This work can be used to process large amount of cyber aware content for effective and accurate prediction in the era of much-needed cyber security.