Biblio

With the rapid growth of the cyber attacks, cyber threat intelligence (CTI) sharing becomes essential for providing advance threat notice and enabling timely response to cyber attacks. Our goal in this paper is to develop an approach to extract low-level cyber threat actions from publicly available CTI sources in an automated manner to enable timely defense decision making. Specifically, we innovatively and successfully used the metrics of entropy and mutual information from Information Theory to analyze the text in the cybersecurity domain. Combined with some basic NLP techniques, our framework, called ActionMiner has achieved higher precision and recall than the state-of-the-art Stanford typed dependency parser, which usually works well in general English but not cybersecurity texts.

Fast-changing topologies and uncoordinated transmissions are two critical challenges of implementing data security in vehicular ad-hoc networks (VANETs). We propose a new protocol, where transmitters adaptively switch between backing off retransmissions and changing keys to improve success rate. A new 3-dimensional (3-D) Markov model, which can analyze the proposed protocol with symmetric or asymmetric keys in terms of data security and connectivity, is developed. Analytical results, validated by simulations, show that the proposed protocol achieves substantially improved resistance against collusion attacks.