An Approach on Cyber Threat Intelligence Using Recurrent Neural Network

Abstract

The term cyber threat intelligence refers to evidence-based knowledge that has been collected, processed, and analyzed to understand about the threat existing or emerging threats on an organization to steal sensitive data. Today the world of cyber security faces numerous challenges with the rapidly evolving cyber-attacks. Cyber threat intelligence helps cyber security to detect and prevent cyber-attacks with the help of numerous tools and techniques. Still advanced persistent threat stands as a promising challenge for cyber security. The term advanced persistent threat (APT) refers to an attack campaign. Where an intruder, or a team of introducer, establishes on illicit, long term presence on a network in order to mine highly sensitive data. It is a sophisticated, sustained cyber-attack which has been carefully planned and designed to cause several large costly data breaches, evade existing security measures, and fly under the radar. This stealthy cyber-attack makes use of various tools and techniques to gain unauthorized access to network and steal highly valuable sensitive data. Usually, the APT groups are the nation state actors or well-resourced groups that target to compromise both public and private organizations. Thus, APT stands as an alarming topic for resources in the field of cyber security. This hinders us to propose a new cyber threat intelligence model that could efficiently detect and prevent APT’s.