Abstract
There are several ways to improve an organization’s cybersecurity protection against intruders. One of the ways is to proactively hunt for threats, i.e., threat hunting. Threat Hunting empowers organizations to detect the presence of intruders in their environment. It identifies and searches the tactics, techniques, and procedures (TTP) of the attackers to find them in the environment. To know what to look for in the collected data and environment, it is required to know and understand the attacker's TTPs. An attacker's TTPs information usually comes from signatures, indicators, and behavior observed in threat intelligence sources. Traditionally, threat hunting involves the analysis of collected logs for Indicator of Compromise (IOCs) through different tools. However, network and security infrastructure devices generate large volumes of logs and can be challenging to analyze thus leaving gaps in the detection process. Similarly, it is very difficult to identify the required IOCs and thus sometimes makes it difficult to hunt the threat which is one of the major drawbacks of the traditional threat hunting processes and frameworks. To address this issue, intelligent automated processes using machine learning can improve the threat hunting process, that will plug those gaps before an attacker can exploit them. This paper aims to propose a machine learning-based threat-hunting model that will be able to fill the gaps in the threat detection process and effectively detect the unknown adversaries by training the machine learning algorithms via extensive datasets of TTPs and normal behavior of the system and target environment. The model is comprised of five main stages. These are Hypotheses Development, Equip, Hunt, Respond and Feedback stages. This threat hunting model is a bit ahead of the traditional models and frameworks by employing machine learning algorithms.