More the investment on digitization, more the need for security. This is now a stark reality. The dependence on digital tools and technologies is enough to justify additional investments for increasing security levels. Better the security structure, difficult it would be for a hacker to mount an attack. Hackers also realize this and have evolved to penetrate multiple cyber defense layers and spend enough time within the targeted infrastructure to cause the required damage. They meticulously plan, implement these attacks with automated tools and immaculate precision. Built-in security software might not be able to detect an advanced attack.

IT teams must enhance their attack armory to distinguish legitimate and malicious activities. Automation alone cannot do this. Human expertise is needed to implement proactive threat detection and response practices by leveraging threat data to ensure rapid response to an incident that has evaded the automated solution. Knowing an attack indicator is difficult as automation can consider improper usage of a tool as malicious activity. If malicious behavior mimics normal user behavior, the rate of false positives is higher. Only human analysts are capable of evaluating an event as legitimate or not. They can investigate and rank automated detections and attribute them based on what’s normal and what’s not, reducing false positives and improving threat detection effectiveness.

Threat hunter capabilities

  • Identify stealth techniques designed by hackers to dodge the best analytics tools and algorithms.
  • Highly trained and dependable force capable of taking complex threats.
  • Data dependant and extracts it from various attacker activities and behaviors.
  • Capable of leveraging rich data sets to gain comprehensive visibility and pull out adversaries from their hideouts.
  • Contextualize data for better insights and incorporate threat intelligence to understand adversary behaviors and maximize hunting efficiency. 

A hunter’s experience plays a significant role in the game of threat hunting. He must think like an attacker and use his ingenuity and expertise to derive test hypotheses and utilize statistical methods to know the attacker’s location. Attacks are recreated and analyzed using available data to understand the full scale of infringement and gain clarity over the range of attack. After comprehending the attack thoroughly, threat hunters raise the alarm for incident response teams to take over, closing the gap between threat detection and response. Not only this reduces false positive rates, but it also brings down the mean time to respond.

Analysts focus on getting the context to ascertain the gravity of the incident. An incident’s severity includes multiple reasons such as type of threat actors, incident detection time, assets impacted, threat specifics, and its impact on customer business, remediation required, etc. Alerts from sensitive assets must be correlated as an attacker can move laterally. Every affected host is identified to gain full visibility on attacker actions. Threat hunters need a high level of expertise to know about attacker motivations, methods, tools to realize the level of harm they can perpetuate.

Machine learning can help

Machine Learning models can be trained on alerts validated by analysts, this includes triage, filtering, queuing, etc. Automation through ML models can also improve analyst outcomes. Though threat hunting is a part of Managed Detection and Response service, it involves a good mix of human know how and automation. This includes combining ML models, User Behaviour & Entity Behaviour Analytics (UEBA), and telemetry from internal & external sources to know attacker tactics, techniques, and procedures. Threat hunters have an offensive mindset. They do thorough research on various threat data types, conduct risk assessments, penetration tests, leverage the MITRE ATT&CK knowledge base, develop hypotheses from alerts, and simulate attacks. Analysts evaluate the data from automated threat detection techniques to understand relationships between data sets to reveal hidden malware threats. ML allows automatic addition of newly discovered threats to the watch list for faster detection and response.

Threat hunting gets an added boost when it’s integrated with various threat intelligence sources. Taking on targeted attacks calls for widespread experience, continuous learning across verticals, regular monitoring, and staying updated on incident response practices to deliver actionable remediation. It must be a routine exercise as attackers are looking to innovate new ways to breach. Threat hunting is critical to strengthening security posture, as it enables proactive detection of threats at the initial stage of an attack or compromise.

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