Knowledge is Power: Entity Extraction for Cyber Threat Intelligence

Entity Extraction, Homeland Security, Intelligence Analysis, Risk Management, Social Media Analysis

Cyber attacks are one of the top threats to national security and public and private sector organizations in general. They can cripple critical infrastructure (e.g., the electrical grid, power plants, nuclear facilities, financial institutions, military bases), steal valuable intellectual property and sensitive information, paralyze operations, and be exceedingly costly to remedy.

Traditional cyber security has focused on structured data from network activity logs. There is, however, great largely-untapped value in cyber threat intelligence from external unstructured data sources to alert about and gain insight into new methods, weaknesses, or bad actors among others. By detecting emerging cyber threats discussed in the dark web, forums, chatrooms, news, and social media in real time, organizations are able to develop comprehensive cyber threat profiles and alert systems to proactively defend against cyber threats.

As with other Big Data problems, a cyber threat intelligence solution must be able to handle the three V’s of Big Data: the volume, variety, and velocity of data. There are in fact massive amounts of unstructured data to analyze, from many sources, in various formats, and that need to be processed in near real time. In the case of Cyber Threat Intelligence, there is a fourth dimension at play: novelty. Cyber threats evolve rapidly and new types of cyber attacks, malware, tactics, and bad actors appear constantly. Keeping up with the scale and complexity of all this unstructured data may seem like a daunting task.

How NetOwl’s Entity Extraction Provides Critical Cyber Threat Intelligence

Knowledge is power. Using sophisticated AI and Natural Language Processing, NetOwl’s Entity Extraction can identify mentions of new, previously unknown vulnerabilities, tactics, methods, bad actors, targets, and cyber-attacks based on linguistic context.

There are a number of ways in which NetOwl’s Entity Extraction provides unparalleled cyber threat intelligence capabilities:

  1. NetOwl’s extensive Cyber Security ontology integrates concepts from US CERT, US Department of Defense, and other leading cyber security organizations to automatically identify key cyber-related events in unstructured data. Such events include denial of service attacks, cyber espionage, cyber theft, and insider threat among others, as well as the entities involved in these cyber events – including malware, attackers, target organizations, and target assets.
  2. NetOwl’s real-time, scalable Entity Extraction for Cyber Security empowers your organization to process massive volumes of unstructured threat intelligence data.
  3. NetOwl’s intelligent normalization of extracted information enables data aggregation to reveal the bigger picture such as trending actors, malware, tactics, methods, vulnerabilities, and targets. With NetOwl’s dashboard, threat data can be sliced and diced as desired to greatly enhance threat intelligence analytics and reveal actionable insights.
  4. The cyber attacks with the biggest potential for catastrophic damage are those hitting the nation’s critical infrastructure – the electrical grid, power plants, nuclear facilities, financial institutions, military bases, and many others. NetOwl’s Entity Extraction for Cyber Security includes extensive coverage of critical infrastructure such as energy, financial, and telecommunication facilities and organizations. NetOwl automatically identifies mentions of critical infrastructure in unstructured threat intelligence data for monitoring and alerting not only for incidents directly related to your specific organization, but to related organizations.

NetOwl’s Entity Extraction for Cyber Security enables the early detection and monitoring of emerging threats. It provides your organization with insights into emerging cyber threats so that you can make risk-based decisions and proactively defend against those threats.