“Threat hunting,” or “cyber threat hunting” is the process of proactively and iteratively searching through networks and datasets to detect threats that evade existing automated tools and is done by a threat hunter or security analyst. It is essential for network security because it works to identify hidden threats within an existing set of network data.

Threat hunting utilizes manual techniques from the threat hunter and machine-assisted techniques, the combination of which aims to find Tactics, Techniques, and Procedures (TTPs) of advanced adversaries. While this methodology is both time-tested and effective, it is also time consuming, and can sometimes miss important clues in mountains of network data. In the article below, we will discuss not only what threat hunting is, but also how it can be made more efficient through the use of modern tools.

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About Version 2
Version 2 is one of the most dynamic IT companies in Asia. The company develops and distributes IT products for Internet and IP-based networks, including communication systems, Internet software, security, network, and media products. Through an extensive network of channels, point of sales, resellers, and partnership companies, Version 2 offers quality products and services which are highly acclaimed in the market. Its customers cover a wide spectrum which include Global 1000 enterprises, regional listed companies, public utilities, Government, a vast number of successful SMEs, and consumers in various Asian cities.

GREYCORTEX uses advanced artificial intelligence, machine learning, and data mining methods to help organizations make their IT operations secure and reliable.

MENDEL, GREYCORTEX’s network traffic analysis solution, helps corporations, governments, and the critical infrastructure sector protect their futures by detecting cyber threats to sensitive data, networks, trade secrets, and reputations, which other network security products miss.

MENDEL is based on 10 years of extensive academic research and is designed using the same technology which was successful in four US-based NIST Challenges.