ECCouncil 212-82 Exam With Confidence Using Practice Dumps

 A data-driven hunting method is a type of threat hunting method that employs existing data collected from various sources, such as DNS and proxy logs, to generate and test hypotheses about potential threats. This method relies on data analysis and machine learning techniques to identify patterns and anomalies that indicate malicious activity. A data-driven hunting method can help discover unknown or emerging threats that may evade traditional detection methods. An entity-driven hunting method is a type of threat hunting method that focuses on specific entities, such as users, devices, or domains, that are suspected or known to be involved in malicious activity. A TTP-driven hunting method is a type of threat hunting method that leverages threat intelligence and knowledge of adversary tactics, techniques, and procedures (TTPs) to formulate and test hypotheses about potential threats. A hybrid hunting method is a type of threat hunting method that combines different approaches, such as data-driven, entity-driven, and TTP-driven methods, to achieve more comprehensive and effective results. 

A low-interaction honeypot is a type of honeypot that simulates a real OS as well as the applications and services of a target network, but only responds to pre-configured commands. It is designed to capture basic information about the attacker, such as their IP address, tools, and techniques. A low-interaction honeypot is easier to deploy and maintain than a high-interaction honeypot, which fully emulates a real system and allows the attacker to interact with it. A pure honeypot is a real system that is intentionally vulnerable and exposed to attackers. A medium-interaction honeypot is a type of honeypot that offers more functionality and interactivity than a low-interaction honeypot, but less than a high-interaction honeypot.

Anomaly-Based Intrusion Detection Systems (IDS):

Anomaly-based IDS monitor network traffic and system activities for unusual patterns that may indicate malicious behavior. They are effective in identifying unknown threats by detecting deviations from the established baseline of normal activities.