According to the NIST incident response handbook, after detecting a ransomware outbreak and notifying the affected parties, the next step is to eradicate the malicious software from the infected machines. This involves removing the ransomware and any associated malware to prevent further encryption or spread of the infection3

Predictive analytics is the most suitable data analytic technique for anticipating future cyberattacks. It involves using historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on patterns and trends. This approach can help organizations forecast potential cyber threats and take proactive measures to mitigate them before they occur123.

The packet capture shown in the exhibit is indicative of DNS tunneling. This conclusion is drawn from the observation of the packets' consistent size and frequency, which are directed to a specific destination IP address. Such a pattern is characteristic of DNS tunneling, where data is encapsulated within DNS queries and responses. This method is often used to bypass security controls or for data exfiltration, as it can blend with regular DNS traffic, making detection more challenging.
DNS tunneling can be used for legitimate purposes, such as when an organization's remote workers need to access internal networks. However, it is also a technique commonly exploited by attackers to smuggle data out of a network or to communicate with command and control servers while evading detection.
References:
* Cisco's official training materials on Performing CyberOps Using Cisco Security Technologies (CBRCOR) provide insights into analyzing packet captures and identifying different types of network traffic, including malicious activities like DNS tunneling.
* The Cisco CyberOps Associate Certification resources include detailed information on threat identification and response strategies pertinent to DNS tunneling.