Today, we released a Threat Definition Update bundle for our Tripwire Industrial Visibility solution to aid in the detection of Zerologon. Otherwise known as CVE-2020-1472, Zerologon made news in the summer of 2020 when it received a CVSSv3 score of 10—the most critical rating of severity. Zerologon is a vulnerability that affects the cryptographic authentication mechanism used by the Microsoft Windows Netlogon Remote Protocol (MS-NRPC), a core authentication component of Active Directory.

Lateral movement is one of the most consequential types of network activity for which organizations need to be on the lookout. After arriving at the network, the attacker keeps ongoing access by essentially stirring through the compromised environment and obtaining increased privileges (known as “escalation of privileges”) using various tools and techniques. Attackers then use those privileges to move deeper into a network in search of treasured data and other value-based assets.

Let’s talk visibility for a moment. Security visibility is a data-at-scale problem. Searching, analyzing, and processing across all your relevant data at speed is critical to the success of your team’s ability to stop threats at scale. Elastic Security can help you drive holistic visibility for your security team, and operationalize that visibility to solve SIEM use cases, strengthen your threat hunting practice with machine learning and automated detection, and more.

Security Information and Event Management (SIEM) tools play a vital role in helping your organization in discovering threats and analyzing security incidents. Logsign’s internal team continuously makes correlation rules and alerts so that your team’s workload is minimized. In our previous posts, we discussed generating important reports and deriving maximum possible benefits from use cases. In this article, we will be discussing SIEM alerts best practices.

Security and development teams are increasingly adopting DevOps methodologies. However, traditional security tools bolted onto the development process often cause friction, decrease velocity, and require time-consuming manual processes. Manual tools and legacy AppSec approaches limit security teams’ ability to deliver the timely and actionable security feedback needed to drive improvements at the pace of modern development.

While working with customers over the years, I've noticed a pattern with questions they have around operationalizing machine learning: “How can I use Machine Learning (ML) for threat detection with my data?”, “What are the best practices around model re-training and updates?”, and “Am I going to need to hire a data scientist to support this workflow in my security operations center (SOC)?” Well, we are excited to announce that the SplunkWorks team launched a new add-

As companies responded to the COVID-19 pandemic with remote work, cybercriminals increased their social engineering and ransomware attack methodologies. Ransomware, malicious code that automatically downloads to a user’s device and locks it from further use, has been rampant since the beginning of March 2020. According to a 2020 report by Bitdefender, ransomware attacks increased by seven times when compared year-over-year to 2019.

“Doubt is an unpleasant condition, but certainty is an absurd one.” Whilst I claim no particular knowledge of the eighteenth-century philosopher Voltaire, the quote above (which I admit to randomly stumbling upon in a completely unrelated book) stuck in my mind as a fitting way to consider the shift from traditional, perimeter-focused ’network security’ thinking to that of ‘ZTA’ (Zero Trust Architecture.)

Deep packet inspection (DPI) refers to the method of examining the full content of data packets as they traverse a monitored network checkpoint. Whereas conventional forms of stateful packet inspection only evaluate packet header information, such as source IP address, destination IP address, and port number, deep packet inspection looks at fuller range of data and metadata associated with individual packets.