Deep learning is a core part of CrowdStrike’s arsenal of machine learning (ML) techniques, and we are constantly innovating in this area to boost the performance of our ML models. However, ML can consume large amounts of computing resources. To minimize the computing load and its associated costs, we strive to optimize performance and resource utilization for our models as well as address any safety issues related to the use of third-party tools.

With news headlines like “A massive ransomware attack hit hundreds of businesses” becoming common, concern about malware has never been higher. High-profile examples of malware like DarkSide, REvil have been profiled so many times that not only cybersecurity professionals are on edge — every organization that has on-premises or in-the-cloud workloads is concerned.

Corelight Investigator furthers its commitment to delivering next-level analytics through the expansion of its machine learning models. Security teams are now enabled with additional supervised and deep learning models, including: We continue to provide complete transparency behind our evidence -- showing the logic behind our machine learning models and detections, allowing analysts to quickly and easily validate the alerts.

One thing’s for sure: in 2023, Machine Learning will penetrate even more businesses in even more sectors, following tremendous growth last year. But when it comes to ML, what should organizations have their eyes on, to stay ahead of their competitors?

85% of Machine Learning (ML) projects fail. This stark reminder from Gartner – despite more tools being available to businesses than ever. The thing is ML success is not just about tools and technology; it’s about how they’re put into production by experts. Plural. Machine Learning – that improves productivity and profitability by finding valuable insights buried deep in your company databases – needs a small army to leverage it.

Recent advancements in machine learning, the latest on Black Proxies, and the DHS Cyber Safety Board’s plan to review Lapsus$ gang’s hacking tactics.

The SMLS team enables Splunk customers to find obscure and buried threats in large amounts of data through expert analytics. This work is part of a set of machine learning detections built by a specialized team of security-focused data scientists working in concert with Splunk’s threat research teams to help Splunk customers sift through vast amounts of data to identify and alert users of suspicious content.

Ransomware attacks are on the rise. Many organizations have fallen victim to ransomware attacks. While there are different forms of ransomware, it typically involves the attacker breaching an organization’s network, encrypting a large amount of the organization’s files, which usually contain sensitive information, exfiltrating the encrypted files, and demanding a ransom.

Phishing is one of the most common online security threats. A phishing website tries to mimic a legitimate page in order to obtain sensitive data such as usernames, passwords, or financial and health-related information from potential victims. Machine learning (ML) algorithms have been used to detect phishing websites, as a complementary approach to signature matching and heuristics.