Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Machine Learning


Release with Trust or Die. Key swampUP 2023 Announcements

Every year, JFrog brings the DevOps community and some of the world’s leading corporations together for the annual swampUP conference, aimed at providing real solutions to developers and development teams in practical ways to prepare us all for what’s coming next.


Understanding Machine Learning Attacks, Techniques, and Defenses

Machine learning (ML) is a subset of Artificial Intelligence (AI), which enables machines and software to automatically learn from historical data to generate accurate output without being programmed to do so. Many leading organizations today have incorporated machine learning into their daily processes for business intelligence. But the ability of machine learning can be altered by threat actors to be malicious, causing systems to malfunction, or to execute an attack.


Toward a more resilient SOC: the power of machine learning

To protect the business, security teams need to be able to detect and respond to threats fast. The problem is the average organization generates massive amounts of data every day. Information floods into the Security Operations Center (SOC) from network tools, security tools, cloud services, threat intelligence feeds, and other sources. Reviewing and analyzing all this data in a reasonable amount of time has become a task that is well beyond the scope of human efforts.


Leveraging the Dark Side: How CrowdStrike Boosts Machine Learning Efficacy Against Adversaries

The power of the CrowdStrike Falcon® platform lies in its ability to detect and protect customers from new and unknown threats by leveraging the power of the cloud and expertly built machine learning (ML) models. In real-world conditions and in independent third-party evaluations, Falcon’s on-sensor and cloud ML capabilities consistently achieve excellent results across Windows, Linux and macOS platforms.


Fine-tuning Cloud SIEM detections through machine learning

Security engineering teams spend hours every week tuning their security information and event management (SIEM) systems to ensure that they are effective at detecting security threats and minimizing false positives. Such “tuning tax” is common as customers add new SIEM rules to cope with rapidly changing threat landscape and attacker tactics and as their attack surface evolves through automated changes to their application and infrastructure stacks.


Machine Learning in Security: Detect Suspicious TXT Records Using Deep Learning

There are about 90 DNS resource record types (RR) of which many of them are obsolete today. Of the RR’s used, DNS TXT record offers the most flexibility in content by allowing user defined text. The TXT record initially designed to hold descriptive text (RFC 1035) is widely used for email verification, spam prevention and domain ownership verification.


Machine Learning in Security: Detecting Suspicious Processes Using Recurrent Neural Networks

Malicious software like ransomware often use tactics, techniques, and procedures such as copying malicious files to the local machine to propagate themselves across the network. A few years ago, the Cybersecurity and Infrastructure Security Agency, the Federal Bureau of Investigation, and the Department of Health and Human Services issued a joint cybersecurity advisory to ward off potential harm from threat actors for at-risk entities.


Trustwave MailMarshal PageML Scanner Detects 30% More Phishing Attempts

Trustwave’s MailMarshal received a major update this month with the addition of PageML to the Blended Threat Module. The BTM enables the email security solution to conduct in-depth, real-time scans when a URL in an email is clicked to determine if the URL is malicious. PageML boosts the BTM’s ability to detect malicious URLs by one-third by applying machine learning techniques to page content in real time. The new scanning feature is named PageML, short for Page Machine Learning.


CrowdStrike's Free TensorFlow-to-Rust Conversion Tool Enables Data Scientists to Run Machine Learning Models as Pure Safe Code

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.