Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Machine Learning

CrowdStrike

Using Artificial Intelligence and Machine Learning to Combat Hands-on-Keyboard Cybersecurity Attacks

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

Corelight Investigator introduces new Machine Learning Models

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.

calligo

How Machine Learning as a Service improves organizational productivity and reduces costs

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.

splunk

Machine Learning in Security: Deep Learning Based DGA Detection with a Pre-trained Model

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.

netskope

Detecting Ransomware Using Machine Learning

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.

netskope

Deep Learning for Phishing Website Detection

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.

Artificial Intelligence and Machine Learning: A Growing Reality

James Rees talks about ai or artificial intelligence and machine learning as science fiction staples for 20 years but is now a growing reality. Connect with James Rees Hello, I am James Rees, the host of the Razorwire Podcast. This podcast brings you insights from leading cyber security professionals who dedicate their careers to making a hacker’s life that much more difficult.

CrowdStrike's Approach to Artificial Intelligence and Machine Learning

CrowdStrike combines human and machine intelligence to uncover new threats and enable high fidelity detections. Machine learning is implemented across the process lifecycle in the CrowdStrike platform. In this demonstration we will dive into how machine learning is used and how it can benefit your organization’s security.
CrowdStrike

From Data to Deployment: How Human Expertise Maximizes Detection Efficacy Across the Machine Learning Lifecycle

Security is a data problem. One of the most touted benefits of artificial intelligence (AI) and machine learning (ML) is the speed at which they can analyze potentially millions of events and derive patterns out of terabytes of files. Computational technology has progressed to the point where computers can process data millions of times faster than a human could.