All ML technology isn’t created equal. Learn how the CrowdStrike® ML-based Engine Defends Against Unknown Malware. While many organizations are guarding the front door with yesterday’s signature-based antivirus (AV) solutions, today’s unknown malware walks out the back door with all their data. What’s the answer?
A new white paper, “The Rise of Machine Learning in Cybersecurity,” explains machine learning (ML) technology — what it is, how it works and why it offers better protection against the sophisticated attacks that bypass standard security measures. You’ll also learn about CrowdStrike’s exclusive ML technology and how, as part of the Falcon platform’s next-gen AV solution, it dramatically increases your ability to detect attacks that use unknown malware.
Learn about the tools, technologies and techniques required for comprehensive detection and remediation of advanced malware threats and why traditional signature-based approaches fall short of protecting your organization.
Cyber threat intelligence is unquestionably a hot buzzword in the security industry these days. It is being used to seek venture capital and fund startups. It is being pitched to the enterprise market by providers and consultants. However, in this paper, we argue that the majority of what is being billed as “threat intelligence” isn’t. It’s data. From lists of bad IPs or application vulnerabilities to malware signatures, social media data or indicators of compromise (“IOCs”), none of these things are “intelligence.” They’re data.
In this white paper, we define the difference between intelligence and data, and then illustrate the theoretical discussion in a concise case study in the tangible terms of a real-world practitioner and an actual event.
Published By: Webroot
Published Date: Sep 18, 2013
This FAQ tells you how to move beyond the old trade-off between anti-malware effectiveness and speed. It answers questions such as what is wrong with conventional approaches, which includes the inability of clients to perform signature matching operations on today’s more than 70 million malware variants. It also discusses how the cloud and behavioral detection overcome the limitations of signature-based approaches. Finally, it answers questions about how cloud solutions can offer specific benefits such as:
• Improving speed by offloading pattern matching from endpoints
• Eliminating large signature downloads
• Stopping zero day attacks
Fileless attacks surged in 2017, largely due to their ability to bypass traditional antivirus solutions. Last year was host to several fileless malware victories. OceanLotus Group infiltrated Asian corporations during Operation Cobalt Kitty, and conducted nearly six months of fileless operations before detection. Ransomware hall-of-famers Petya and WannaCry both implemented fileless techniques in their kill chains. Every major player in information security agrees that fileless attacks are difficult to stop, and the threats are growing worse. Abandoning files is a logical and tactical response to traditional AV solutions which have overcommitted to file-intensive and signature-based blacklists. What can security solutions offer when there are no infected files to detect? How will a blacklist stop an aggressor that only uses legitimate system resources? The security landscape is changing and the divide between traditional AV products and next-generation security solutions is growing wider by the day. Cylance® has built a reputation on security driven by artificial intelligence and provides a frontline defense against fileless malware. This document details how Cylance protects organizations.
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