Dark Reading is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

Vulnerabilities / Threats

7/22/2015
01:50 PM
Connect Directly
Google+
Twitter
RSS
E-Mail
50%
50%

Researchers Enlist Machine Learning In Malware Detection

No sandbox required for schooling software to speedily spot malware, researchers will demonstrate at Black Hat USA.

In 100 milliseconds or less, researchers are now able to determine whether a piece of code is malware or not -- and without the need to isolate it in a sandbox for analysis.

Welcome to the age of machine learning as a tool for more efficiently detecting malware, via so-called "deep learning" techniques. Researchers have built a special machine learning tool module that employs static analysis of a piece of code to quickly spot -- and ultimately, stop -- malware infections. A pair of researchers plans to demonstrate live at Black Hat USA next month just how this approach can spot malware from live malware feeds.

Matt Wolff, chief data scientist at Cylance, says his team is applying deep learning--a more granular subset of machine learning--to malware detection by training the software via legitimate files and malicious ones, and teaching the application/algorithm which is which. The application then can take files it's never seen before and spot malware, he says.

It uses a static analysis approach. When you run malware to test it, the malware has a window "to fight back before you can stop it," Wolff says. "We don't run it [the malware], so the malware doesn't have a chance. And it's fast," he says, faster than sandboxing and analyzing malware.

The concept of employing machine learning and deep learning to malware detection isn't really new, but it's been only over the past few years that it's become more realistic to deploy, thanks to cloud-based computing options making the cost of big-data computing more affordable. You don't have to build a data center of hundreds of machines anymore; you can rent the necessary processing power for machine learning.  "Advances in processors, memory, etc., lend themselves to help make these techniques more powerful," Wolff says. "We don't see anyone [else] applying algorithms to … malware detection" yet, he says.

"The main premise behind machine learning is matching patterns. When you look at malware, you may not see any patterns. But when you look at a half of a billion samples, there may be tons of patterns that are relatively easy to discern," he says. "The goal of this model is to find these patterns."

A typical malware characteristic would be the ability for the code to use functions that capture and log keystrokes, for example.

Machine/deep learning is especially helpful in staying atop the increasingly polymorphic nature of malware. "If a malware author two months later comes up with a new [variant], there's a high probability the module you wrote is going to detect that. It has a predictive capability," Wolff says.

With the mountains of malware generated daily, the need for a more automated and intelligent method to learn, adapt, and catch malware is crucial. Cylance has some one- to 2 petabytes of data in its data set for machine learning: "We typically have a few hundred CPUs running for days to process and work through the data, and weeks and months running and training the machines to learn these things," Wolff says. It takes hundreds of gigabytes of memory, CPUs and "big machines," he says.

The machine learning-based method for now is all about detection. It's up to the security analyst or other tools to decide what to do next with the newly discovered malicious code, he says.

A deep learning system could ultimately replace today's existing malware detection tools, Wolff says. "A machine learning engine is more effective" than a signature-based engine, he says.

Wolff and his colleague Andrew Davis, a machine learning scientist at Cylance, will feed their deep-learning module some fresh meat malware live during their talk at Black Hat, called "Deep Learning on Disassembly."  "We'll … see what it catches," says Wolff.

[Register now for Black Hat USA.]

Kelly Jackson Higgins is the Executive Editor of Dark Reading. She is an award-winning veteran technology and business journalist with more than two decades of experience in reporting and editing for various publications, including Network Computing, Secure Enterprise ... View Full Bio

Comment  | 
Print  | 
More Insights
Comments
Oldest First  |  Newest First  |  Threaded View
Small Business Security: 5 Tips on How and Where to Start
Mike Puglia, Chief Strategy Officer at Kaseya,  2/13/2020
Architectural Analysis IDs 78 Specific Risks in Machine-Learning Systems
Jai Vijayan, Contributing Writer,  2/13/2020
Register for Dark Reading Newsletters
White Papers
Video
Cartoon
Current Issue
6 Emerging Cyber Threats That Enterprises Face in 2020
This Tech Digest gives an in-depth look at six emerging cyber threats that enterprises could face in 2020. Download your copy today!
Flash Poll
How Enterprises Are Developing and Maintaining Secure Applications
How Enterprises Are Developing and Maintaining Secure Applications
The concept of application security is well known, but application security testing and remediation processes remain unbalanced. Most organizations are confident in their approach to AppSec, although others seem to have no approach at all. Read this report to find out more.
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2020-9308
PUBLISHED: 2020-02-20
archive_read_support_format_rar5.c in libarchive before 3.4.2 attempts to unpack a RAR5 file with an invalid or corrupted header (such as a header size of zero), leading to a SIGSEGV or possibly unspecified other impact.
CVE-2019-20479
PUBLISHED: 2020-02-20
A flaw was found in mod_auth_openidc before version 2.4.1. An open redirect issue exists in URLs with a slash and backslash at the beginning.
CVE-2011-2498
PUBLISHED: 2020-02-20
The Linux kernel from v2.3.36 before v2.6.39 allows local unprivileged users to cause a denial of service (memory consumption) by triggering creation of PTE pages.
CVE-2012-2629
PUBLISHED: 2020-02-20
Multiple cross-site request forgery (CSRF) and cross-site scripting (XSS) vulnerabilities in Axous 1.1.1 and earlier allow remote attackers to hijack the authentication of administrators for requests that (1) add an administrator account via an addnew action to admin/administrators_add.php; or (2) c...
CVE-2014-3484
PUBLISHED: 2020-02-20
Multiple stack-based buffer overflows in the __dn_expand function in network/dn_expand.c in musl libc 1.1x before 1.1.2 and 0.9.13 through 1.0.3 allow remote attackers to (1) have unspecified impact via an invalid name length in a DNS response or (2) cause a denial of service (crash) via an invalid ...