Analytics

7/20/2016
12:00 PM
Connect Directly
Twitter
RSS
E-Mail
50%
50%

Improving Attribution & Malware Identification With Machine Learning

New technique may be able to predict not only whether unfamiliar, unknown code is malicious, but also what family it is and who it came from.

One of the cybersecurity promises of machine learning (particularly "deep learning") is that it can accurately identify malware nobody has ever seen before because of what it's learned about malware it's seen in the past. Konstantin Berlin, senior research engineer at Invincea Labs, is trying to take the techology further, so that organizations can get more information about unfamiliar code than simply "it's benign" or "it's malicious."

Berlin, who will be presenting his work next month at Black Hat, says security pros also want to know more about the malware family so they can plan their mitigation strategy accordingly. His technique, he says will do that, as well as improve malware triage and attribution by using new methods of recognizing similarities between malware samples. This can all be done in a customized way that enables each organization to choose what features and factors interest them most.

Berlin explains machine learning's difference to traditional signature-based anti-malware like this: If, for example, you want to predict the direction a rocket will go when it sets off, he says you don't necessarily need to learn the physics of propulsion and enter equations into the machine. You simply need to feed it lots of data of examples of rockets going off until it learns to accurately predict where the rockets will go. "Based upon millions of observations, it won't necessarily explain the rule, but it works in terms of prediction."

So, even if the machine has never seen something before, it will know it's malicious -- even if it doesn't know precisely why.

What Berlin wants to do, however, is give people more than just benign or malicious.

To do that, he's using a technique that improves the way security tools recognize what binary is similar to another -- and therefore how they are classified into families, attributed to malware authors, and tied to threat actors. 

According to Berlin, the current process usually used is expensive to develop, and requires periodic retuning that is done manually because organizations have their own sets of features they look for in malware binaries, their own weighting system for which features are most significant, and their own methods for minimizing the impact of those features that aren't important at all. Because of the costs and the labor, the retuning isn't done as often, and therefore it's more difficult to keep up with the pace of malware evolution.  

The method Berlin is presenting at Black Hat next month may not only improve accuracy but make the process cheaper, he believes. It uses a technique called supervised embedding, and is something the security world more commonly encounters in facial recognition.

Supervised embedding is a way to disregard malware samples' unimportant features, enhance their most important features, and re-map the distance between those malware samples. Distance thus mirrors "semantic sense" and similarity is measured by the features the security team has deemed are the most essential for their needs. So, if they're specifically interested in principally grouping malware by the likely threat actor, target industry, attack vector or attack type, they could. Any features of a file that are unrelated to whether it is malicious are automatically eliminated, says Berlin, "so the distances rely on the tradecraft of the malware."

It does not require a stack of signatures, but the technology does require a database of labels for all of these malware features. Berlin is using Microsoft's existing database of families and variants, but organizations could invest in creating their own bespoke database that truly zeroes in on the information they want.

"That's the beauty of machine learning," he says. "You train it for the task you want to accomplish."

This sort of system, this brain, is considerably lighter to carry around than a stack of signatures, too, says Berlin. This "statistical approach," requires less power than an "all or nothing" approach, he says. 

Related Content:

Black Hat USA returns to the fabulous Mandalay Bay in Las Vegas, Nevada July 30 through Aug. 4, 2016. Click for information on the conference schedule and to register.

Sara Peters is Senior Editor at Dark Reading and formerly the editor-in-chief of Enterprise Efficiency. Prior that she was senior editor for the Computer Security Institute, writing and speaking about virtualization, identity management, cybersecurity law, and a myriad ... View Full Bio

Comment  | 
Print  | 
More Insights
Comments
Newest First  |  Oldest First  |  Threaded View
Julian Assange Arrested in London
Dark Reading Staff 4/11/2019
8 'SOC-as-a-Service' Offerings
Steve Zurier, Freelance Writer,  4/12/2019
Register for Dark Reading Newsletters
White Papers
Video
Cartoon
Current Issue
5 Emerging Cyber Threats to Watch for in 2019
Online attackers are constantly developing new, innovative ways to break into the enterprise. This Dark Reading Tech Digest gives an in-depth look at five emerging attack trends and exploits your security team should look out for, along with helpful recommendations on how you can prevent your organization from falling victim.
Flash Poll
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2019-1840
PUBLISHED: 2019-04-18
A vulnerability in the DHCPv6 input packet processor of Cisco Prime Network Registrar could allow an unauthenticated, remote attacker to restart the server and cause a denial of service (DoS) condition on the affected system. The vulnerability is due to incomplete user-supplied input validation when...
CVE-2019-1841
PUBLISHED: 2019-04-18
A vulnerability in the Software Image Management feature of Cisco DNA Center could allow an authenticated, remote attacker to access to internal services without additional authentication. The vulnerability is due to insufficient validation of user-supplied input. An attacker could exploit this vuln...
CVE-2019-1826
PUBLISHED: 2019-04-18
A vulnerability in the quality of service (QoS) feature of Cisco Aironet Series Access Points (APs) could allow an authenticated, adjacent attacker to cause a denial of service (DoS) condition on an affected device. The vulnerability is due to improper input validation on QoS fields within Wi-Fi fra...
CVE-2019-1829
PUBLISHED: 2019-04-18
A vulnerability in the CLI of Cisco Aironet Series Access Points (APs) could allow an authenticated, local attacker to gain access to the underlying Linux operating system (OS) without the proper authentication. The attacker would need valid administrator device credentials. The vulnerability is due...
CVE-2019-1830
PUBLISHED: 2019-04-18
A vulnerability in Locally Significant Certificate (LSC) management for the Cisco Wireless LAN Controller (WLC) could allow an authenticated, remote attacker to cause the device to unexpectedly restart, which causes a denial of service (DoS) condition. The attacker would need to have valid administr...