Threat Intelligence

6/13/2017
06:15 PM
Connect Directly
Twitter
LinkedIn
Google+
RSS
E-Mail
50%
50%

How Bad Data Alters Machine Learning Results

Machine learning models tested on single sources of data can prove inaccurate when presented with new sources of information.

The effectiveness of machine learning models may vary between the test phase and their use "in the wild" on actual consumer data.

Many research papers claim high rates of malware detection and false positives with machine learning, and often deep learning, models. However, nearly all of these rates are within the context of a single source of data, which authors use to train and test their models.

Machine learning has become more advanced but isn't used enough yet in security, says Hillary Sanders, data scientist for Sophos' data science research group. She anticipates usage will increase in coming years to address the rise of different forms of malware.

Historically, Sanders explains, static signatures have been used to detect malware. This method doesn't scale well because software needs to be updated with new signatures as more malware is created. Machine learning and deep learning automatically generate more flexible patterns, which could better detect malicious content compared with stricter static signatures.

"This enables us to move away from signature detection and more toward deep learning detection, which doesn't really require signatures and is going to be better at detecting malware that has never been seen before," she says.

The challenge is in creating a deep learning model to detect forms of malware that don't yet exist. Sanders explains the problem of using current data to test these models, which would ideally be used to detect future malware strains in different clients and environments.

"We can't be sure the data we trained on is going to be super similar to the data in organization deployment," she explains. "If we're training on data that isn't like the data we want to eventually test on, our model might fail catastrophically."

In current machine learning research, accuracy estimates don't consider how systems will process future data. Sanders says modern publications lack time-decay analysis and sensitivity analysis, which could lead to a lack of trust among those who rely on this information.

"If researchers forget to focus on sensitivity testing and time decay, our models are liable to fail catastrophically in the wild," she explains.

Time-decay analysis simulates how the accuracy of data decreases over time, she explains. Consider a dataset with information from January through April. If a machine learning model is trained on data before February 1, it will do well on processing data from January, but accuracy will begin to decay after February.

Sensitivity analysis tweaks inputs for machine learning models to see how output is affected. Sanders will present sensitivity results in her presentation titled "Garbage In Garbage Out: How Purportedly Great Machine Learning Models Can Be Screwed Up By Bad Data" at this year's Black Hat USA conference in Las Vegas.

This analysis will include a deep learning model designed to detect malicious URLs, which was trained and tested using three sources of URL data. As part of her discussion, she'll dive into what caused the results by focusing on how the data sources are different, and higher-level feature activations the neural net identified in some datasets but not in others.

For security teams, the end goal with deep learning is to stop malware. If training and testing data is biased compared with real-world data, models are likely to miss out.

"You ignore the thing you could be optimizing for," says Sanders. "You could miss swaths of malware."

Black Hat USA returns to the fabulous Mandalay Bay in Las Vegas, Nevada, July 22-27, 2017. Click for information on the conference schedule and to register.

 

Related Content:

Kelly Sheridan is the Staff Editor at Dark Reading, where she focuses on cybersecurity news and analysis. She is a business technology journalist who previously reported for InformationWeek, where she covered Microsoft, and Insurance & Technology, where she covered financial ... View Full Bio

Comment  | 
Print  | 
More Insights
Comments
Newest First  |  Oldest First  |  Threaded View
When Your Sandbox Fails
Kowsik Guruswamy, Chief Technology Officer at Menlo Security,  4/11/2019
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...