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.

Endpoint

2/6/2019
04:00 PM
Connect Directly
Twitter
LinkedIn
Google+
RSS
E-Mail
50%
50%

Google Tackles Gmail Spam with Tensorflow

Tensorflow, Google's open-source machine learning framework, has been used to block 100 million spam messages.

Google reports Gmail is blocking 100 million extra spam emails per day following the implementation of Tensorflow, its open source, machine-learning framework, to supplement existing spam detection.

Machine learning isn't new to Gmail: Google has long been using machine-learning models and rule-based filters to detect spam, and its current protections have reportedly prevented more than 99.9% of spam, phishing, and malware from landing in Gmail inboxes. Today's attackers seek new ways to hit Gmail's 1.5 billion users and 5 million business clients with advanced threats.

Considering the size of Gmail's user base, 100 million extra messages doesn't seem like much. However, since it already blocks so much, the last remaining threats are toughest to identify.

Enter TensorFlow, an open source software library that developers can use to build artificial intelligence (AI) tools. It was developed by researchers and engineers from the Google Brain team within its AI division in 2015, and is used among companies including Google, Intel, SAP, Airbnb, and Qualcomm.

"We're now blocking spam categories that used to be very hard to detect," said Neil Kumaran, product manager for counter-abuse technology, in a blog post on the news.

TensorFlow protections complement Google's machine learning and rule-based protections to try and block the last 0.1% of spam emails from getting through. It supplements current detection by finding image-based messages, emails with hidden embedded content, and messages from newly created domains that may try to hide a low volume of spam emails within legitimate traffic.

Unlike rule-based spam filters, machine-learning models hunt for patterns in unwanted emails that people may not catch. Every email has thousands of defining signals, each of which can help determine whether it's legitimate. TensorFlow helps weed through the chaos and spot spammy emails that seem real, as well as emails that have spam-like qualities but are authentic.

Kumaran says TensorFlow also helps with personalizing spam protections for each user. The same email could be considered spam to one person but important information to another.

Applying machine learning at scale can be complex and time-consuming. Google is aiming to simplify the process with TensorFlow, which also adds the flexibility to train and experiment with different models at the same time in order to choose the most effective, instead of doing so one at a time.

Still, Gmail security will continue to pose a major challenge for Google. A new report shows how attackers are abusing "dots don't matter," a longstanding Gmail security feature, to create fraudulent accounts on websites and use variations of the same email address.

Confidential Computing: Google Buckles Down on Asylo
Google reports it's investing in confidential computing, which aims to secure applications and data in use, even from privileged access and cloud providers. In addition to today's Gmail news, Google has published an update on Asylo, an open source framework it introduced in May 2018 to simplify the process of creating and using enclaves on Google Cloud and other platforms.

The adoption of confidential computing has been slow going due to dependence on specific hardware, complexity around deployment, and lack of development tools to create and run applications in these environments. Asylo makes it easier to build applications that run in trusted execution environments (TEEs) with different platforms – for example, Intel SGX.

Google anticipates in the future Aslo will be integrated into developer pipelines, and users will able to launch Asylo apps directly from commercial marketplaces. However, confidential computing is still an emerging technology and enclaves lack established design practices.

To accelerate its use, Google is starting a Confidential Computing Challenge, a contest in which developers can create new use cases. Applicants have until April 1 to submit essays describing a novel use case for the tech.

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
44% of Security Threats Start in the Cloud
Kelly Sheridan, Staff Editor, Dark Reading,  2/19/2020
Zero-Factor Authentication: Owning Our Data
Nick Selby, Chief Security Officer at Paxos Trust Company,  2/19/2020
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-2020-9405
PUBLISHED: 2020-02-26
IBL Online Weather before 4.3.5a allows unauthenticated reflected XSS via the redirect page.
CVE-2020-9406
PUBLISHED: 2020-02-26
IBL Online Weather before 4.3.5a allows unauthenticated eval injection via the queryBCP method of the Auxiliary Service.
CVE-2020-9407
PUBLISHED: 2020-02-26
IBL Online Weather before 4.3.5a allows attackers to obtain sensitive information by reading the IWEBSERVICE_JSONRPC_COOKIE cookie.
CVE-2020-9398
PUBLISHED: 2020-02-25
ISPConfig before 3.1.15p3, when the undocumented reverse_proxy_panel_allowed=sites option is manually enabled, allows SQL Injection.
CVE-2015-5201
PUBLISHED: 2020-02-25
VDSM and libvirt in Red Hat Enterprise Virtualization Hypervisor (aka RHEV-H) 7-7.x before 7-7.2-20151119.0 and 6-6.x before 6-6.7-20151117.0 as packaged in Red Hat Enterprise Virtualization before 3.5.6 when VSDM is run with -spice disable-ticketing and a VM is suspended and then restored, allows r...