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
GitHub Named in Capital One Breach Lawsuit
Dark Reading Staff 8/14/2019
The Mainframe Is Seeing a Resurgence. Is Security Keeping Pace?
Ray Overby, Co-Founder & President at Key Resources, Inc.,  8/15/2019
The Flaw in Vulnerability Management: It's Time to Get Real
Jim Souders, Chief Executive Officer at Adaptiva,  8/15/2019
Register for Dark Reading Newsletters
White Papers
Video
Cartoon Contest
Current Issue
7 Threats & Disruptive Forces Changing the Face of Cybersecurity
This Dark Reading Tech Digest gives an in-depth look at the biggest emerging threats and disruptive forces that are changing the face of cybersecurity today.
Flash Poll
The State of IT Operations and Cybersecurity Operations
The State of IT Operations and Cybersecurity Operations
Your enterprise's cyber risk may depend upon the relationship between the IT team and the security team. Heres some insight on what's working and what isn't in the data center.
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2019-5034
PUBLISHED: 2019-08-20
An exploitable information disclosure vulnerability exists in the Weave Legacy Pairing functionality of Nest Cam IQ Indoor version 4620002. A set of specially crafted weave packets can cause an out of bounds read, resulting in information disclosure. An attacker can send packets to trigger this vuln...
CVE-2019-5035
PUBLISHED: 2019-08-20
An exploitable information disclosure vulnerability exists in the Weave PASE pairing functionality of the Nest Cam IQ Indoor, version 4620002. A set of specially crafted weave packets can brute force a pairing code, resulting in greater Weave access and potentially full device control. An attacker c...
CVE-2019-5036
PUBLISHED: 2019-08-20
An exploitable denial-of-service vulnerability exists in the Weave error reporting functionality of the Nest Cam IQ Indoor, version 4620002. A specially crafted weave packets can cause an arbitrary Weave Exchange Session to close, resulting in a denial of service. An attacker can send a specially cr...
CVE-2019-8103
PUBLISHED: 2019-08-20
Adobe Acrobat and Reader versions, 2019.012.20035 and earlier, 2019.012.20035 and earlier, 2017.011.30142 and earlier, 2017.011.30143 and earlier, 2017.011.30142 and earlier, 2015.006.30497 and earlier, and 2015.006.30498 and earlier have an out-of-bounds read vulnerability. Successful exploitation ...
CVE-2019-8104
PUBLISHED: 2019-08-20
Adobe Acrobat and Reader versions, 2019.012.20035 and earlier, 2019.012.20035 and earlier, 2017.011.30142 and earlier, 2017.011.30143 and earlier, 2017.011.30142 and earlier, 2015.006.30497 and earlier, and 2015.006.30498 and earlier have an out-of-bounds read vulnerability. Successful exploitation ...