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.

Application Security

10/18/2017
04:08 PM
Curtis Franklin
Curtis Franklin
Curt Franklin
50%
50%

McAfee Brings AI to Security With New Products

McAfee has announced new products at MPOWER - products that bring AI and machine learning to security analytics.

McAfee, at its first MPOWER event since leaving the Intel corporate fold, has made a series of product announcements focused on analytics and security. The new Investigator product takes the lead, with additional products announced for SOCs, endpoints and cloud platforms.

McAfee Investigator is an analytics platform that, according to McAfee, includes both artifical intelligence and machine learning components in order to bring clarity to the mass of data accumulating in most security operations centers on a daily basis. In the statement announcing Investigator, McAfee said that the servie is designed to provide more accurate threat prioritization; faster, more thorough malware investigation; and increased SOC efficiency. In all of these areas, investigator is intended to work in concert with human security analysts rather than as a replacement for them.

McAfee's AI functions are able to take security data and analyze it, prioritizing the important information found inside the data and then providing that information to analysts in a visual format intended to make it faster and easier for humans to respond. In particular, Investigator's analysis should allow security professionals to quickly understand which information is important and what might be done with it.

Artificial intelligence and machine learning are also taking their places in McAfee's endpoint protection. The company announced that new capabilities for McAfee endpoint software and services include decision-making with deep learning, pre- and post-execution machine learning and machine learning with reach.

In pre- and post-execution machine learning, McAfee endpoint machine learning reviews files before and after they execute, comparing the two and gaining knowledge with new data. The knowledge gained is amplified by what McAfee is calling machine learning with reach: 300 million sensors around the world serve as a source to inform algorithms. The additional data should allow the AI to reach more accurate conclusions far faster than would be possible with the data from a single system (or networ) alone.

In the final major announcements of the morning, McAfee dove into the cloud by announcing the new McAfee Cloud Workload Security which is intended to bring hybrid cloud architectures into the McAfee ePolicy Orchestrator fold with increased protection and visibility, and the availability of McAfee Web Gateway to AWS.

McAfee's MPOWER conference runs through the rest of the week; more announcements are anticipated during the conference.

Related posts:

— Curtis Franklin is the editor of SecurityNow.com. Follow him on Twitter @kg4gwa.

Comment  | 
Print  | 
More Insights
Comments
Newest First  |  Oldest First  |  Threaded View
COVID-19: Latest Security News & Commentary
Dark Reading Staff 9/25/2020
Hacking Yourself: Marie Moe and Pacemaker Security
Gary McGraw Ph.D., Co-founder Berryville Institute of Machine Learning,  9/21/2020
Startup Aims to Map and Track All the IT and Security Things
Kelly Jackson Higgins, Executive Editor at Dark Reading,  9/22/2020
Register for Dark Reading Newsletters
White Papers
Video
Cartoon
Current Issue
Special Report: Computing's New Normal
This special report examines how IT security organizations have adapted to the "new normal" of computing and what the long-term effects will be. Read it and get a unique set of perspectives on issues ranging from new threats & vulnerabilities as a result of remote working to how enterprise security strategy will be affected long term.
Flash Poll
How IT Security Organizations are Attacking the Cybersecurity Problem
How IT Security Organizations are Attacking the Cybersecurity Problem
The COVID-19 pandemic turned the world -- and enterprise computing -- on end. Here's a look at how cybersecurity teams are retrenching their defense strategies, rebuilding their teams, and selecting new technologies to stop the oncoming rise of online attacks.
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2020-15208
PUBLISHED: 2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can ...
CVE-2020-15209
PUBLISHED: 2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a `nullptr` buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one....
CVE-2020-15210
PUBLISHED: 2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and ...
CVE-2020-15211
PUBLISHED: 2020-09-25
In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices f...
CVE-2020-15212
PUBLISHED: 2020-09-25
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `outpu...