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.

Black Hat USA
July 31 - August 5, 2021
Las Vegas, NV, USA
SecTor
November 4 - October 30, 2021
Toronto, ON, Canada
Black Hat Europe
November 8-11, 2021
Virtual Event
5/29/2019
09:00 AM
Black Hat Staff
Black Hat Staff
Event Updates
50%
50%

Level Up Your Data Forensics Game at Black Hat USA

Learn about the latest supply chain attacks, red team threats, and "deep fake" detection tricks at the premier cybersecurity event in Las Vegas this August.

The threat landscape is always shifting in cybersecurity and there’s no better place to check in with colleagues and learn the latest tricks and techniques than Black Hat USA in Las Vegas.

Attend this premier cybersecurity event and check out the Briefings Data Forensics & Incident Response track which offers techniques that will help you understand how an attack unfolded, if and when a breach occurred, and how it can be prevented in the future. Fantastic Red-Team Attacks and How to Find Them promises to reveal prevalent and ongoing gaps across organizations uncovered by testing defenses against a broad spectrum of attacks via Red Canary’s Atomic Red Team testing framework. Plus, you’ll learn the open-sourced Event Query Language for creating high signal-to-noise analytics. In a live demonstration, you’ll see how these powerful but easy-to-craft analytics can catch adversarial behaviors that are commonly missed in organizations today.

In Detecting Deep Fakes with Mice you’ll see how researchers worked to train different machines and creatures to detect real vs. fake speech in “deep fake” videos. For machines, you’ll look at two approaches based on machine learning: one based on game theory called generative adversarial networks (GAN), and one based on mathematical depth-wise convolutional neural networks (Xception).

For biological systems, researchers gathered a broad range of human subjects as well as mice, which don’t understand the words, but respond to the stimulus of sounds and can be trained to recognize real vs. fake phonetic construction. Researchers theorize that this may be advantageous in detecting the subtle signals of improper audio manipulation, without being swayed by the semantic content of the speech. In this 25-Minute Briefing you’ll learn how they evaluated the relative performance of all four discriminator groups (GAN, Xception, humans, and mice) using a "deep fakes" data set recently published by Google.

The Enemy Within: Modern Supply Chain Attacks take you behind the scenes of today’s cloud-powered industries with a Microsoft security expert. You’ll learn about previously undisclosed supply chain attacks including techniques and objectives of adversaries, mechanisms that were effective in blunting attacks, and the sometimes-comical challenges of dealing with one of the most complex assets to defend: developers.

For more information about these Briefings and many more check out the Black Hat USA Briefings page, which is regularly updated with new content as we get closer to the event!

Black Hat USA will return to the Mandalay Bay in Las Vegas August 3-8, 2019. For more information on what’s happening at the event and how to register, check out the Black Hat website.

Comment  | 
Print  | 
More Insights
Comments
Oldest First  |  Newest First  |  Threaded View
Commentary
Ransomware Is Not the Problem
Adam Shostack, Consultant, Entrepreneur, Technologist, Game Designer,  6/9/2021
Edge-DRsplash-11-edge-ask-the-experts
How Can I Test the Security of My Home-Office Employees' Routers?
John Bock, Senior Research Scientist,  6/7/2021
News
New Ransomware Group Claiming Connection to REvil Gang Surfaces
Jai Vijayan, Contributing Writer,  6/10/2021
Register for Dark Reading Newsletters
White Papers
Video
Cartoon Contest
Write a Caption, Win an Amazon Gift Card! Click Here
Latest Comment: This gives a new meaning to blind leading the blind.
Current Issue
The State of Cybersecurity Incident Response
In this report learn how enterprises are building their incident response teams and processes, how they research potential compromises, how they respond to new breaches, and what tools and processes they use to remediate problems and improve their cyber defenses for the future.
Flash Poll
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2020-9493
PUBLISHED: 2021-06-16
A deserialization flaw was found in Apache Chainsaw versions prior to 2.1.0 which could lead to malicious code execution.
CVE-2021-28815
PUBLISHED: 2021-06-16
Insecure storage of sensitive information has been reported to affect QNAP NAS running myQNAPcloud Link. If exploited, this vulnerability allows remote attackers to read sensitive information by accessing the unrestricted storage mechanism. This issue affects: QNAP Systems Inc. myQNAPcloud Link vers...
CVE-2021-3535
PUBLISHED: 2021-06-16
Rapid7 Nexpose is vulnerable to a non-persistent cross-site scripting vulnerability affecting the Security Console's Filtered Asset Search feature. A specific search criterion and operator combination in Filtered Asset Search could have allowed a user to pass code through the provided search field. ...
CVE-2021-32685
PUBLISHED: 2021-06-16
tEnvoy contains the PGP, NaCl, and PBKDF2 in node.js and the browser (hashing, random, encryption, decryption, signatures, conversions), used by TogaTech.org. In versions prior to 7.0.3, the `verifyWithMessage` method of `tEnvoyNaClSigningKey` always returns `true` for any signature that has a SHA-5...
CVE-2021-32623
PUBLISHED: 2021-06-16
Opencast is a free and open source solution for automated video capture and distribution. Versions of Opencast prior to 9.6 are vulnerable to the billion laughs attack, which allows an attacker to easily execute a (seemingly permanent) denial of service attack, essentially taking down Opencast using...