According to foreign policy experts and the defense establishment, the United States is caught in an artificial intelligence arms race with China — one with serious implications for national security. The conventional version of this story suggests that the United States is at a disadvantage because of self-imposed restraints on the collection of data and … Read More “Data, Surveillance, and the AI Arms Race” »
Category: machinelearning
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Yesterday, I visited the NSA. It was Cyber Command’s birthday, but that’s not why I was there. I visited as part of the Berklett Cybersecurity Project, run out of the Berkman Klein Center and funded by the Hewlett Foundation. (BERKman hewLETT — get it? We have a web page, but it’s badly out of date.) … Read More “Visiting the NSA” »
Nice bit of adversarial machine learning. The image from this news article is most of what you need to know, but here’s the research paper. Powered by WPeMatico
In what I am sure is only a first in many similar demonstrations, researchers are able to add or remove cancer signs from CT scans. The results easily fool radiologists. I don’t think the medical device industry has thought at all about data integrity and authentication issues. In a world where sensor data of all … Read More “Maliciously Tampering with Medical Imagery” »
Researchers have been able to fool Tesla’s autopilot in a variety of ways, including convincing it to drive into oncoming traffic. It requires the placement of stickers on the road. Abstract: Keen Security Lab has maintained the security research work on Tesla vehicle and shared our research results on Black Hat USA 2017 and 2018 … Read More “Adversarial Machine Learning against Tesla’s Autopilot” »
No one doubts that artificial intelligence (AI) and machine learning (ML) will transform cybersecurity. We just don’t know how, or when. While the literature generally focuses on the different uses of AI by attackers and defenders and the resultant arms race between the two I want to talk about software vulnerabilities. All software … Read More “Machine Learning to Detect Software Vulnerabilities” »
Researchers are able to create fake fingerprints that result in a 20% false-positive rate. The problem is that these sensors obtain only partial images of users’ fingerprints — at the points where they make contact with the scanner. The paper noted that since partial prints are not as distinctive as complete prints, the chances of … Read More “Using Machine Learning to Create Fake Fingerprints” »
James Mickens gave an excellent keynote at the USENIX Security Conference last week, talking about the social aspects of security — racism, sexism, etc. — and the problems with machine learning and the Internet. Worth watching. Powered by WPeMatico
Fascinating research de-anonymizing code — from either source code or compiled code: Rachel Greenstadt, an associate professor of computer science at Drexel University, and Aylin Caliskan, Greenstadt’s former PhD student and now an assistant professor at George Washington University, have found that code, like other forms of stylistic expression, are not anonymous. At the DefCon … Read More “Identifying Programmers by their Coding Style” »
Really interesting article: A trained eye (or even a not-so-trained one) can discern when something phishy is going on with a domain or subdomain name. There are search tools, such as Censys.io, that allow humans to specifically search through the massive pile of certificate log entries for sites that spoof certain brands or functions common … Read More “Detecting Phishing Sites with Machine Learning” »