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Using Machine Learning to Guess PINs from Video

Researchers trained a machine-learning system on videos of people typing their PINs into ATMs:

By using three tries, which is typically the maximum allowed number of attempts before the card is withheld, the researchers reconstructed the correct sequence for 5-digit PINs 30% of the time, and reached 41% for 4-digit PINs.

This works even if the person is covering the pad with their hands.

The article doesn’t contain a link to the original research. If someone knows it, please put it in the comments.

Slashdot thread.

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Hiding Malware in ML Models

Interesting research: “EvilModel: Hiding Malware Inside of Neural Network Models”.

Abstract: Delivering malware covertly and detection-evadingly is critical to advanced malware campaigns. In this paper, we present a method that delivers malware covertly and detection-evadingly through neural network models. Neural network models are poorly explainable and have a good generalization ability. By embedding malware into the neurons, malware can be delivered covertly with minor or even no impact on the performance of neural networks. Meanwhile, since the structure of the neural network models remains unchanged, they can pass the security scan of antivirus engines. Experiments show that 36.9MB of malware can be embedded into a 178MB-AlexNet model within 1% accuracy loss, and no suspicious are raised by antivirus engines in VirusTotal, which verifies the feasibility of this method. With the widespread application of artificial intelligence, utilizing neural networks becomes a forwarding trend of malware. We hope this work could provide a referenceable scenario for the defense on neural network-assisted attacks.

News article.

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The Future of Machine Learning and Cybersecurity

The Center for Security and Emerging Technology has a new report: “Machine Learning and Cybersecurity: Hype and Reality.” Here’s the bottom line:

The report offers four conclusions:

  • Machine learning can help defenders more accurately detect and triage potential attacks. However, in many cases these technologies are elaborations on long-standing methods — not fundamentally new approaches — that bring new attack surfaces of their own.
  • A wide range of specific tasks could be fully or partially automated with the use of machine learning, including some forms of vulnerability discovery, deception, and attack disruption. But many of the most transformative of these possibilities still require significant machine learning breakthroughs.
  • Overall, we anticipate that machine learning will provide incremental advances to cyber defenders, but it is unlikely to fundamentally transform the industry barring additional breakthroughs. Some of the most transformative impacts may come from making previously un- or under-utilized defensive strategies available to more organizations.
  • Although machine learning will be neither predominantly offense-biased nor defense-biased, it may subtly alter the threat landscape by making certain types of strategies more appealing to attackers or defenders.

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The Supreme Court Narrowed the CFAA

In a 6-3 ruling, the Supreme Court just narrowed the scope of the Computer Fraud and Abuse Act:

In a ruling delivered today, the court sided with Van Buren and overturned his 18-month conviction.

In a 37-page opinion written and delivered by Justice Amy Coney Barrett, the court explained that the “exceeds authorized access” language was, indeed, too broad.

Justice Barrett said the clause was effectively making criminals of most US citizens who ever used a work resource to perform unauthorized actions, such as updating a dating profile, checking sports scores, or paying bills at work.

What today’s ruling means is that the CFAA cannot be used to prosecute rogue employees who have legitimate access to work-related resources, which will need to be prosecuted under different charges.

The ruling does not apply to former employees accessing their old work systems because their access has been revoked and they’re not “authorized” to access those systems anymore.

More.

It’s a good ruling, and one that will benefit security researchers. But the confusing part is footnote 8:

For present purposes, we need not address whether this inquiry turns only on technological (or “code-based”) limitations on access, or instead also looks to limits contained in contracts or policies.

It seems to me that this is exactly what the ruling does address. The court overturned the conviction because the defendant was not limited by technology, but only by policies. So that footnote doesn’t make any sense.

I have written about this general issue before, in the context of adversarial machine learning research.

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