Showing only posts tagged machine learning. Show all posts.

Attacking the Performance of Machine Learning Systems

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Interesting research: “ Sponge Examples: Energy-Latency Attacks on Neural Networks “: Abstract: The high energy costs of neural network training and inference led to the use of acceleration hardware such as GPUs and TPUs. While such devices enable us to train large-scale neural networks in datacenters and deploy them on edge …

Manipulating Machine-Learning Systems through the Order of the Training Data

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Yet another adversarial ML attack: Most deep neural networks are trained by stochastic gradient descent. Now “stochastic” is a fancy Greek word for “random”; it means that the training data are fed into the model in random order. So what happens if the bad guys can cause the order …

Using Radar to Read Body Language

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Yet another method of surveillance : Radar can detect you moving closer to a computer and entering its personal space. This might mean the computer can then choose to perform certain actions, like booting up the screen without requiring you to press a button. This kind of interaction already exists …

How to improve visibility into AWS WAF with anomaly detection

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When your APIs are exposed on the internet, they naturally face unpredictable traffic. AWS WAF helps protect your application’s API against common web exploits, such as SQL injection and cross-site scripting. In this blog post, you’ll learn how to automatically detect anomalies in the AWS WAF metrics …

Hiding Malware in ML Models

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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 …

The Future of Machine Learning and Cybersecurity

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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 …

7 ways to improve security of your machine learning workflows

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In this post, you will learn how to use familiar security controls to build more secure machine learning (ML) workflows. The ideal audience for this post includes data scientists who want to learn basic ways to improve security of their ML workflows, as well as security engineers who want …

2021 Cybersecurity Trends: Bigger Budgets, Endpoint Emphasis and Cloud

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Insider threats are redefined in 2021, the work-from-home trend will continue define the threat landscape and mobile endpoints become the attack vector of choice, according 2021 forecasts. [...]

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