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Turns out converting files into images is a highly effective way to detect malware

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A branch of artificial intelligence called machine learning is all around us. It's employed by Facebook to help curate content (and target us with ads), Google uses it to filter millions of spam messages each day, and it's part of what enabled the OpenAI bot to beat the reigning Dota 2 champions last year in two out of three matches. There are seemingly endless uses. Adding one more to the pile, Microsoft and Intel have come up with a clever machine learning framework that is surprisingly accurate at detecting malware through a grayscale image conversion process. Microsoft detailed the technology in a blog post (via ZDNet), which it calls static malware-as-image network analysis, or STAMINA.


How to use deep learning AI to detect and prevent malware and APTs in real-time

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This column is available in a weekly newsletter called IT Best Practices. The number of new malware variations that pop up each day runs somewhere between 390,000 (according to AV-TEST Institute) and one million (according to Symantec Corporation). These are new strains of malware that have not been seen in the wild before. Even if we consider just the low end figure, the situation is still dire. Especially when it comes to advanced persistent threats (APTs), which are the most sophisticated mutations of viruses and malware, which are very effective at going completely undetected by many of the cybersecurity technologies in use today.


Computer vision and deep learning provide new ways to detect cyber threats

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This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. The last decade's growing interest in deep learning was triggered by the proven capacity of neural networks in computer vision tasks. If you train a neural network with enough labeled photos of cats and dogs, it will be able to find recurring patterns in each category and classify unseen images with decent accuracy. What else can you do with an image classifier? In 2019, a group of cybersecurity researchers wondered if they could treat security threat detection as an image classification problem.


Teaching AI to detect malware, one data set at a time

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On Tuesday, for example, 34 companies including Microsoft, Oracle and Facebook signed the Cybersecurity Tech Accord, publicly committing to protect internet users, work together and improve resilience in the space. But outside of large-scale initiatives, the basics, such as malware detection, have a long road ahead as the cyberattacks keep rolling in. Advancements in AI and ML on the enterprise side are important to counter hackers also utilizing the technology to automate attacks. The most effective kind of malware is a strain that hits without a business ever knowing, but advanced detection capabilities harnessing AI and ML are steadily helping cybersecurity teams overcome the odds. But without good data, good defensive and detection measures are hard to build.


Computer vision can help spot cyber threats with startling accuracy

#artificialintelligence

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. The last decade's growing interest in deep learning was triggered by the proven capacity of neural networks in computer vision tasks. If you train a neural network with enough labeled photos of cats and dogs, it will be able to find recurring patterns in each category and classify unseen images with decent accuracy. What else can you do with an image classifier? In 2019, a group of cybersecurity researchers wondered if they could treat security threat detection as an image classification problem.