Cybersecurity professionals are constantly looking for new and innovative ways to stay one step ahead of attackers. Yet in the first quarter of 2022 alone, there were 404 publicly reported data breaches in the U.S.—a 14% increase compared to the first quarter of 2021, according to the Identity Theft Resource Center. Of particular concern is the alarming rise in ransomware breaches, which increased by 13% in a single year—representing a jump greater than the past five years combined, according to the 2022 Verizon Data Breach Investigations Report (DBIR).

No wonder an increasing number of organizations are beginning to explore how deep learning, and its ability to mimic the human brain, can outsmart and outpace the world’s fastest and dangerous cyber threats.

The most advanced form of artificial intelligence (AI) technology, and a type of machine learning, deep learning uses neural networks to instinctively and autonomously anticipate and prevent unknown malware and zero-day attacks before they can wreak havoc on an IT environment.

Most cybersecurity technologies, such as endpoint detection and response (EDR) solutions, simply identify, track, record, and contain a threat once it has already entered an environment. Machine learning-based cybersecurity solutions are also an essential part of any security strategy, and use pre-labelled data, classified as either benign or malicious, to detect dangerous patterns.

But neither set of cybersecurity solutions can proactively defend against sophisticated attacks without constant human tweaking. Fortunately, deep learning can mimic the functionality and connectivity of neurons in the human brain, enabling neural networks to independently learn from raw and un-curated data and automatically recognize unknown threats.

“Deep learning is the only family of algorithms that works on raw data to identify cybersecurity threats with unmatched speed and accuracy,” says Guy Caspi, CEO of Deep Instinct, a cybersecurity company.

The result is a powerful solution that can accurately identify highly sophisticated attack patterns at record speeds.  

Time for a different line of defense

Although deep learning has been around since the 1940s, the high cost and complexity of graphics processing units (GPUs) have kept the technology out of reach for many organizations. But that’s changing with the increasing processing power and lower costs of graphics chips.

The timing couldn’t be better. The increasing availability of ransomware-as-a-service offerings, such as ransomware kits and target lists, are making it easier than ever for bad actors—even those with limited experience—to launch a ransomware attack, causing crippling damage in the very first moments of infection. Other sophisticated attackers use targeted strikes, in which the ransomware is placed inside the network to trigger on command.

Another cause for concern is the increasing disappearance of an IT environment’s perimeter as cloud compute storage and resources move to the edge. Today’s organizations must secure endpoints or entry points of end-user devices, such as desktops, laptops, and mobile devices, from being exploited by malicious hackers—a challenging feat, according to Michael Suby,  research vice president, security and trust, at IDC. “Attacks continue to evolve, as do the endpoints themselves and the end users who utilize their devices,” he says. “These dynamic circumstances create a trifecta for bad actors to enter and establish a presence on any endpoint and use that endpoint to stage an attack sequence.”

The increased pace of high-profile threats (e.g., ransomware) is up to

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By: MIT Technology Review Insights
Title: Deep learning delivers proactive cyber defense
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Published Date: Wed, 20 Jul 2022 14:53:34 +0000

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