MIT report investigates the impact of deep learning on cybersecurity

There are a lot of buzzwords in the world of cybersecurity marketing. When an emerging concept hits a certain viral tipping point, it seems like all of the vendors are suddenly using the same buzzword — making everything even more confusing. Artificial intelligence and machine learning are omnipresent in cybersecurity marketing – and they are often confused with deep learning. a A recent report from the Massachusetts Institute of Technology It clarifies the distinction between the three, and emphasizes the value of deep learning for more effective cybersecurity.

MIT Technology Review Insights report “Deep learning provides proactive cyber defense‘, sponsored by Deep Instinct – a cybersecurity resource that has developed the world’s first and only purpose-built deep learning cybersecurity framework. Transformation in Executive Leadership This week with Lynne Bess, the former CEO of Palo Alto Networks and COO of Zscaler, who took over as CEO and Jay Caspi, the co-founder of Deep Instinct and the former CEO who succeeded Bess as Chairman and transition to Chief Product Officer – on a mission to prove that prevention Better than detection and response, and that deep learning is the factor that makes it possible.

Karen Crowley, director of solutions marketing for Deep Instinct, said, “This paper from MIT is important for industry to explain the key differences between machine learning and deep learning. There is a perception that all artificial intelligence [artificial intelligence] Equal, organizations need to understand the differences in the results they can achieve. Deep learning provides a game-changing methodology to prevent attacks before they are detected and responded.”

Artificial intelligence vs. machine learning vs. deep learning

The MIT report explains, “The terms ‘AI’, ‘machine learning’ and ‘deep learning’ are often confused. The technologies are separate but related. Artificial intelligence is a broad umbrella that encompasses a number of technologies, including machine learning and deep learning.” Machine learning is a subset of artificial intelligence, and deep learning is a subset of machine learning.”

In other words, it all falls under the term “artificial intelligence”, striving to emulate human intelligence or solve problems in some way. Machine learning goes one step further with a model that is able to learn and improve based on additional data. Deep learning takes machine learning to another level – adding a layered neural network that is able to operate with significantly larger volumes of structured and unstructured data for processing and learning at a significantly higher scale.

Prevention and Proactive Cyber ​​Security

It is important to understand the differences and not simply assume that all AI is created equal, though, because when it comes to cybersecurity, deep learning is able to offer benefits that the other two cannot match.

Much of the difference comes down to the data and how the different models are trained. Machine learning typically trains on about 2% of the data – focusing on things like headers and metadata. By contrast, deep learning absorbs 100% of the raw data.

The deep learning model understands both what good data looks like and what bad data looks like — and it does so at a much larger scale. Millions and millions of samples are fed to the neural network, enabling the model to have better context, greater accuracy in predicting behavior and proactively identifying threats with very few false positives.

Deep learning has proven particularly effective in combating ransomware. Once the ransomware payload has been implemented and the victim’s data is encrypted, it is too late. Finding and replying won’t help you at this point. You should be able to prevent ransomware in the first place. Deep learning enables the model to understand the DNA of an attack and accurately predict suspicious and malicious behavior. You don’t have to have seen this specific attack before, nor do you need to fully understand or sign how the attack works or expect the attack to follow a specific scenario. Being able to predict and prevent ransomware attacks before they are carried out is critical.

Merrill Sychek, Global Director of Cyber ​​Security for Honeywell, agreed that “deep learning is critical to cybersecurity to pre-empt attacks like ransomware.” “We need to beat the attackers at their game. Deep learning provides this opportunity by understanding the DNA of files and immediately determining if there is malicious intent before it can descend and infiltrate the environment.”

point proof

Deep Instinct understands that there is a lot of confusion and misinformation to contend with – both for deep learning relative to other AI models, and for the concept of prevention rather than the prevailing mantra of detection and response.

The MIT report is an example of Deep Instinct’s quest to demonstrate the value of deep learning and educate the market, but it’s not the first. Deep instinct too lately Works with unit 221 b To conduct comprehensive, independent testing to assess threat prevention capabilities.

Deep Instinct beat this assessment brilliantly – turning 221B CEO Lance James from skeptic to believer. The 221B team threw everything they had into Deep Instinct – including custom ransomware using proprietary technologies – and Deep Instinct stopped them all.

Take a look at MIT report and results Unit Rating 221 B And decide for yourself. Perhaps deep learning can break the supposed abuse mentality and effectively help organizations prevent cyberattacks rather than just trying to detect and respond to them faster.

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