Deep Learning: It's Not Too Good to Be True - It's Genuine Progress

Deep learning has come on leaps and bounds in recent years and is fast-tracked to becoming one of the most revolutionary technologies in cybersecurity. As a subset of artificial intelligence (AI), deep learning is often confused with associated technologies like machine learning (ML). Still, its capabilities go above and beyond these less advanced solutions.

While some have previously turned their noses up at deep learning, most are now recognizing the future of the technology and the value it holds in preventative approaches to cybersecurity. Everything must start somewhere. After all, Netflix, Spotify and Tesla were all disregarded at some point in their development. Yet, they are now some of the most well-known billion-dollar brands using deep learning in the world.

As threats against organizations continue to rise, especially adversarial AI and ransomware, deep learning and its prevention mantra is crucial. It’s time for businesses to look beyond the market hype and recognize the genuine progress being made in front of them.

What Makes Deep Learning Different?

The first thing to clarify is that deep learning is not the same as ML. Yes, they are both subsets of AI, but that is where the likeness ends. ML relies far more on human interaction and uses manual, supervised datasets. While this technology ultimately cuts down the number of human resources allocated to its maintenance, it still requires workers to input data over time. Furthermore, by using pre-classified, engineered data, ML is far more susceptible to compromise.

Deep learning is designed to bypass these known weaknesses of ML. Imitating the model of a human brain, deep learning runs off raw datasets and independently learns to recognize malicious code, meaning it can later identify incoming attacks before they get the chance to land. While this technology requires huge amounts of raw data during the ‘training’ stages, ultimately, the more data fed into the system, the smarter it will become.

Why Deep Learning Succeeds Where Others Fail

There has been a hype around ML and its revolutionary capabilities for years, but this has lit up a beacon for cyber-criminals. Now, they’ve cracked the code. Threat actors can now hijack business ML systems, tamper with the datasets before input and reverse the malicious and benign labels, causing endless havoc for the organization. They can also go one step further and create backdoors to networks, giving them easy access to the system whenever they want.

By learning on large volumes of raw data, deep learning is far more resistant to any form of compromise and manipulation. Whereas ML can only process and recognize the malicious and benign code input by workers, deep learning instinctively learns over time and facilitates a more preventative approach to cybersecurity, rather than a reactive one.

Deep learning is not a new concept. It has existed for many years but has only recently made its way into the mainstream thanks to advancements in technology such as graphics processing units (GPU). Now, world-renowned companies like Amazon, Tesla and Google are investing in deep learning to strengthen and further their capabilities, with other use cases including self-driving cars and revolutionary medical research. These companies are using some of the six deep learning frameworks that exist in the world today. However, we are the only ones using the cybersecurity framework.

Prevention Over Mitigation 

Most cybersecurity solutions today are focused on mitigating the effects of a breach rather than preventing them from happening in the first place. With deep learning, businesses can finally move away from the traditional detection and response procedures and prevent attacks before they take place. After all, you want security protection to take place before the burglar enters your home, not after they’ve started ransacking your living room.

Deep learning is designed to integrate with existing security stacks so organizations avoid the hassle of replacing existing technology. As well as strengthening the company security posture, deep learning helps streamline processes and frees up employee time. For example, once integrated, the technology reduces the number of alerts received by the security team each week by 25% or more. Therefore, less time is wasted on false positives.

Not Hype, but the Future

The market is constantly flooded with news of revolutionary tech that will save the day. Let’s face it; the cybersecurity landscape will remain as unpredictable as ever. Yet, deep learning greatly improves a business’ chances against incoming threats. Unfortunately, deep learning is quickly becoming a market buzzword like so many other valuable technologies. It’s vital that organizations avoid this misleading hype by grasping what this technology is truly capable of and how it works.

Typically, when something seems too good to be true, it usually is. Yet, not when it comes to deep learning.

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