How Deep Learning is Flipping the Script on Cyber Threats
Even by the rapid pace of the IT industry, artificial intelligence (AI) is an extremely fast-moving technology. While the concept has been around for decades, recent advances in processing power mean that we are seeing constant innovations in the field. With the technology continually reinventing itself, it’s no wonder that…
Even by the rapid pace of the IT industry, artificial intelligence (AI) is an extremely fast-moving technology. While the concept has been around for decades, recent advances in processing power mean that we are seeing constant innovations in the field. With the technology continually reinventing itself, it’s no wonder that many in the business world are struggling to keep up with the latest developments.
The blanket use of “AI” as a popular marketing buzzword has made it harder to distinguish different branches of technology. For example, most of the current security solutions described as AI should more accurately be called machine learning (ML). But one of the most recent developments in the field is deep learning, a more advanced model that is already transforming the cybersecurity landscape.
Business decision-makers need to know the difference and do their due diligence if they are to invest in the best solutions to protect their operations.
So, what’s the difference between machine learning and deep learning?
ML solutions are programmes that have been trained to identify patterns and links. This is achieved through feature engineering, where the tool is manually fed datasets to learn the difference between benign and malicious activity.
As such, ML is increasingly used to help analyse incoming threat data and deal with more routine, expected threats, providing a much-needed boost to struggling security teams. However, the manual aspect of their training means they are limited to smaller data samples. This results in a loss of accuracy and finesse, which means ML tools often struggle with unknown threats such as zero days. What’s more, traditional ML cannot be trained to recognise a threat that hasn’t been seen before.
Many attacks are now able to execute before traditional ML tools notice the threat. For example, the fastest ransomware can begin encrypting files just 15 seconds after activation.
Threat actors have also learned to trick ML tools by feeding them false data that will cause them to misclassify threats as benign activity, a process known as “adversarial AI”.
Contrasting this is deep learning, which isn’t a new concept but has only recently made the transition into the mainstream due to the advancement of factors like processing power. Leading tech firms including Google, Tesla and Amazon are investing heavily in the technology, with applications spanning medical research and self-driving vehicles, to analysing consumer buying and media behaviour.
Deep learning learns in a much more complex way than traditional systems via a deep neural network inspired by the human brain. The neural network is left to process large quantities of raw, unlabelled data which it then determines as malicious or benign. This is a more time-intensive process initially, but the resulting complexity and speed are more than worth it. Because deep learning tools learn independently, they can process vast amounts of data compared to traditional ML tools that require manual input.
As a result, the technology is not only able to accurately identify more complex patterns than traditional ML, but it operates at exponentially higher speeds. The bleeding edge of the technology can detect and block malware in just 20 milliseconds.
Why is deep learning valuable for stopping cyber attacks?
While creating a deep learning solution is extremely complex, you don’t need a PhD to operate the end result, and most solutions have a strong focus on usability. This means there is a huge potential for businesses to start integrating the technology into their security stack.
The exponential boost to accuracy and speed delivered by deep learning has a tremendous impact on cybersecurity. High-level solutions reliably spot even the most advanced malware before it even has a chance to enter the IT environment.
This means sophisticated threat actors that have grown used to running rings around traditional security solutions will suddenly find their attack paths blocked. At this speed, we effectively move from prevention to prediction as attacks are stopped before they can truly begin. This is particularly valuable when it comes to combating ransomware attacks, which are often centred around encrypting as much as possible before they are caught and stopped.
The more complex learning process of deep learning also means it cannot be easily manipulated by adversarial AI techniques in the same way as standard ML solutions.
Integrating a deep learning solution into an existing security stack also helps to greatly reduce the number of incoming alerts a security team needs to deal with. This means analysts can stop wasting their time slogging through low-level alerts and false positives which is particularly valuable when you consider many teams are spending more time on false alarms than real threats.
What does this mean for the future of the cybersecurity industry?
Deep learning has the potential to revolutionise how we identify and defend against threats. The technology’s analytic accuracy speed means that incoming threats can be identified before they arrive, shifting the cyber battleground outside of the IT infrastructure. This is a stark contrast to the current status quo of security teams desperately racing against threats within their network.
We’re in an innovation race against cybercriminals. Organisations can either continue to fall behind, or they can take the lead with the most advanced form of AI available. Deep learning stops more threats than other approaches, and it does it much faster.
There is still a fair bit of uncertainty around deep learning because the different technical terms can be confusing to a newcomer. Our mission is to help clarify and explain the differences and benefits of deep learning.
But as technology becomes more widely understood in the next few years, we can expect it to begin reshaping the way we think about security. Organisations can begin seeing immediate results by integrating deep learning into their existing security stacks. Those organisations at the forefront of adopting deep learning technology will establish themselves as hardened targets, too tough for all but the most determined adversaries to try and crack.