Exploring the Application of Deep Learning in Cyber Security

Where Deep Learning Plays a Role in Cyber Security

Deep learning (DL) is a subset of machine learning (ML), and is able to learn and improve on its own by examining computer algorithms. Deep learning uses artificial neural networks that are designed to imitate how humans think and learn. Until recently, neural networks were limited by computing power and thus were limited in complexity. But now, advancements in big data analytics have permitted larger, more sophisticated neural networks, allowing computers to observe, learn, and react to complex situations faster than humans. 

Existing cyber security solutions fail to address the growing dynamics of modern cyberattacks, particularly in detecting new threats, analyzing complex vectors and events, and an inability to scale to the sheer volume of attacks. Applying deep learning in cyber security can eliminate many of these problems with new approaches and methods, being applied to DDoS detection, identifying behavioral anomalies, detecting malware and botnets, and voice identification. 

Deep Learning Improves on Machine Learning

Machine learning has always been seen as an innovative solution to protecting cyber assets. But ML tools can potentially be reverse-engineered to create bias or vulnerability that lower the effectiveness of its defenses. Hackers can even use their own ML algorithms to infect a cyber security solution with false data sets, for example. 

Deep learning, on the other hand, circumvents the need for data scientists to manually feed data sets. DL models are able to process massive volumes of raw data that are used to automatically train the cyber security system. DL neural networks are trained to become autonomous and don’t need human oversight and intervention. Over time, DL is able to more accurately identify highly complex patterns from large data sets than ML, and do it much faster. 

Deep Learning in Cyber Security is More Proactive

What’s even more interesting about deep learning in cyber security is its ability to proactively identify and stop attacks before they happen. Most cyber tools are reactionary and rely on specific indicators of a compromise to detect a threat. They generally only recognize threats they already know about, but they’re not effective against unknown or zero-day threats. 

Deep learning algorithms use deep neural networks to “think” like a human brain and can adjust themselves to data properties they are trained on. That makes it easier for it to adapt automatically to the massive volume of threats out there. While ML requires too much human intervention move fast enough, DL continues to evolve and learn over time to pre-emptively recognize threats it has not seen before and prevent them from taking effect. 

DL can be very effective for intrusion detection and prevention (ID/IP), where it detects malicious network activity and prevents bad actors from accessing a network. In the past, machine learning was used for these types of defenses, but ML algorithms tended to generate too many false positives, which in turn made it more difficult for security teams to root out the real problems. DL neural networks can make ID/IP systems smarter by analyzing traffic more accurately to differentiating good activity from the bad. 

Advantages of Deep Learning in Cyber Security

The application of DL offers three key advantages for cyber security teams. 

• Simple: unlike machine learning, DL greatly simplifies the feature creation process, replacing complex, highly technical data pipelines with simpler, more easily trainable models. This allows cyber teams to offload more of their work, and DL can be trained for learning specific features, helping to detect unknown attacks such as zero-day malware. 
• Scalable: Typical ML algorithms require the storage of all data points in memory, which is difficult to achieve when massive datasets are in play. This makes ML less able to improve performance with lots of data, and thus cannot scale. Deep learning, conversely, can be trained on datasets of different sizes and can iterate over smaller data batches. The models are better fitted to large datasets and scale much easier. 
• Reusable: DL models can be re-trained when new data is introduced without having to start over from the beginning. They are better for continuous online training, which is vital for large production models. DL models can also be repurposed so previous work can be reinvested into more robust and powerful models. 

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Learning Deep Learning!

There is a lot to learn in the exciting field of deep learning and cyber security. Deep Learning courses with a focus on Keras and TensorFlow are a great first step, and CISSP certification training will build on any tech professional’s knowledge to become a valued member of the cyber security team.