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Cybersecurity: Neural Networks Simplified

A Simplified View of Neural Networks 

In today’s digital world, safeguarding your business against cyber threats is more critical than ever. As a business owner, you know that data is one of your most valuable assets. But, how do you protect it effectively? Neural networks may be the key to significantly improving your security posture. In this post, I’ll conceptually break down the terminology and deduce how neural networks can be applied to your cybersecurity efforts. 

What are Neural Networks?

To visualize a neural network, picture your brain, with all of its neurons, all interconnected and communicating. Now, visualize a mini, artificial version of this functioning inside a computer. A neural network is essentially multiple artificial neurons working together to process information, much like the neurons in your brain work together to process information.  

How do Neural Networks process data?  

Input Layer

This is where the neural network receives the data. Think of it as the reception desk of an office. This is where information is first received. For cybersecurity, this information might be things like who is trying to access your network and from what location. 

Hidden Layers

After the reception desk, the information is passed to the other employees in the office. This is like passing information to hidden layers. They typically do the heavy lifting of processing information. The more layers and neurons (“employees”) you have here, the more complex and thorough the processing. 

Output Layer

Finally, the processed information needs to be “acted upon”. This is done in the output layer, which you can think of as the manager’s office. It takes the processed data and makes a decision with the data. For example, in cybersecurity, the output layer decides whether an attempted login should or should not be blocked, based on whether any anomalies are identified.  

How Can Neural Networks Improve Cybersecurity?

Identifying Bad Actors

One of the purposes of cybersecurity is to accurately detect threats. Neural networks are very effective at spotting patterns and recognizing when something isn’t part of normal day to day activities as a potential security threat. 

Computational Speed

Time is of the essence when dealing with cyber threats. Neural networks can process information extraordinarily fast, allowing them to instantly spot and quickly respond to threats. 


Just like the hiring process for a company’s human resources department, neural networks can be customized to fit your business's needs. They learn from your specific data and adapt over time, getting better and better at protecting your unique environment. 

Reduction of False Alarms

False alarms can create unnecessary and costly disruptions to business operations. Neural networks help to reduce the likelihood of flagging something as a threat when it is not, saving you time and stress.

Staying Ahead of the Curve

Cyber threats are constantly evolving, and neural networks can evolve with them. They learn as they go, adapting to new tactics and techniques used by cyber criminals.  

Applying Neural Networks? 

Incorporating neural networks in various cybersecurity tasks, including anomaly detection, malware detection, intrusion detection, and network traffic analysis, into your cybersecurity strategy will significantly improve your overall business performance. Cybersecurity solutions that incorporate neural networks enhance your ability to learn and adapt, spot patterns, and quickly respond to threats. These solutions offer a powerful tool for protecting your business’s critical data. If you're interested in staying a step ahead in the evolving landscape of data security, it is worth considering the addition of neural networks to your security toolbox. 

About the author

Bradley D. Castle