Neural networks are the connection between neuroscience and cybersecurity. Neuroscience is the study of the nervous system, particularly neurons. A neuron is a brain cell that mainly consists of dendrites and axons - which work together to receive and send signals throughout the body. Though there are still mysteries with the brain, neuroscientists have estimated that there are about 100 billion neurons. Machine learning, and its subsets, are inspired by the brain and seek to mimic it.

With cybersecurity, continuous innovation is necessary to maintain the security of systems and networks. Digital attacks manifest in various fashions, all aiming to cause havoc through malware, social engineering, advanced persistent threats, etc. The algorithms combat tedious work and improve the security posture of organizations.

Understanding Neural Networks

This is where neural networks come in. A neural network is comprised of four main components: inputs, weights, threshold, and output. The process starts first with data input, neural networks then recognize the pattern through training (hidden layer), and finally, the output is predicted.

Within this neural network model, the hidden layers are non-linear and this is where new features are extracted from the inputs. The more layers a neural network has, the deeper it becomes. In addition, each layer has neurons connected to other layers, and the output of a layer is passed into the network’s next layer.

Why is this relevant?

With cybersecurity and neural networks, the intersection is relevant to gain a deeper understanding of the threats we face and to build a system where each threat is learned from. Cybersecurity professionals’ responses to these threats can become much more comprehensive. With an extended learning stage, the behavioral data of benign activity and true positives improve the system in recognizing points of failure and suspicious activity. Perhaps this won’t solve all security-related problems, but those that work in a Security Operation Center (SOC), like me, find this exciting.

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