A deep neural network is a machine learning model made of multiple layers of connected parameters that learn patterns from data. Each layer transforms the input, allowing the model to represent complex relationships that are hard to encode with fixed rules. Because knowledge is distributed across many weights, the model’s decisions are often probabilistic rather than fully deterministic or easy to trace.
In cyber security, this matters because deep models can be useful and fragile at the same time. Defenders use them for phishing detection, malware classification, anomaly detection, and speech or image analysis. Attackers may try to evade them with adversarial inputs, poisoned training data, or prompt-and-response tricks that exploit blind spots in learned behavior. Security teams also need to monitor how the model behaves after deployment, since context changes can shift performance and risk. That makes deep neural networks not just software components, but systems that must be tested, constrained, and measured over time.