From the course: XAI and Interpretability in Cybersecurity

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Using SHAP for cybersecurity insights

Using SHAP for cybersecurity insights

- [Narrator] Picture this. You are a cybersecurity expert, armed with a sophisticated machine learning model to detect threats. But how do you explain its decisions to your team or stakeholders? This is where SHAP comes in. SHAP is a game-changing tool in the world of explainable AI. And today, we're going to be rolling up our sleeves to apply SHAP in a cybersecurity context. So why is this important? Well, SHAP can provide deep insights into which features influences your model's prediction, potentially uncovering new patterns in threat detection. SHAP, which stands for Shapley Addictive Explanation, is based on game theory concepts. It assigns each feature an important value for a particular prediction. Number one, it provides consistent and fair feature attributions. Number two, it can work on any machine learning model. Number three, it offers both local, which focuses on individual prediction, and global, which focuses on the overall model explanations. Now let's get our hands on…

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