Day 13 Naive Bayes
Today, I dove into one of the oldest and surprisingly effective ML classifiers — Naive Bayes.
🔹 It’s based on Bayes’ Theorem:
P(Class | Features) = [ P(Features | Class) × P(Class) ] / P(Features)
🔹 The “naive” part? It assumes all features are independent — rarely true in reality, but often good enough, especially for text classification.
Naive Bayes is like a doctor diagnosing a patient by looking at symptoms one at a time, assuming each symptom (like cough, fever, fatigue) occurs independently. In reality, symptoms often correlate — but this simplified model still gets the diagnosis right surprisingly often.
🛠️ Common Use Cases
✅ Spam Filtering
✅ Text Classification
✅ Intrusion Detection Systems (IDS)
🧠 Despite its simplicity, Naive Bayes performs surprisingly well — particularly on high-dimensional datasets like emails and documents.
🚧 Limitations
Struggles with non-linear relationships or complex interactions between features.
Can be sensitive to skewed class distributions if not properly calibrated.
But that independence assumption? A sweet spot for attackers.
🔐 Security Lens
⚠️ Independence Assumption Abuse
Attackers inject correlated features to game the classifier. &#xNAN;Example: A spam email might include benign terms like “invoice” or “team update” to lower its spam score and evade detection.
⚠️ Feature Poisoning
Adversaries inject mislabeled or crafted data into the training set to skew feature probabilities, corrupting the model's logic.
⚠️ Privacy Leaks via Probabilistic Outputs
Naive Bayes outputs probabilities. Confidence scores can leak info about the training data, enabling membership inference attacks.
📚 Key References
Rubinstein et al. (2009) — Privacy-Preserving Classification
Lowd & Meek (2005) — Adversarial Learning in Naive Bayes Spam Filters
Biggio et al. (2013) — Evasion Attacks against Machine Learning at Test Time
💬 Question
How much do you trust simple models like Naive Bayes in high-stakes systems? Let’s discuss — sometimes old tools still hold up, but only when you know their limits.
📅 Up next (Day 14): Support Vector Machines (SVM) — and how attackers can shift the decision boundary to their advantage ⚖️
🔗 Missed Day 12? Catch up here: https://lnkd.in/ghkbH6Nb
#100DaysOfAISec #AISecurity #MLSecurity #MachineLearningSecurity #NaiveBayes #CyberSecurity #AIPrivacy #AdversarialML #LearningInPublic #100DaysChallenge #ArifLearnsAI #LinkedInTech
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