Day 5 Decision Tree & Random Forest
Decision Trees & Random Forests — Easy to Understand, Easier to Exploit?
Today, I explored two classic ML algorithms that are surprisingly interpretable — and surprisingly vulnerable 👀
🔹 Decision Trees
Think of them like "if-then" flowcharts. They split data step by step:
👉 If credit score > 700 → Approve loan 👉 Else if income is high & no late payments → Approve 👉 Else → Reject
It’s a checklist-style model — easy to understand, easy to debug.
🔹 Random Forests
Now imagine a forest of decision trees. Each tree sees a slightly different view of the data and gives its vote.
Out of 100 trees: 🗳 70 say “Approve”, 30 say “Reject” ✅ Final answer? Approve.
The majority wins — leading to better accuracy & reduced bias.
🔐 Security Lens
⚠️ Decision Tree Risks
Evasion Attacks – Tiny input tweaks can change the outcome. 📌 A spammer adjusts a few words slightly to bypass a spam filter — and it works.
Privacy Leakage – Trees can memorize sensitive training data. 📌 A medical model might reveal symptom patterns tied to real patients.
⚠️ Random Forest Risks
Adversarial Examples – Even with many trees, crafted inputs can slip through. 📌 A malware sample crafted to fool all trees might evade detection entirely.
Data Poisoning – If attackers poison the training data, the entire forest learns wrongly. 📌 Injecting fake “benign” malware during training teaches the model to trust real threats.
📚 References
• Kantchelian et al., 2016 – Evasion Attacks on Decision Trees • Biggio et al., 2018 – Poisoning Attacks on Random Forests • Yeom et al., 2019 – Privacy Risks in Tree Models • Bonus Read: Demystifying Decision Trees – SapienceSpace
💬 Question
How do you secure your ML models from evasion or poisoning?
📅 Tomorrow: I’ll explore Boosting — and why making your model “stronger” might also make it fragile ⚠️
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#100DaysOfAISec – Day 5 Post #AISecurity #MLSecurity #DecisionTrees #CyberSecurity #AIPrivacy #AdversarialML #LearningInPublic #100DaysChallenge #ArifLearnsAI #LinkedInTech
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