Day 12 KNN & Clustering

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Day 12 Poster

Today I explored two of the simplest — yet surprisingly powerful — machine learning techniques:

🔹 K-Nearest Neighbors (KNN) 🔹 Clustering Algorithms like K-Means

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🔸 KNN – Like asking your 3 closest neighbors for restaurant recommendations — and going with the majority.

• Doesn’t scale well with large data (lazy learning)

• Suffers from the curse of dimensionality

• Use Case: Real-time classification, stock market forecasting, data pre-processing

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🔸 Clustering – Like sorting socks by color — no names, just similarity.

• Sensitive to initial conditions and number of clusters

• Inability to handle categorical data

• Use Case: Grouping similar logs across distributed DBs, customer segmentation, threat pattern discovery

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🧠 Security Relevance

Both are intuitive, interpretable, and widely used in cybersecurity — for anomaly detection, threat grouping, and log clustering. But when nearness = trust, it opens the door to subtle — and dangerous — manipulations 👇

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🔐 Security Lens

⚠️ Evasion via Distance Manipulation (KNN)

Attackers can subtly modify malicious inputs to appear close to benign ones — bypassing detection.

💡 Example: Slightly altered malware that lives in the "neighborhood" of clean files.

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⚠️ Cluster Poisoning Attacks

In unsupervised setups, adversaries inject crafted data to shift cluster centers or distort groupings.

💡 Example: Fake logs or reviews injected to confuse anomaly detectors.

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⚠️ Model Extraction Risks

KNN-based systems are query-heavy and memory-based — attackers can reconstruct training data if they know the distance metric.

💡 Example: API misuse to reverse-engineer sensitive training sets.

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📚 Key References

• Jagielski et al. (2018): Manipulating Machine Learning with Adversarial Clustering

• Tramer et al. (2016): Model Extraction via Query Attacks

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💬 Discussion Prompt

Have you ever used clustering for log analysis or threat detection? What was your biggest challenge?

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📅 Coming Up

Naive Bayes — and how its “strong independence” assumption becomes an adversary’s playground 🎯

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🔗 Missed Day 11?

Catch up here: https://lnkd.in/g3EwkEQA

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#100DaysOfAISec - Day 12 Post #AISecurity #MLSecurity #MachineLearningSecurity #KNN #Clustering #CyberSecurity #AIPrivacy #AdversarialML #LearningInPublic #100DaysChallenge #ArifLearnsAI #LinkedInTech