Day 12 KNN & Clustering
Today I explored two of the simplest — yet surprisingly powerful — machine learning techniques:
🔹 K-Nearest Neighbors (KNN) 🔹 Clustering Algorithms like K-Means
🔸 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
🔸 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
🧠 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 👇
🔐 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.
⚠️ 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.
⚠️ 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.
📚 Key References
Jagielski et al. (2018): Manipulating Machine Learning with Adversarial Clustering
Tramer et al. (2016): Model Extraction via Query Attacks
💬 Discussion Prompt
Have you ever used clustering for log analysis or threat detection? What was your biggest challenge?
📅 Coming Up
Naive Bayes — and how its “strong independence” assumption becomes an adversary’s playground 🎯
🔗 Missed Day 11?
Catch up here: https://lnkd.in/g3EwkEQA
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