Artificial Intelligence and Cyber Security — Part 1

  1. Unsupervised Learning — Detecting anomalies, data labeling, identifying patterns of normal behavior, clustering, and communalizing
  2. Supervised Learning- Classification — Categorization of threats, sources, data classification, deep learning for image categorization
  3. Supervised LearningRegression — Predicting threat priority, severity, data volumes
  4. Recommendation Systems — Association between events, recommendations, and advisory
  5. Natural Language Processing — building knowledge graphs, analyzing social media sentiments, policy and rules semantic analysis
  6. Reinforcement Learning — Risk and reward for a response, remediation action
  1. Malicious Traffic Identification using a risk score of traffic flows
  2. Fraudulent and/or Risky user identification using behavioral analytics and user risk scores
  3. Identifying phishing websites using page ranking, community detection, image classification
  4. Identifying Botnet domains using domain risk score and reputation metrics
  5. Database attacks using abnormal activity detection
  6. Security intelligence consolidation and correlation using knowledge graphs
  7. Automated threat disposition by learning analyst behavior
  8. Threat categorization and attack phase determination by learning threat frameworks
  9. Data classification and crown jewels identification
  10. Account take over and fraud detection using reputation and risk scores



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Aankur Bhatia

Aankur Bhatia


Aankur works as the Chief Data Scientist for a large multinational company. Passionate about application of ML to Cyber Security and holds over 20 patents