Toivo Herman Kamati
ABSTRACT
The use of deep learning in detecting network attacks is widely adopted. However, supervised learning requires labeled training data to learn behaviour of honeypot in honeynet. This research studies the problem of learning patterns from honeypot behaviour to validate mass email marketing. Honeypots are deceiving, and their behaviour are the sources of security data for cyber-security research. Deep learning models are explored to learn behaviour from labeled time series of sequences of security events collected from production virtual Local Area Network. Evaluation on deception demonstrates detection of mass email marketing using Multilayer Perceptron Neural Networks with an accuracy of 87%.
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Index Terms
- Honeypot Behaviour Patterns Learning for Mass Email Marketing Detection
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