Traditional red teams test what humans think to attack.
HabeSec tests the
mathematical blind spots in your ML model
- the gaps that emerge from how your model learned, not how attackers behave.
These blind spots are invisible to human testers and only detectable
through automated adversarial simulation.
🎯
Perturbation Attack
Tests whether an attacker who carefully calibrates their behavior
to stay just below your detection thresholds can evade your model completely.
🪞
Mimicry Attack
Simulates an attacker who copies your normal traffic patterns exactly
the most dangerous and hardest-to-detect attack type found in real networks.
📊
Noise Injection
Tests whether an attacker who randomizes their behavior to avoid
pattern matching can bypass your detection system undetected.
🔬
Research Validated
Built on MSc research validated on the CICIDS 2017 benchmark dataset.
98.72% clean accuracy hiding 80.89% adversarial evasion proven on real data.