ThreatBytes
Super Intelligence for novel malware research. Founder & Principal Researcher.
Programme →Research
Malware analysis, agentic AI and adversarial ML.
Malware analysis, agentic AI and adversarial ML — ThreatBytes and open artefacts.
- Programme
- ThreatBytes
- Questions
- 5 open
- Lifecycle
- 10 stages
- Artefacts
- 2 open resources
Programmes
Artefacts
Open malware-analysis artefacts.
Template · Licence MIT · v1.0
Malware Analysis Report Template
A standardised report format with a JSON Schema and Markdown template, mapped to MITRE ATT&CK.
YARA · Licence MIT · v1.0
YARA Rule Starter Kit
Tested, documented YARA rules with safe test fixtures and CI that checks every rule on each push.
Questions
Research questions
- RQ-001Can SI models integrating deep learning, reinforcement learning, and reasoning modules accurately predict previously unseen malware behaviours before execution?In development
- RQ-002How can agentic cognitive SI infer malware intent from incomplete or evolving behavioural indicators?Planned
- RQ-003What combination of pattern recognition, reinforcement learning, and reasoning strategies yields optimal predictive and preventive malware defence?Planned
- RQ-004How can autonomous SI agents execute reliable, pre-emptive mitigation actions while maintaining low false-positive rates and avoiding collateral disruption?Planned
- RQ-005How can community-submitted malware samples enhance the learning and predictive capability of the agentic SI framework?Planned
Lifecycle
Ten-stage lifecycle
01
Observe
02
Collect
03
Analyse
04
Reason
05
Predict
06
Decide
07
Mitigate
08
Evaluate
09
Learn
10
Contribute
Governance
Ethics & governance
Quarantine-first intakeprovenance logging · ethics review · sandboxing
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