Research / ThreatBytes
Super Intelligence for novel malware research.
Agentic SI, neural networks, behavioural analysis and cognitive reasoning for detecting previously unseen malware.
- Role
- Founder & Principal Researcher
- Status
- VECTOR SI v1 in development
- Affiliation
- To be confirmed
The knowledge gap
Malware now adapts, mutates and evades traditional defences through polymorphism, obfuscation and autonomous propagation. Most defensive systems remain reactive: they rely on rules, known signatures or behavioural indicators that identify malicious activity only after compromise.
Machine learning has improved malware classification, but most implementations are retrospective. They analyse software after execution rather than reasoning over incomplete signals to anticipate novel malware before it runs.
The gap is not detection alone. It is autonomous systems that learn from historical, simulated and community-submitted specimens to infer future threat behaviour. ThreatBytes develops an agentic, reasoning-driven framework to do that, before execution.
Four phases
Phase 1
Data
Historical corpus of 110M+ unique specimens plus governed community intake. Behavioural, temporal, structural and signature features; synthetic data from sandboxed execution.
Phase 2
Model design
Hybrid agentic SI: deep learning, reinforcement learning, cognitive and reasoning layers, anomaly detection. Continuous learning, with the emphasis on pre-execution prediction.
Phase 3
Proactive defence
Integration with the ThreatBytes platform for ingestion and reasoning-based decisions. Agentic mitigation such as sandbox containment and endpoint isolation, deployed in containers.
Phase 4
Evaluation
Controlled execution, cyber-range and red-team testing. Benchmarked against reactive IDS/IPS (e.g. Snort, Suricata), with explainable SI analysis.
VECTOR SI v1 · In development
Versatile Engine for Cognitive Threat Observation & Response.
Baseline static and behavioural classifiers on historical corpus features; reactive scoring only.
Source: threatbytes.dev. The planned ingest reaches 100 million specimens in equal annual increments from 2027 to 2032, under quarantine and ethics governance.
Open artefacts you can use now.
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.