PhenoVector – Behavioural Genome Engine for Running Processes

PhenoVector is a fully local, lightweight behavioural‑genome engine that derives structured “genome vectors” for running system processes. It performs safe on‑device telemetry collection using psutil, computes >20 behaviour‑driven gene scores, assigns risk levels, and provides an optional Streamlit dashboard for visual exploration.

All capabilities described here match the actual source code: features.py, genes.py, genome.py, and app.py.

---

---

🔍 What PhenoVector Does

1. Collects Runtime Behaviour Features

From each process (features.py):

• CPU percentage
• RSS memory
• Thread count
• Open file handles
• Network connections
• Executable entropy (first 200kB)
• Lifetime & CPU time
• System process / temp executable identification

2. Computes Behavioural Gene Scores

Each behaviour feature is normalised via PopulationStats and transformed into 20+ genes (genes.py):

• resource_abuse
• entropy
• impersonation
• exfiltration
• tracking
• persistence
• mutation
• stealth
• latency
• syscall_diversity
• burst_density
• thread_intensity
• registry_touch
• io_intensity
• network_activity
• file_entropy
• handle_abuse
• injection_sus
• dll_sideload

Every gene strictly returns a float in [0,1].

3. Produces Per‑Process Genome Objects

genome.py returns a structured ProcessGenome:

• identity: pid, name, exe
• behaviour features
• gene vector (dict)
• risk_score (0–1)
• risk_level (benign, suspicious, high)

4. Optional Visual Dashboard

app.py provides:

• PCA/t‑SNE process clustering
• Radar charts of gene profiles
• IsolationForest anomaly scoring
• PID whitelisting
• Raw genome table & JSON export

---

📦 Installation

pip install phenovector

---

🚀 Quick Usage

from phenovector.genome import analyze_system

genomes = analyze_system(limit=100)
for g in genomes:
    print(g.pid, g.name, g.risk_score, g.risk_level)
    print(g.genes)

---

📊 Run the Streamlit UI

streamlit run -m phenovector.app

---

📄 License

APACHE 2.0