The Ransomware Behaviour Simulation and Prevention Tool

Ransomware Behaviour Simulation & Prevention Tool (Python) | Tour2Tech
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Python • Watchdog • PyCryptodome • ML

Ransomware Behaviour Simulation & Prevention – Sandbox, Detect, Block, Recover

Safely simulate ransomware-like encryption, monitor filesystem events, detect mass-encrypt behavior, and auto-prevent damage with backups and process blocking — all in a controlled lab.

  • Sandboxed AES/XOR encryption demo (no real system risk)
  • Real-time behavior detection (rename rate, extension bursts)
  • Automatic kill + quarantine + restore from secure backups
Delivery in 3–5 days • Pan-India support
*Demo video placeholder. Replace with your link.
1. Introduction

The Ransomware Behaviour Simulation & Prevention Tool is a Python-based cybersecurity project to study, simulate, and mitigate ransomware in a safe sandbox. It mimics encryption behavior, observes system impacts, and deploys real-time prevention like process blocking and automated recovery from secure backups. Ideal for hands-on learning and practical defense testing.

2. Existing System vs Proposed System
Existing System
  • Signature-based AV misses polymorphic variants.
  • Limited or no behavioral visibility.
  • No safe lab to analyze attack flow.
Proposed System
  • Sandboxed file-encryption simulation (AES/XOR).
  • Behavioral detection (mass-encrypt, rename spikes).
  • Auto-prevention: kill, quarantine, backup-restore.
  • Optional ML classifier for process risk.
  • Detailed incident report & logs.
3. Working
  1. Simulation Module: Creates a sandbox and encrypts sample files with AES/XOR.
  2. Monitoring Module: Watchdog tracks file creates/renames/extension bursts.
  3. Behavioral Detection: Flags mass encryption rates, suspicious write patterns.
  4. Alert & Prevention: Terminates process, quarantines directory, isolates keys.
  5. Recovery: Restores originals from backup store.
  6. Report: Generates summary with indicators and actions taken.
4. Technology Stack
  • Language: Python
  • Libraries: os, shutil, hashlib, psutil, watchdog, PyCryptodome, pandas, scikit-learn
  • Algorithms: AES for simulation; Random Forest/SVM for detection
  • Database: SQLite3/CSV for logs & threat history
  • Interface: Tkinter or Flask dashboard
  • Platform: Windows/Linux sandbox
5. Modules
Ransomware Simulation

Safe AES/XOR demo.

  • Sandbox-only operations
  • Configurable targets
File Monitoring

Real-time events.

  • Watchdog observers
  • Extension/rename spikes
Behavior Analysis

Heuristics + ML.

  • Rate thresholds
  • RF/SVM models
Prevention

Stop & quarantine.

  • Kill PIDs (psutil)
  • Isolate artifacts
Backup & Recovery

One-click restore.

  • Versioned backups
  • Integrity checks (hash)
Logging & Reporting

Traceable audits.

  • SQLite/CSV
  • Incident reports
6. Advantages
  • Safe hands-on ransomware lab with zero system risk.
  • Behavior-first approach catches novel variants.
  • Real-time alerts and automatic prevention.
  • Deep learning for cybersecurity students & teams.
  • Foundation for next-gen endpoint protection.
7. Applications
  • Academic labs & research on ransomware behavior.
  • Security training for IT professionals.
  • Sandbox testing for AV/EDR models.
  • Enterprise readiness drills & tabletop exercises.
  • Awareness demos for non-technical audiences.
Python Integration Sketch (Watchdog + PyCryptodome + ML)
import os, time, shutil, hashlib, psutil, sqlite3
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler
from Crypto.Cipher import AES
from Crypto.Random import get_random_bytes
import joblib  # for RandomForest/SVM model (optional)

SANDBOX = "sandbox/"
BACKUP  = "backup/"
DB      = "events.db"
THRESH_FILES_PER_MIN = 40  # example threshold for mass-encrypt behavior

def encrypt_file(path, key):
    # AES-CTR demo encryption (sandbox only)
    with open(path, "rb") as f: data = f.read()
    cipher = AES.new(key, AES.MODE_CTR)
    ct = cipher.encrypt(data)
    with open(path + ".enc", "wb") as f: f.write(cipher.nonce + ct)
    os.remove(path)

def simulate_encryption():
    key = get_random_bytes(16)
    for root,_,files in os.walk(SANDBOX):
        for name in files:
            if not name.endswith(".enc"):
                encrypt_file(os.path.join(root,name), key)
                time.sleep(0.05)  # pacing for demo

class RansomEventHandler(FileSystemEventHandler):
    def __init__(self):
        self.events = []
        self.last_min = int(time.time() // 60)
    def on_modified(self, event):
        self.track(event)
    def on_created(self, event):
        self.track(event)
    def track(self, event):
        now_min = int(time.time() // 60)
        if now_min != self.last_min:
            self.evaluate_rate()
            self.events.clear()
            self.last_min = now_min
        self.events.append(event.src_path)
    def evaluate_rate(self):
        rate = len(self.events)
        if rate > THRESH_FILES_PER_MIN:
            alert_and_prevent("Mass encryption rate detected")

def alert_and_prevent(reason):
    # Kill suspected processes (demo: own PID)
    for p in psutil.process_iter(["pid","name","cmdline"]):
        try:
            if "python" in (p.info["name"] or "").lower():
                p.terminate()
        except Exception:
            pass
    # Quarantine sandbox
    shutil.make_archive("quarantine", "zip", SANDBOX)
    # Restore from BACKUP
    restore_from_backup()
    log_incident(reason)

def restore_from_backup():
    if os.path.exists(BACKUP):
        for root,_,files in os.walk(BACKUP):
            for name in files:
                src = os.path.join(root,name)
                dst = src.replace(BACKUP, SANDBOX)
                os.makedirs(os.path.dirname(dst), exist_ok=True)
                shutil.copy2(src, dst)

def log_incident(msg):
    conn = sqlite3.connect(DB)
    cur = conn.cursor()
    cur.execute("CREATE TABLE IF NOT EXISTS incidents(ts INTEGER, msg TEXT)")
    cur.execute("INSERT INTO incidents VALUES(?,?)", (int(time.time()), msg))
    conn.commit(); conn.close()

if __name__ == "__main__":
    # Setup backup first, then run Observer + optional simulate_encryption()
    handler = RansomEventHandler()
    obs = Observer(); obs.schedule(handler, SANDBOX, recursive=True); obs.start()
    try:
        simulate_encryption()  # for demo only
        while True: time.sleep(1)
    except KeyboardInterrupt:
        obs.stop()
    obs.join()
              
Delivery includes safe sandbox scripts, Watchdog monitoring, AES/XOR simulators, prevention & recovery flows, SQLite reporting, optional ML classifier, and Flask/Tkinter dashboards.
What You Get
ItemIncludedNotes
Python Source CodeSimulation + Monitoring + Prevention
AES/XOR Demo EncryptorsSandboxed only
Behavior Detection EngineThresholds + optional ML
Backup & RecoveryVersioned restore flow
Flask/Tkinter UIControls, logs, graphs
Demo VideoSetup & working walkthrough
Report & PPTCollege-format templates
SupportInstallation + viva Q&A (1 month)

FAQs — Ransomware Simulation & Prevention

No. The simulator targets only a designated sandbox folder with sample files. Backups are kept separately for safe restores.

Not for simulation/monitoring. Some prevention actions (like killing protected processes) may require elevated permissions.

Yes. Thresholds, extension blocklists, and ML features are configurable. You can plug in additional detectors and response actions.

Want a hands-on ransomware lab?

Get the Simulation & Prevention Tool with code, demo, docs, and support.

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