◈ INTELLIGENT IDS/IPS

IoT/OT Intelligent Intrusion Detection Framework

A lightweight, rule-based IDS/IPS framework for non-traditional IoT/OT environments. Detects SYN floods, data exfiltration, and DDoS activity across the Purdue Model zones using real-world RT-IoT2022 dataset traffic data.

RT-IoT2022 Dataset Rule-Based Detection Purdue Model 3-Zone Architecture 10+ Attack Types 15+ Visualizations IPS Response Python 3.11 Pandas Matplotlib

PURDUE MODEL — ZONE ARCHITECTURE

ENTERPRISE ZONE

LEVEL 3–4

Network infrastructure & cloud. Detects large-scale DDoS traffic floods targeting network availability.

DDoS Detection flow_pkts_per_sec > 5000

CONTROL ZONE

LEVEL 2

Gateways, routers, local controllers. Primary detection point for SYN floods & port scanning.

SYN Flood / Scan flow_SYN_flag_count > 0

FIELD ZONE

LEVEL 0–1

IoT cameras & edge devices. Detects abnormal payload volumes indicating data exfiltration.

Data Exfiltration payload_bytes/sec > 1M

🛡️ IDS DETECTION LAYER

Rule-Based · Alert Generation · IPS Response Simulation

01 — Core Logic

Rule-Based Detection Engine

◈ PRIORITY 1 · CONTROL ZONE

⚡

SYN Flood / Scan

→ Control Zone Alert

Detects SYN flag anomalies from port scanning and connection flooding attacks. Any SYN activity triggers an immediate Control Zone alert — the highest-priority detection in the system.

if row['flow_SYN_flag_count'] > 0:
Zone = "Control Zone"
Type = "SYN Flood / Scan"

→ Block IP · Log Alert · Simulate IPS

◈ PRIORITY 2 · FIELD ZONE

📡

High Data Exfiltration

→ Field Zone Alert

Monitors payload transmission rates from IoT devices. Unusually high payload bytes per second indicate compromised devices transmitting sensitive data outside normal communication patterns.

elif row['payload_bytes_per_second'] > 1_000_000:
Zone = "Field Zone"
Type = "High Data Exfiltration"

→ Block IP · Log Alert · Simulate IPS

◈ PRIORITY 3 · ENTERPRISE ZONE

🌊

DDoS-like Activity

→ Enterprise Zone Alert

Detects high-rate traffic flooding patterns targeting network availability. Packets-per-second exceeding threshold signals a coordinated DDoS attack at the enterprise network layer.

elif row['flow_pkts_per_sec'] > 5000:
Zone = "Enterprise Zone"
Type = "DDoS-like Activity"

→ Block IP · Log Alert · Simulate IPS

03 — Analytics Output

15+ Visualization Types

Attack Type Distribution

Detected Alerts by Zone

ZONES

Control Zone — SYN Flood

72%

Enterprise Zone — DDoS

20%

Field Zone — Exfiltration

Attack Intensity Over Time

Normal vs Attack Traffic Split

Attack Traffic

~87%

Normal (Thing_Speak)

~6%

Normal (MQTT_Publish)

~4%

Normal (Wipro_bulb)

~3%

⚠ Dataset is heavily skewed toward DoS-based attacks — important for IDS threshold calibration

04 — New Extension

Live Device Integration

🔴 NEW · Real-Time Firewall & IDS/IPS Live Connection Feature

This extension moves the framework beyond static dataset analysis into live network monitoring — connecting directly to physical firewalls, IDS appliances (Snort/Suricata), and compatible cyber security devices for real-time alert ingestion and automated response.

INTEGRATION ARCHITECTURE

🔥

Firewall (pfSense / Fortinet / Cisco)

Syslog feed → Python socket listener on UDP 514

NEW

↓ syslog stream

🛡️

IDS/IPS Engine (Snort / Suricata)

Alert output via unified2 / EVE JSON socket

NEW

↓ parsed alert stream

🧠

IoT/OT Detection Engine

Apply existing rule-based logic to live traffic

LIVE

↓ zone classification

⚡

Automated IPS Response

iptables block · SNMP trap · webhook alert

NEW

LIVE INTEGRATION CODE

# Live syslog listener — replace CSV with real traffic import socket, threading, json class LiveIDSEngine: def __init__(self, host='0.0.0.0', port=514): self.sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) self.sock.bind((host, port)) def parse_syslog(self, raw): # Extract flow features from syslog line return { 'flow_SYN_flag_count': self.extract_syn(raw), 'flow_pkts_per_sec': self.extract_pps(raw), 'payload_bytes_per_second': self.extract_bps(raw) } def listen(self): while True: data, addr = self.sock.recvfrom(4096) row = self.parse_syslog(data.decode()) alert = detect_single_event(row) if alert: self.trigger_ips(alert, addr[0]) def trigger_ips(self, alert, ip): # Block at firewall via iptables os.system(f"iptables -A INPUT -s {ip} -j DROP") send_webhook_alert(alert, ip)

05 — Outcomes

System Results & Capabilities

100K+

Alerts Generated

Zone Classifications

15+

Graph Outputs

10+

Attack Types Detected

⚠️ Known Limitations

Static thresholds — rule-based logic may not adapt to evolving attack patterns or generate false positives under unusual traffic conditions.

Static dataset only — operates on RT-IoT2022 CSV rather than live real-time traffic (addressed in the new Live Integration feature above).

Simulated IPS — response actions such as blocking IPs are simulated in software, not enforced at the network layer (also addressed in Live Integration).

Limited protocol depth — does not perform deep application-layer inspection for advanced persistent threats.

🚀 Future Enhancements

ML-based anomaly detection — integrate Isolation Forest or Autoencoder models for adaptive, threshold-free detection of unknown attacks.

Real-time traffic monitoring — live syslog ingestion from firewalls, Snort/Suricata, and compatible IoT security appliances (in progress above).

SIEM integration — connect to Splunk or OpenSearch for centralized analysis, correlation across devices, and automated playbook response.

Deep packet inspection — extend protocol-level analysis to application layer for detection of encrypted tunneling and advanced exfiltration techniques.

06 — Data Foundation

Dataset & Tools

📡

RT-IoT2022 Dataset

Primary · Real-Time IoT Network Traffic

Kaggle/UCI dataset containing labeled real-world IoT network traffic with 10+ attack types. Includes flow-level features: SYN flag counts, packet rates, payload bytes per second, protocol types, and service identifiers — directly aligned with the detection logic.

PRIMARY DATASET

🛠️

Tech Stack

Python 3.11 · VS Code · Libraries

Built entirely in Python using Pandas for data manipulation and alert generation, Matplotlib for 15+ visualization types, and standard library modules for file I/O and random sampling. Designed for simplicity, transparency, and suitability for resource-constrained IoT environments.

LIGHTWEIGHT & PORTABLE