Anomaly Detection in IoT Networks

OT Detection Use Cases for your OT SOC When it comes to building an OT SOC, there’s a big misconception: many assume success is about collecting every log or integrating every system In reality, the key is focusing on operationally meaningful visibility — the detections that actually help you understand what’s happening inside your control network >> In industrial environments, context defines everything > The same Modbus write command could mean two very different things: > a maintenance engineer performing a scheduled update — or an attacker changing control logic. > Without context, both look identical in your SIEM. >> An OT SOC must speak the language of process, assets, and operations, not just alerts. It should tell you when something changes, who initiated it, and whether it threatens safety, reliability, or integrity >> Below are 10 detection use cases I always recommend as a starting point. They’re mapped to MITRE ATT&CK for ICS and NCA OTCC, but more importantly, they’re grounded in what actually happens inside real plants and industrial networks 1. Unauthorized PLC Programming Detect logic or configuration changes outside scheduled maintenance windows 2. ICS Protocol in IT Zone Flag Modbus, DNP3, or BACnet traffic on IT networks — strong evidence of segmentation drift or misconfiguration 3. PLC Stop or Mode Change Command Detect STOP or PROGRAM mode changes — an event that can halt production and indicate malicious control 4. Remote Access to HMI from Unapproved Source Identify RDP, VNC, or TeamViewer sessions from IT zones targeting OT HMIs — a common lateral movement path 5. New Device in Control VLAN Catch unauthorized or rogue devices joining deterministic control networks where new assets should rarely appear 6. PLC Firmware Downgrade or Version Change Detect unauthorized firmware rollbacks — a subtle but serious method of tampering or hiding malicious code 7. OPC UA Anonymous Session Identify untrusted or anonymous OPC UA sessions that bypass normal authentication or encryption 8. Engineering Software on Non-Engineering Host Detect the execution of TIA Portal, Control Builder, or similar tools on unauthorized systems — often a sign of credential misuse or insider activity 9. PLC Configuration Upload Monitor FTP/TFTP uploads to PLCs — an activity that could replace control logic or inject malicious configuration 10. Abnormal HMI Behavior Spot rapid screen changes, tag edits, or command spamming from operators — signs of misuse, automation, or compromise They aren’t just security detections — they’re process integrity safeguards. Each one gives the SOC visibility into the exact actions adversaries use during real OT incidents — often before physical impact occurs When combined with contextual data (authorized engineers, maintenance schedules, device baselines) and network telemetry , these detections evolve from simple alerts into actionable operational intelligence #OTSOC #OTsecurity #ICSsecurity

Building strong AIoT systems isn’t about sensors or models - it’s about trustworthy data pipelines that can think for themselves. I've found that the best AIoT systems aren't just smart, they're reliable because of their data. Here are the 7 key powers that make IoT + AI data truly robust 👇 ➞ 1. Timestamp Discipline: AI detects clock drift, sequence mismatches, and disordered events automatically. Use case: timestamp drift models, sequence anomaly detection. ✅ Action: Detect and realign out-of-order events early. ➞ 2. Sensor Validation Rules: AI learns normal sensor behavior dynamically instead of relying on fixed thresholds. Use case: sensor health scoring, auto-calibration suggestions. ✅ Action: Flag sensors behaving “off-pattern” using anomaly detection. ➞ 3. Missing-Data Resilience: Predicts and fills missing data intelligently while identifying dropout sources. Use case: smart interpolation, dropout classification. ✅ Action: Build models that classify data loss across devices and pipelines. ➞ 4. Event-Stream Modeling: Transforms raw signals into meaningful machine states. Use case: state classification (idle/running/fault), event correlation. ✅ Action: Train classifiers that convert raw events into operational insights. ➞ 5. Real-Time Ingestion Reliability: Predicts pipeline failures before they occur. Use case: health forecasting, auto-scaling triggers. ✅ Action: Predict ingestion backlogs using throughput and latency features. ➞ 6. Context Enrichment: Turns raw sensor data into contextual insights with AI metadata tagging. Use case: location inference, machine type identification, LLM-based enrichment. ✅ Action: Auto-attach asset metadata for smarter analytics. ➞ 7. Alert Tuning vs Noise: AI filters false alarms and ranks alerts by impact severity. Use case: alert deduplication, priority scoring, root cause analysis. ✅ Action: Train models using past ticket data to reduce alert fatigue. AIoT success = Data you can trust + Models that adapt. Build smarter pipelines that don’t just move data - they understand it. 🔁 Repost if you're building for the real world, not just connected demos. ➕ Follow Nick Tudor for more insights on AI + IoT that actually ship.

🚀 𝗪𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂𝗿 𝗣𝗟𝗖 𝗰𝗼𝘂𝗹𝗱 𝘁𝗮𝗹𝗸 𝘁𝗼 𝘁𝗵𝗲 𝗰𝗹𝗼𝘂𝗱, 𝗱𝗲𝘁𝗲𝗰𝘁 𝗮𝗻𝗼𝗺𝗮𝗹𝗶𝗲𝘀 𝘄𝗶𝘁𝗵 𝗔𝗜, 𝗮𝗻𝗱 𝘀𝗲𝗻𝗱 𝗮𝗹𝗲𝗿𝘁𝘀 𝗯𝗮𝗰𝗸 𝘁𝗼 𝘁𝗵𝗲 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 -> 𝗮𝗹𝗹 𝗶𝗻 𝗿𝗲𝗮𝗹 𝘁𝗶𝗺𝗲? Well… I built exactly that. And today I’m sharing the 𝗳𝘂𝗹𝗹 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄. 🔎 This is my third post on HiveMQ, and this time we go 𝗘𝗱𝗴𝗲 → 𝗖𝗹𝗼𝘂𝗱 → 𝗔𝗜 → 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝘂𝘀𝗶𝗻𝗴 𝗮 𝗿𝗲𝗮𝗹 𝗣𝗟𝗖, 𝗢𝗣𝗖 𝗨𝗔, 𝗠𝗤𝗧𝗧, 𝗮𝗻𝗱 𝗮 𝗣𝘆𝘁𝗵𝗼𝗻 𝗠𝗟 𝗺𝗼𝗱𝗲𝗹 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗶𝗻 𝗙𝗹𝗮𝘀𝗸. 🐝 𝐇𝐢𝐯𝐞𝐌𝐐 𝐄𝐝𝐠𝐞-𝐭𝐨-𝐂𝐥𝐨𝐮𝐝 𝐀𝐈 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 — 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝 Using the diagram below, I break down how a PLC vibration signal travels across: 1️⃣ 𝗜𝗻𝗽𝘂𝘁 𝗟𝗮𝘆𝗲𝗿 PLC → OPC UA → HiveMQ Edge (running in Revolution Pi) 2️⃣ 𝗧𝗿𝗮𝗻𝘀𝗽𝗼𝗿𝘁 𝗟𝗮𝘆𝗲𝗿 HiveMQ Edge → MQTT → HiveMQ Cloud 3️⃣ 𝗔𝗜 𝗟𝗮𝘆𝗲𝗿 HiveMQ Cloud → Python ML server → Anomaly Detection 4️⃣ 𝗢𝘂𝘁𝗽𝘂𝘁 𝗟𝗮𝘆𝗲𝗿 AI model → MQTT → HiveMQ Cloud → HiveMQ Edge → PLC/HMI 📌 𝗗𝗮𝘁𝗮 𝘁𝗼𝗽𝗶𝗰𝘀: • 𝗩𝗶𝗯𝗿𝗮𝘁𝗶𝗼𝗻 → machine/s71500/vib • 𝗔𝗹𝗲𝗿𝘁𝘀 → machine/s71500/alert 💡 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐭𝐡𝐢𝐬 𝐞𝐱𝐚𝐜𝐭 𝐬𝐞𝐭𝐮𝐩 𝐲𝐨𝐮𝐫𝐬𝐞𝐥𝐟? I’ve shared the complete step-by-step execution, configuration, and Python API code in the Code and Compile Exclusive Wiki so you can practice this workflow hands-on: https://lnkd.in/eUvZeJi2 🔗 If you want to learn more about the tools used in this pipeline, check out HiveMQ 𝗘𝗱𝗴𝗲 and HiveMQ 𝗖𝗹𝗼𝘂𝗱 both powerful platforms for IIoT and data movement. 🎥 𝐀𝐧𝐝 𝐲𝐞𝐬… 𝐚 𝐋𝐈𝐕𝐄 𝐝𝐞𝐦𝐨 𝐢𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞𝐝 𝐚𝐭 𝐭𝐡𝐞 𝐞𝐧𝐝 𝐨𝐟 𝐭𝐡𝐞 𝐚𝐫𝐭𝐢𝐜𝐥𝐞. Watch it in action and see how the anomaly detection works with vibration data. #HiveMQ #IIoT #MQTT #OPCUA #Automation #EdgeComputing #ML #CloudComputing #CodeAndCompile #IndustrialAI #PLC #PythonAI #PredictiveMaintenance

This is a pleasure sharing that one of our 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐏𝐚𝐩𝐞𝐫 Titled “𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞𝐝 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐟𝐨𝐫 𝐀𝐭𝐭𝐚𝐜𝐤 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐌𝐐𝐓𝐓-𝐄𝐧𝐚𝐛𝐥𝐞𝐝 𝐒𝐦𝐚𝐫𝐭 𝐇𝐨𝐦𝐞 𝐒𝐲𝐬𝐭𝐞𝐦𝐬” has been 𝐩𝐮𝐛𝐥𝐢𝐬𝐡𝐞𝐝 included in 𝐈𝐄𝐄𝐄 𝐗𝐩𝐥𝐨𝐫𝐞 with 𝐃𝐎𝐈 : 𝟏𝟎.𝟏𝟏𝟎𝟗/𝐄𝐦𝐞𝐫𝐠𝐈𝐍𝟔𝟑𝟐𝟎𝟕.𝟐𝟎𝟐𝟒.𝟏𝟎𝟗𝟔𝟎𝟗𝟕𝟖 𝐩𝐮𝐛𝐥𝐢𝐬𝐡𝐞𝐝 𝐛𝐲 𝐈𝐄𝐄𝐄. This Paper discusses and explores the MQTT protocol, which is an efficient and lightweight messaging protocol commonly used in IoT systems for communication. To identify and mitigate cyber-attacks in smart home systems, this study applies a set of machine learning algorithms to analyze MQTT attack dataset, which aims to detect and classify various cyber-attacks using multiple machine learning approaches with high accuracy and combine their prediction to find out the final result. The study begins with an introduction of smart home, its components, basic introduction, architecture of MQTT protocol, and major attack vectors targeting MQTT-based systems, highlighting vulnerabilities that can be exploited by attackers. Afterward this research paper proposes and implementing a model which uses a set of machine learning algorithms on MQTT attack dataset to train and test models, these become capable to identify anomalies in MQTT traffic, which enables the detection of attacks such as dos attack, flooding packet attack, SlowITe Attack and brute force attack for unauthorized access of servers or systems. Feature selection and data standardization done, all trained and tested models demonstrate high accuracy and effectiveness in threat detection and classification on a comprehensive MQTT dataset. This study illustrates the effectiveness and good application of AI/ML-driven approaches for security leak prevention which is boosting the smart home environments security attack detection and contributing towards the secure IoT ecosystems.   https://lnkd.in/g8K926tV

🚀 New Publication Alert! Published my research on ML-based Wireless Intrusion Detection in the International Journal of Wireless Security and Networks, expanding on my earlier work on IoT network/Edge devices. 🤔 Problem Statement: Can ML models defend IoT networks against adversarial attacks? ✍ Key Finding: Tree-based models barely flinched. Even at 25% feature manipulation, XGBoost and Random Forest kept 99.8%+ performance. Meanwhile, MLP? Not so resilient. 🤯 📊 Evaluated on CICIoT2023 & IoT Intrusion datasets 🎯 Tested under realistic adversarial traffic conditions ✅ Identified critical features: timing & protocol-based metrics Why This Matters: While we're securing LLMs, we should consider that not all ML models/deployments are built for the real world. If you're deploying on IoT networks, choose wisely. 🔗 Full paper: https://lnkd.in/eZSY6NhC #MachineLearning #IoTSecurity #Cybersecurity #IntrusionDetection #LLM #AdversarialML #Research #ModelSecurity #MLRobustness