Cybersecure Intelligent Sensor Framework for Smart Buildings: AI-Based Intrusion Detection and Resilience Against IoT...

Area of Science:

• Cybersecurity
• Artificial Intelligence
• Smart Building Technology

Background:

• The proliferation of Internet of Things (IoT) devices in smart buildings creates significant cybersecurity vulnerabilities.
• Attacks targeting Building Management Systems (BMS) and critical infrastructure like HVAC can compromise building safety and functionality.
• Existing security measures may not adequately address the evolving landscape of IoT-based cyber threats.

Purpose of the Study:

• To present a novel cybersecure intelligent sensor framework for smart buildings.
• To enhance the detection, classification, and response to IoT-related cyberattacks.
• To improve the overall cybersecurity posture of smart building infrastructures.

Main Methods:

• Development of an automated Intrusion Detection System (IDS) utilizing machine learning algorithms.
• Integration of intelligent sensor networks with AI-based analytics for continuous monitoring.
• Implementation of predictive modeling and automated response mechanisms for threat mitigation.

Main Results:

• The proposed framework demonstrated high detection rates for various cyberattacks.
• Low false alarm frequencies were observed, indicating system accuracy.
• Fast response times to detected threats were achieved, minimizing potential damage.

Conclusions:

• AI-enhanced cybersecurity systems are crucial for protecting IoT-based smart buildings.
• The developed framework offers a promising solution for enhancing smart building resilience against cyber threats.
• Continuous monitoring and automated responses are key to effective IoT security in smart environments.