Cyber-Internet Security Framework to Conquer Energy-Related Attacks on the Internet of Things with Machine Learning...

Area of Science:

• Computer Science
• Network Security
• Internet of Things

Background:

• The Internet of Things (IoT) connects numerous devices, creating complex communication networks.
• IoT systems, integrating Wireless Sensor Networks (WSN) and Mobile Ad hoc Networks (MANET), face significant security vulnerabilities due to their interconnected and unprotected environments.
• Traditional security approaches are insufficient for the unique challenges posed by IoT.

Purpose of the Study:

• To address the security threats in IoT networks, specifically Denial of Service (DoS) attacks like Delegate Entity Attacks (DEA).
• To propose a novel security mechanism that enhances energy management and extends the lifespan of IoT nodes.
• To improve the detection and mitigation of sophisticated attacks that are difficult to identify.

Main Methods:

• Implementation of a Hierarchical Intrusion Detection System (HIPS) partitioned into Delegate Entity (DE) and Pivotal Entity (PE).
• Utilization of a Random Number Generator (RNG) within the HIPS framework (RNGHID) for anomaly detection.
• Development of a Malicious Node Alert System (MNAS) employing machine learning algorithms for attack classification and network-wide alerts.

Main Results:

• The proposed RNGHID system effectively identifies abnormal node behavior and pinpoints the location and impact of SD attacks.
• The MNAS classifies various attack types, providing timely warnings to other network nodes.
• The protocol demonstrates desirable properties including indivisible authentication, rapid authentication, and minimal transmission and storage overhead.

Conclusions:

• The RNGHID system offers a robust solution for detecting and mitigating Delegate Entity Attacks in IoT networks.
• The proposed approach enhances network security while optimizing energy consumption and prolonging the operational life of IoT devices.
• This research contributes a significant advancement in securing the complex and evolving landscape of the Internet of Things.