Detection and Mitigation of IoT-Based Attacks Using SNMP and Moving Target Defense Techniques

Area of Science:

• Cybersecurity
• Cloud Computing
• Internet of Things (IoT)

Background:

• The integration of IoT and cloud technologies offers significant business advantages but introduces security challenges.
• Ensuring data confidentiality and security for IoT devices in cloud environments is critical for user trust.
• Distributed denial-of-service (DDoS) and false data injection attacks pose significant threats to IoT cloud systems.

Purpose of the Study:

• To propose and validate a comprehensive security solution for IoT devices operating in cloud environments.
• To protect against sophisticated cyber threats, specifically DDoS and false data injection attacks.
• To enhance the overall security posture and trustworthiness of cloud-integrated IoT systems.

Main Methods:

• Integration of Simple Network Management Protocol (SNMP) and Kullback-Leibler Distance (KLD) for attack detection.
• Application of Access Control Rules (ACL) and Moving Target Defense (MTD) techniques for attack mitigation.
• Experimental validation using simulations on the Amazon Web Service (AWS) platform.

Main Results:

• The proposed framework effectively detects both DDoS and false data injection attacks.
• ACL and MTD techniques significantly reduce attack probabilities and system delays.
• Simulations on AWS demonstrated a substantial improvement in the security of IoT cloud deployments.

Conclusions:

• The developed security framework provides a robust defense against critical cyber threats in cloud IoT ecosystems.
• Combining SNMP, KLD, ACL, and MTD offers a synergistic approach to enhancing IoT security.
• Implementing such integrated security measures is essential for maintaining user trust and realizing the full potential of IoT-cloud integration.