Method for Detecting Abnormal Activity in a Group of Mobile Robots.

Area of Science:

• Robotics
• Cybersecurity
• Network Analysis

Background:

• Wireless networks face diverse cyber attacks, necessitating robust defense mechanisms.
• Current security solutions are often inadequate for mobile robotic systems, highlighting the need for specialized protection.
• Developing tailored security strategies for groups of mobile robots is crucial for maintaining operational integrity.

Purpose of the Study:

• To analyze cyber parameters such as power consumption and residual energy in robotic systems.
• To conduct in-depth traffic analysis for evaluating attack effectiveness and identifying network anomalies.
• To establish metrics for characterizing the static behavior of robotic systems and their components.

Main Methods:

• Analysis of robotic system behavior under normal operating conditions.
• Development of an experimental stand for empirical validation.
• Theoretical analysis to support observed patterns in system behavior.
• Identification of key metrics including power consumption and network packet statistics (incoming, outgoing, redirected, dropped).

Main Results:

• Robotic systems exhibit static and uniform behavior under normal conditions.
• Specific indicators within robotic system components show minimal deviation from the mean, confirming static operation.
• A set of metrics was identified to quantify the static nature of robotic system operations.
• These metrics are suitable for independent analysis by robotic nodes with minimal performance impact.

Conclusions:

• The identified metrics are vital for developing integrated anomaly detection systems for robotic platforms.
• Characterizing static behavior provides a foundation for distinguishing normal operations from cyber intrusions.
• This approach enables proactive security measures tailored to the unique characteristics of robotic systems.