Satellite-Assisted Continuous Roving Unmanned IoT/RF Sensor Enhanced Robot (SATCRUISER) for AI Data Generation - Preliminary Findings

Abstract: The proliferation of Internet of Things (IoT) devices has revolutionized the landscape of facility and supply chain security, offering unprecedented opportunities for real-time monitoring, proactive threat detection, and enhanced operational resilience. However, the increasing adoption of IoT devices in the context of facility and supply chain security has resulted in novel implications for safeguarding assets and mitigating risks. The dynamic nature of modern facilities and complex supply chains demands robust security measures to safeguard assets, prevent disruptions, and ensure operational continuity. Traditionally, security systems have relied on static, fragmented approaches, often lacking comprehensive visibility and real-time insights. The advent of IoT devices, presented a paradigm shift, enabling the creation of interconnected ecosystems that monitor and secure critical assets throughout the facility and supply chain. IoT devices offer an array of benefits in facility and supply chain security. These devices can be deployed as sensors, actuators, and monitoring devices, collecting and transmitting data on various parameters such as access control, environmental conditions, inventory levels, and equipment performance. While the proliferation of IoT devices presents potential, it also brings forth certain challenges. The interconnectedness of devices increases the attack surface, raising concerns about cybersecurity vulnerabilities and potential breaches. Ensuring robust security measures, including secure device authentication, encryption, and regular firmware updates, is crucial to safeguard against unauthorized access and potential data compromises.Artificial Intelligence (AI) is one rapidly maturing and evolving technology domain with potential to bolster the security of this class of IOT-based security solutions. Specifically, AI holds potential to provide more robust and responsive capabilities to ensure IOT device and endpoint security (e.g., anomaly detection, predictive maintenance, threat intelligence, automated security response, behavior monitoring, etc.). AI-driven security measures hold the potential to provide a robust defense against emerging threats, that can continuously learn and adapt to detect and enable mitigation of emerging threat vectors. However, high-quality data to train AI models is vital. The accuracy and performance of AI algorithms heavily depend on the quality, diversity, and quantity of the training data. To train AI algorithms effectively, a vast amount of diverse and labeled data is required. However, acquiring such data can be challenging, as it necessitates extensive and continuous data collection from various sources. Traditional methods of data gathering often fall short due to limitations in coverage, scalability, and real-time data availability. This paper provides an overview and initial findings from a rapid prototyping / hackathon effort to develop a Satellite-Assisted Continuous Roving Unmanned IoT/RF Sensor Enhanced Robot (SATCRUISER) that enables continuous collection, geolocalization, and backhaul of facility's IOT endpoints and wireless activity. The intent of SATCRUISER is to enable continuous collection of quality IOT wireless data that can be used for initial AI model training and, eventually, ongoing model learning and facility monitoring. We present the SATCRUISER system architecture and initial findings from pilot collection periods to source a baseline corpus of data to train novel AI IOT security models.

Keywords: AI, UGV, RF, IOT, Data Generation

DOI: 10.54941/ahfe1004308

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