Securing Intelligent Edge Systems - 695.624

This course explores the security, privacy, and resilience of modern edge computing systems and distributed intelligent environments. Students examine the architectures, communication models, and operational characteristics of connected devices, edge applications, edge–cloud infrastructures, and data-driven platforms operating in heterogeneous and resource-constrained environments. The course emphasizes the identification and analysis of vulnerabilities across communication protocols, software platforms, data pipelines, application-layer interactions, and distributed system integrations. Students evaluate how architectural and design decisions influence attack surfaces, privacy exposure, reliability, and operational risk across modern edge ecosystems. The course also examines the growing integration of artificial intelligence into edge systems and the security challenges introduced by learning-enabled components and autonomous functionality. Topics include adversarial machine learning, model integrity, inference privacy, trust management, secure deployment of AI-enabled services, and risks associated with data-driven decision-making at the edge. Through technical readings, case studies, and applied analysis, students investigate emerging threats and practical defense strategies spanning IoT, cyber-physical systems, autonomous platforms, and distributed AI applications. The course culminates in a comprehensive security design or assessment project focused on realistic intelligent edge system scenarios.

Course Prerequisite(s)

EN.695.644 Digital Forensics Technologies and Techniques or equivalent course with some knowledge of Network Security.