AI-driven methods for resiliency and security assessment: the case for autonomous driving system and HPC storage system

Nowadays, computing systems are used extensively in mission-critical exploration, transportation, scientific study, and manufacturing. With the advances in computation technologies, computing systems have become ever more complex. Due to the system’s complexity, it is increasingly hard for humans operators to monitor, assess, and manage the system directly. Moreover, traditional model-based or rule-based assessment techniques cannot provide sufficient coverages because of the wide range of use cases and failure modes of complex systems. Recently artificial intelligence (AI)-driven methods are used for timely accurate and high-coverage assessments in complex systems because of their ability to learn from data without explicitly modeling the complex systems. This thesis discusses our work on AI-driven assessment methods—RoboTack, DiverseAV, and Kaleidoscope—in the domain of security (RoboTack) and reliability (DiverseAV, Kaleidoscope) assessment in two critical use cases: autonomous driving systems (ADS) and high-performance computing (HPC) storage systems. We show that by using artificial intelligence and machine learning-based techniques, we can perform high-accuracy, high-coverage security, or reliability assessments of large-scale, complex systems efficiently in real-time.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2021

Author dc:creator

• Cui, Shengkun

Contributors dc:contributor

• Kalbarczyk, Zbigniew T.

• Autonomous Driving System
• Autonomous Vehicle
• HPC System
• Artificial Intelligence
• Machine Learning
• Fault-tolerant System
• Reliability
• Security
• Adversarial Attack
• Fault Detection
• Failure Localization
• Failure Diagnosis

Statement dc:rights

• Copyright 2021 Shengkun Cui

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/110655

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/110655