Matching Items (4)
Description
The security of Internet-of-Things (IoT) is essential for its widespread adoption. The recent advancement in Artificial Intelligence (AI) brings both challenges and opportunities to IoT security. On the one hand, AI enables better security designs. On the other hand, AI-based advanced attacks are more threatening than traditional ones. This dissertation aims to study the dual effects of AI on IoT security, specifically IoT device security and IoT communication security. Particularly, this dissertation investigates three important topics: 1) security of acoustic mobile authentication, 2) Deep Learning (DL)-guided jamming attacks on cross-technology IoT networks, and 3) DL-powered scalable group-key establishment for large IoT networks. Chapter 2 presents a thorough study on the security of acoustic mobile authentication. In particular, this chapter proposes two mobile authentication schemes identifying the user's mobile device with its linear and nonlinear acoustic fingerprints, respectively. Both schemes adopt the Data Mining (DM) techniques to improve their identification accuracy. This chapter identifies a novel fingerprint-emulation attack and proposes the dynamic challenge and response method as an effective defense. A comprehensive comparison between two schemes in terms of security, usability, and deployment is presented at the end of this chapter, which suggests their respective suitable application scenarios. Chapter 3 identifies a novel DL-guided predictive jamming attack named DeepJam. DeepJam targets at cross-technology IoT networks and explores Deep Reinforcement Learning (DRL) to predict the victim's transmissions that are not subject to the Cross-Technology Interference (CTI). This chapter also proposes two effective countermeasures against DeepJam for resource capable and resource constrained IoT networks, respectively. Chapter 4 proposes a drone-aided DL-powered scalable group-key generation scheme, named DroneKey, for large-scale IoT networks. DroneKey is a physical-layer key generation scheme. In particular, DroneKey actively induces correlated changes to the wireless signals received by a group of devices and explores DL techniques to extract a common key from them. DroneKey significantly outperforms existing solutions in terms of the scalability and key-generation rate.
ContributorsHan, Dianqi (Author) / Zhang, Yanchao YZ (Thesis advisor) / Reisslein, Martin MR (Committee member) / Xue, Guoliang GX (Committee member) / Zhang, Junshan JZ (Committee member) / Arizona State University (Publisher)
Created2022
Description
Important features of smart grids are identified as efficient transmission of electricity and monitoring data, speedier recovery from disrupted power supplies, decreased operation and management costs, improved security, etc. All these can be made possible by a well-planned advanced communication system for the grid. However, most of the existing research not only fail to provide a clear understanding of the intra-and-inter dependencies of joint power-communication systems, necessary for a reliable and resilient operation of the grid, but also debates on the best suited design for the communication network. This dissertation introduces a simple, yet accurate multi-valued-logic based model of interdependency called the Modified Implicative Interdependency Model (MIIM) which can depict the interactions between the components of these power-communication systems and using this model an existing problem in the grid concerning cascading failure of entities is solved. Communication system for smart grid is responsible for securely sending both power transmission control data and environmental monitoring data to Control Centers. In this dissertation, a hybrid communication network, comprising of both wired and wireless communication is proposed together with a secure routing protocol to mitigate different types of cyber-attacks. Also, to prevent false data injections and owing to some limitations in MIIM, a further improvement is made to develop the Multi-State Implicative Interdependency Model which considers the data dependency of communication entities. In this dissertation, the issue of communication cost incurred due to ill-designed topology is also addressed, and an optimal-cost communication topology is planned for modern smart grids. It is also identified that communication cost analysis cannot be done without considering the optimal placement of Phasor Measurement Unit (PMU). Consequently, the optimal PMU placement problem is studied simultaneously with the minimum cost network design problem, and an attempt to minimize the overall cost is made in this dissertation. All the designs and network algorithms proposed here, are tested on substation location data of Arizona.
ContributorsRoy, Sohini (Author) / Sen, Arunabha AS (Thesis advisor) / Pal, Anamitra AP (Committee member) / Xue, Guoliang GX (Committee member) / Reisslein, Martin MR (Committee member) / Arizona State University (Publisher)
Created2022
Description
Over the past few years, the Internet of Things (IoT) has become an essential element of daily life. At the core of IoT are the densely deployed heterogeneous IoT sensors, such as RFID tags, cameras, temperature sensors, pressure sensors. These sensors work collectively to sense and capture intricate details of the surroundings, enabling the provision of highly detailed and comprehensive information. This fine-grained information encompasses a wide range of critical parameters that contribute to intelligent decision-making processes. Therefore, the security and privacy of heterogeneous IoT systems are indispensable. The heterogeneous nature of IoT systems poses a number of security and privacy challenges, including device security and privacy, data security and privacy, communication security, and AI and machine learning security. This dissertation delves into specific research issues related to device, communication, and data security, addressing them comprehensively. By focusing on these critical aspects, this work aims to enhance the security and privacy of heterogeneous IoT systems, contributing to their reliable and trustworthy operation. Specifically, Chapter 1 introduces the challenges and existing solutions in heterogeneous IoT systems. Chapter 2 presents SmartRFID, a novel UHF RFID authentication system to promote commodity crypto-less UHF RFID tags for security-sensitive applications. Chapter 3 presents WearRF-CLA, a novel CLA scheme built upon increasingly popular wrist wearables and UHF RFID systems. Chapter 4 presents the design and evaluation of PhyAuth, a PHY message authentication framework against packet-inject attacks in ZigBee networks. Chapter 5 presents NeighborWatch, a novel image-forgery detection framework for multi-cameras system with OFoV. Chapter 6 discusses the future work.
ContributorsLi, Ang (Author) / Zhang, Yanchao YZ (Thesis advisor) / Fan, Deliang DF (Committee member) / Xue, Guoliang GX (Committee member) / Reisslein, Martin MR (Committee member) / Arizona State University (Publisher)
Created2023
Description
Pushing the artificial intelligence frontier to resource-constrained edge nodes for edge intelligence is nontrivial. This dissertation provides a comprehensive study of optimization-based meta-learning algorithms to build a theoretic foundation of edge intelligence, with the focus on two topics: 1) model-based reinforcement learning (RL); 2) distributed edge learning. Under this common theme, this study is broadly organized into two parts. The first part studies meta-learning algorithms for model-based RL. First, the fundamental limit of model learning is explored for linear time-varying systems, using a two-step meta-learning algorithm with an episodic block model. A comprehensive non-asymptotic analysis of the sample complexity is provided, where a two-scale martingale small-ball approach is devised to address the challenges in sample correlation and small sample sizes. Next, policy learning of offline RL in general Markov decision processes is explored. To tackle the challenges therein, e.g., value overestimation and possibly poor quality of offline datasets, a model-based offline Meta-RL approach with regularized policy optimization is proposed, by learning a meta-model for task inference and a meta-policy for safe exploration of out-of-distribution state-actions. The second part investigates meta-learning algorithms for distributed edge learning. First, the general edge supervised learning is considered, where the edge node aims to quickly learn a good model with limited samples. A platform-aided collaborative learning framework is proposed to learn a model initialization via federated meta-learning across multiple nodes, which is transferred to target nodes for fine-tuning. Then, a channel gating module is introduced to select important channels of backbone networks for efficient local computation. A novel federated meta-learning approach is developed to learn meta-initializations for backbone networks and gating modules, from which a task-specific channel gated network is quickly adapted. Taking one step further, the continual edge learning is investigated in the context of online meta-learning, where each node has a sequence of online tasks. A multi-agent online meta-learning framework is developed to accelerate the task-average performance in a single node under limited communication among neighbors, through the lens of distributed online convex optimization. Building on distributed online gradient descent with gradient tracking, the optimal task-average regret is achieved at a faster rate.
ContributorsLin, Sen (Author) / Zhang, Junshan JZ (Thesis advisor) / Ying, Lei LY (Thesis advisor) / Bertsekas, Dimitri DB (Committee member) / Nedich, Angelia AN (Committee member) / Wang, Weina WW (Committee member) / Arizona State University (Publisher)
Created2021