Data Analytics and Performance Enhancement in Edge-Cloud Collaborative Internet of Things Systems

Based on the evolving communications, computing and embedded systems technologies, Internet of Things (IoT) systems can interconnect not only physical users and devices but also virtual services and objects, which have already been applied to many different application scenarios, such as smart home, smart healthcare, and intelligent transportation. With the rapid development, the number of involving devices increases tremendously. The huge number of devices and correspondingly generated data bring critical challenges to the IoT systems. To enhance the overall performance, this thesis aims to address the related technical issues on IoT data processing and physical topology discovery of the subnets self-organized by IoT devices. First of all, the issues on outlier detection and data aggregation are addressed through the development of recursive principal component analysis (R-PCA) based data analysis framework. The framework is developed in a cluster-based structure to fully exploit the spatial correlation of IoT data. Specifically, the sensing devices are gathered into clusters based on spatial data correlation. Edge devices are assigned to the clusters for the R-PCA based outlier detection and data aggregation. The outlier-free and aggregated data are forwarded to the remote cloud server for data reconstruction and storage. Moreover, a data reduction scheme is further proposed to relieve the burden on the trunk link for data uploading by utilizing the temporal data correlation. Kalman filters (KFs) with identical parameters are maintained at the edge and cloud for data prediction. The amount of data uploading is reduced by using the data predicted by the KF in the cloud instead of uploading all the practically measured data. Furthermore, an unmanned aerial vehicle (UAV) assisted IoT system is particularly designed for large-scale monitoring. Wireless sensor nodes are flexibly deployed for environmental sensing and self-organized into wireless sensor networks (WSNs). A physical topology discovery scheme is proposed to construct the physical topology of WSNs in the cloud server to facilitate performance optimization, where the physical topology indicates both the logical connectivity statuses of WSNs and the physical locations of WSN nodes. The physical topology discovery scheme is implemented through the newly developed parallel Metropolis-Hastings random walk based information sampling and network-wide 3D localization algorithms, where UAVs are served as the mobile edge devices and anchor nodes. Based on the physical topology constructed in the cloud, a UAV-enabled spatial data sampling scheme is further proposed to efficiently sample data from the monitoring area by using denoising autoencoder (DAE). By deploying the encoder of DAE at the UAV and decoder in the cloud, the data can be partially sampled from the sensing field and accurately reconstructed in the cloud. In the final part of the thesis, a novel autoencoder (AE) neural network based data outlier detection algorithm is proposed, where both encoder and decoder of AE are deployed at the edge devices. Data outliers can be accurately detected by the large fluctuations in the squared error generated by the data passing through the encoder and decoder of the AE

A Multi-Agent Systems Approach for Analysis of Stepping Stone Attacks

Stepping stone attacks are one of the most sophisticated cyber-attacks, in which attackers make a chain of compromised hosts to reach a victim target. In this Dissertation, an analytic model with Multi-Agent systems approach has been proposed to analyze the propagation of stepping stones attacks in dynamic vulnerability graphs. Because the vulnerability configuration in a network is inherently dynamic, in this Dissertation a biased min-consensus technique for dynamic graphs with fixed and switching topology is proposed as a distributed technique to calculate the most vulnerable path for stepping stones attacks in dynamic vulnerability graphs. We use min-plus algebra to analyze and provide necessary and sufficient convergence conditions to the shortest path in the fixed topology case. A necessary condition for the switching topology case is provided. Most cyber-attacks involve an attacker launching a multi-stage attack by exploiting a sequence of hosts. This multi-stage attack generates a chain of ``stepping stones” from the origin to target. The choice of stepping stones is a function of the degree of exploitability, the impact, attacker’s capability, masking origin location, and intent. In this Dissertation, we model and analyze scenarios wherein an attacker employs multiple strategies to choose stepping stones. The problem is modeled as an Adjacency Quadratic Shortest Path using dynamic vulnerability graphs with multi-agent dynamic system approach. With this approach, the shortest stepping stone path with maximum node degree and the shortest stepping stone path with maximum impact are modeled and analyzed. Because embedded controllers are omnipresent in networks, in this Dissertation as a Risk Mitigation Strategy, a cyber-attack tolerant control strategy for embedded controllers is proposed. A dual redundant control architecture that combines two identical controllers that are switched periodically between active and restart modes is proposed. The strategy is addressed to mitigate the impact due to the corruption of the controller software by an adversary. We analyze the impact of the resetting and restarting the controller software and performance of the switching process. The minimum requirements in the control design, for effective mitigation of cyber-attacks to the control software that implies a “fast” switching period is provided. The simulation results demonstrate the effectiveness of the proposed strategy when the time to fully reset and restart the controller is faster than the time taken by an adversary to compromise the controller. The results also provide insights into the stability and safety regions and the factors that determine the effectiveness of the proposed strategy