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PhD Student Yaman Sharaf-Dabbagh successfully defends his PhD

PhD Student Yaman Sharaf-Dabbagh successfully defends his dissertation entitled "Security and Privacy of Internet of Things: Authentication and Blockchain".


Reaping the benefits of the Internet of things (IoT) system is contingent upon developing IoT-specific security and privacy solutions. Conventional security and authentication solutions often fail to meet IoT  requirements due to the computationally limited and portable nature of IoT objects. Privacy in IoT is major issue as well in the light of current attacks on Facebook, and Uber. Blockchain on the other hand is an emerging technology with the potential to end the corporations control over our personal information. 
This dissertation, investigates the use of blockchain in IoT systems. The proposed frameworks and solutions are designed to address the main issues faced by blockchain and IoT systems. This dissertation led to the following key contributions. First, a novel lightweight framework, called DShard, that supports low energy devices. The framework utilizes blockchain to solve the limitations of scalability, centralization, and privacy. The framework is able to support small devices with low energy capabilities by dynamically grouping the devices in the system into smaller blockchain based systems called shards. Each shard has a subset of the devices in the system and a portion of the Blockchain. To secure each shard against 51% attacks, the framework determines the type of each device in the system and uses these information to insure all shards are balanced. Second, an IoT objects authentication framework is proposed. The framework uses device-specific information, called fingerprints, along with a transfer learning tool to authenticate objects in the IoT. The framework tracks the effect of changes in the physical environment on fingerprints and uses unique IoT environmental effects features to detect both cyber and cyber-physical emulation attacks. The proposed environmental effects estimation framework is proven to improve the detection rate of attackers without increasing the false positives rate. The proposed framework is also shown to be able to detect cyber-physical attackers that are capable of replicating the fingerprints of target objects which conventional methods are unable to detect. A transfer learning approach is proposed to allow the use of objects with different types and features in the environmental effects estimation process to enhance the performance of the framework while capturing practical IoT deployments with diverse object types.