Federated Learning-basd IoT Security
In this project, we propose a new effective anomaly detection model to differentiate benign patterns of behavior from malicious activities in mobile-based networks. We utilize Federated Learning (FL) technique for aggregating anomaly detection patterns for IDSs. While traditional Machine Learning models mainly rely on computational power and training dataset of a centralized server, FL, which has gained much attention recently in different domains, is defined as a combination of federated and machine learning techniques. Read more, here.
Blockchain For Forensic in Distributed File Systems
The Interplanetary File System (IPFS) is a distributed file system that seeks to decentralize the web and to make it faster and more efficient. It incorporates well-known technologies, including BitTorrent and Git, to create a swarm of computing systems that share information. Since its introduction in 2016, IPFS has seen great improvements and adoption from both individuals and enterprise organizations. Its distributed network allows users to share files and information across the globe. IPFS works well with large files that may consume or require large bandwidth to upload and/or download over the Internet. The rapid adoption of this distributed file system is in part because IPFS is designed to operate on top of different protocols, such as FTP and HTTP. However, there are underpinning concerns relating to security and access control, for example lack of traceability on how the files are accessed. The aim of this project is to complement IPFS with blockchain technology, by proposing a new approach (BlockIPFS) to create a clear audit trail. BlockIPFS allows us to achieve improved trustworthiness of the data and authorship protection, and provide a clear route to trace back all activities associated with a given file using blockchain as a service.
Decentralized Authentication in Internet of Underwater Things
In recent years, there has been a rapid growth in developing smart cities. Nearly 70% of the Earth's surface is covered by water and a large proportion of underwater environments are still unknown and have not been explored. In this light, Internet of things (IoT) is one of the most important technologies used in smart cities. Due to the growth of IoT and its influence in all areas of human life, including the underwater environment, a new class of IoT, called Internet of underwater things (IoUT) has emerged. IoUT includes a network of underwater smart devices that are connected to each other and has applications in environmental monitoring, underwater exploration, disaster prevention and military just to name a few. In autonomous interactions of underwater devices, objects must be authenticated and securely interconnected to avoid security risks by malicious nodes. Most authentication methods and security mechanisms are centralized and often require a trustful third party in communications, which may well increase the computation cost and energy consumption due to the subsequent overhead, especially for underwater communications. On the other hand, there are restrictions on devices in the underwater environment, the most important of which are energy constraints. In this project, we investigate robust, transparent, and energy-efficient decentralized authentication mechanisms for IoUT using blockchain technology.