The Internet of Things (IoT) devices have become popular in diverse domains such as e-Health, e-Home, e-Commerce, and e-Trafficking, etc. With increased deployment of IoT devices in the real world, they can be, and in some cases, already are subject to malicious attacks to compromise the security and privacy of the IoT devices. While a number of researchers have explored such security challenges and open problems in IoT, there is an unfortunate lack of a systematic study of the security challenges in the IoT landscape. In this paper, we aim at bridging this gap by conducting a thorough analysis of IoT security challenges and problems. We present a detailed analysis of IoT attack surfaces, threat models, security issues, requirements, forensics, and challenges. We also provide a set of open problems in IoT security and privacy to guide the attention of researchers into solving the most critical problems.

As increasing amounts of valuable information are produced and persist digitally, the ability to determine the origin of data becomes important. In science, medicine, commerce, and government, data provenance tracking is essential for rights protection, regulatory compliance, management of intelligence and medical data, and authentication of information as it flows through workplace tasks. While significant research has been conducted in this area, the associated security and privacy issues have not been explored, leaving provenance information vulnerable to illicit alteration as it passes through untrusted environments.In this article, we show how to provide strong integrity and confidentiality assurances for data provenance information at the kernel, file system, or application layer. We describe Sprov, our provenance-aware system prototype that implements provenance tracking of data writes at the application layer, which makes Sprov extremely easy to deploy. We present empirical results that show that, for real-life workloads, the runtime overhead of Sprov for recording provenance with confidentiality and integrity guarantees ranges from 1% to 13%, when all file modifications are recorded, and from 12% to 16%, when all file read and modifications are tracked. This is an expanded version of a paper presented at the 7th USENIX Conference on File and Storage Technologies (FAST), Hasan et al. [2009]. R. Hasan and M. ACM Reference Format:Hasan, R., Sion, R., and Winslett, M. 2009. Preventing history forgery with secure provenance.

The Internet of Things (IoT) involves numerous connected smart things with different technologies and communication standards. While IoT opens new opportunities in various fields, it introduces new challenges in the field of digital forensics investigations. The existing tools and procedures of digital forensics cannot meet the highly distributed and heterogeneous infrastructure of the IoT. Forensics investigators will face challenges while identifying necessary pieces of evidence from the IoT environment, and collecting and analyzing those evidence. In this article, we propose the first working definition of IoT forensics and systematically analyze the IoT forensics domain to explore the challenges and issues in this special branch of digital forensics. We propose a Forensics-aware IoT (FAIoT ) model for supporting reliable forensics investigations in the IoT environment.

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The popularity of distributed file systems continues to grow. Reasons they are preferred over traditional centralized file systems include fault tolerance, availability, scalability and performance. In addition, Peer-to-Peer (P2P) system concepts and scalable functions are being incorporated into the domain of file systems. This survey paper explores the design paradigms and important issues that relate to such systems and discusses the various research activities in the field of Distributed Peer-to-Peer file systems.