In this work we apply machine learning algorithms on network traffic data for accurate identification of IoT devices connected to a network. To train and evaluate the classifier, we collected and labeled network traffic data from nine distinct IoT devices, and PCs and smartphones. Using supervised learning, we trained a multi-stage meta classifier; in the first stage, the classifier can distinguish between traffic generated by IoT and non-IoT devices. In the second stage, each IoT device is associated a specific IoT device class. The overall IoT classification accuracy of our model is 99.281%. CCS Concepts •Security and privacy → Mobile and wireless security; •Computing methodologies → Machine learning;

Insider threats are one of today’s most challenging cybersecurity issues that are not well addressed by commonly employed security solutions. In this work, we propose structural taxonomy and novel categorization of research that contribute to the organization and disambiguation of insider threat incidents and the defense solutions used against them. The objective of our categorization is to systematize knowledge in insider threat research while using an existing grounded theory method for rigorous literature review. The proposed categorization depicts the workflow among particular categories that include incidents and datasets, analysis of incidents, simulations, and defense solutions. Special attention is paid to the definitions and taxonomies of the insider threat; we present a structural taxonomy of insider threat incidents that is based on existing taxonomies and the 5W1H questions of the information gathering problem. Our survey will enhance researchers’ efforts in the domain of insider threat because it provides (1) a novel structural taxonomy that contributes to orthogonal classification of incidents and defining the scope of defense solutions employed against them, (2) an overview on publicly available datasets that can be used to test new detection solutions against other works, (3) references of existing case studies and frameworks modeling insiders’ behaviors for the purpose of reviewing defense solutions or extending their coverage, and (4) a discussion of existing trends and further research directions that can be used for reasoning in the insider threat domain.

Smart contracts allow untrusting parties to arrange agreements encoded as code deployed on a blockchain platform. To release their potential, it is necessary to connect the contracts with the outside world, such that they can understand and use information from other infrastructures. However, there are many challenges associated with realizing such a system, and despite the existence of many proposals, no solution is secure, provides easily-parsable data, introduces small overheads, and is easy to deploy. In this paper, we propose Practical Data Feed Service (PDFS), a system that combines the advantages of the previous schemes and introduces new functionalities. PDFS extends content providers by including new features for data transparency and consistency validations. This combination provides multiple benefits like content which is easy to parse and efficient authenticity verification without breaking natural trust chains. PDFS keeps content providers auditable and mitigates their malicious activities (like data modification or censorship) and allows them to create a new business model. We show how PDFS is integrated with content providers, report on a PDFS implementation and present results from conducted experimental evaluations.

No abstract

No abstract