Responsibility and non-repudiation in resource-constrained Internet of Things scenarios

The proliferation and popularity of smart autonomous systems necessitates the development of methods and models for ensuring the effective identification of their owners and controllers. The aim of this paper is to critically discuss the responsibility of Things and their impact on human affairs. This starts with an in-depth analysis of IoT Characteristics such as Autonomy, Ubiquity and Pervasiveness. We argue that Things governed by a controller should have an identifiable relationship between the two parties and that authentication and non-repudiation are essential characteristics in all IoT scenarios which require trustworthy communications. However, resources can be a problem, for instance, many Things are designed to perform in low-powered hardware. Hence, we also propose a protocol to demonstrate how we can achieve the authenticity of participating Things in a connectionless and resource-constrained environment

The Internet of Hackable Things

The Internet of Things makes possible to connect each everyday object to the Internet, making computing pervasive like never before. From a security and privacy perspective, this tsunami of connectivity represents a disaster, which makes each object remotely hackable. We claim that, in order to tackle this issue, we need to address a new challenge in security: education

A Cognitive Framework to Secure Smart Cities

The advancement in technology has transformed Cyber Physical Systems and their interface with IoT into a more sophisticated and challenging paradigm. As a result, vulnerabilities and potential attacks manifest themselves considerably more than before, forcing researchers to rethink the conventional strategies that are currently in place to secure such physical systems. This manuscript studies the complex interweaving of sensor networks and physical systems and suggests a foundational innovation in the field. In sharp contrast with the existing IDS and IPS solutions, in this paper, a preventive and proactive method is employed to stay ahead of attacks by constantly monitoring network data patterns and identifying threats that are imminent. Here, by capitalizing on the significant progress in processing power (e.g. petascale computing) and storage capacity of computer systems, we propose a deep learning approach to predict and identify various security breaches that are about to occur. The learning process takes place by collecting a large number of files of different types and running tests on them to classify them as benign or malicious. The prediction model obtained as such can then be used to identify attacks. Our project articulates a new framework for interactions between physical systems and sensor networks, where malicious packets are repeatedly learned over time while the system continually operates with respect to imperfect security mechanisms