Biblio

Encryption has become an important need for each and every data transmission. Large amount of delicate data is transferred regularly through different computer networks such as e-banking, email applications and file exchange. Cryptanalysis is study of analyzing the hidden information in the system. The process of cryptanalysis could be done by various features such as power, sound, electromagnetic radiation etc. Lightweight cryptography plays an important role in the IoT devices. It includes various appliances, vehicles, smart sensors and RFID-tags (RFID). PRESENT is one such algorithm, designed for resource constrained devices. This requires less memory and consumes less power. The project propounds a model in which the cryptographic keys are analyzed by the trace of power.

Trustworthiness is a soft-security feature that evaluates the correct behavior of nodes in a network. More specifically, this feature tries to answer the following question: how much should we trust in a certain node? To determine the trustworthiness of a node, our approach focuses on two reputation indicators: the self-data trust, which evaluates the data generated by the node itself taking into account its historical data; and the peer-data trust, which utilizes the nearest nodes' data. In this paper, we show how these two indicators can be calculated using the Gaussian Overlap and Pearson correlation. This paper includes a validation of our trustworthiness approach using real data from unofficial and official weather stations in Portugal. This is a representative scenario of the current situation in many other areas, with different entities providing different kinds of data using autonomous sensors in a continuous way over the networks.