Smart objects connected to the Internet, constituting the so called Internet of Things (IoT), are revolutionizing human beings' interaction with the world. As technology reaches everywhere, anyone can misuse it, and it is always essential to secure it. In this work we present a denial-of-service (DoS) detection architecture for 6LoWPAN, the standard protocol designed by IETF as an adaptation layer for low-power lossy networks enabling low-power devices to communicate with the Internet. The proposed architecture integrates an intrusion detection system (IDS) into the network framework developed within the EU FP7 project ebbits. The aim is to detect DoS attacks based on 6LoWPAN. In order to evaluate the performance of the proposed architecture, preliminary implementation was completed and tested against a real DoS attack using a penetration testing system. The paper concludes with the related results proving to be successful in detecting DoS attacks on 6LoWPAN. Further, e xtending the IDS could lead to detect more complex attacks on 6LoWPAN

Recent advances in random matrix theory have spurred the adoption of eigenvalue-based detection techniques for cooperative spectrum sensing in cognitive radio. These techniques use the ratio between the largest and the smallest eigenvalues of the received signal covariance matrix to infer the presence or absence of the primary signal. The results derived so far are based on asymptotical assumptions, due to the difficulties in characterizing the exact eigenvalues ratio distribution. By exploiting a recent result on the limiting distribution of the smallest eigenvalue in complex Wishart matrices, in this paper we derive an expression for the limiting eigenvalue ratio distribution, which turns out to be much more accurate than the previous approximations also in the non-asymptotical region. This result is then applied to calculate the decision sensing threshold as a function of a target probability of false alarm. Numerical simulations show that the proposed detection rule provides a substantial improvement compared to the other eigenvalue-based algorithms.

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