We present intrusion detection algorithms to detect physical layer jamming attacks in wireless networks. We compare the performance of local algorithms on the basis of the signal-to-interference-plus-noise ratio (SINR) executing independently at several monitors, with a collaborative detection algorithm that fuses the outputs provided by these algorithms. The local algorithms fall into two categories: simple threshold that raise an alarm if the output of the SINR-based metrics we consider deviates from a predefined detection threshold and cumulative sum (cusum) algorithms that raise an alarm if the aggregated output exceeds the predefined threshold. For collaborative detection, we use the Dempster-Shafer theory of evidence algorithm. We collect SINR traces from a real IEEE 802:11 network, and with the use of a new evaluation method, we evaluate both the local and the Dempster-Shafer algorithms in terms of the detection probability, false alarm rate, and their robustness to different detection threshold values, under different attack intensities. The evaluation shows that the cusums achieve higher performance than the simple threshold algorithms under all attack intensities. The Dempster-Shafer algorithm when combined with the simple algorithms, it can increase their performance by more than 80%, but for the cusum algorithms it does not substantially improve their already high performance.
Circular, data-driven healthcare is increasingly being considered as an effective model to provide efficient, cost-effective and sustainable healthcare services in the future. Central to this model is the service-dominant "building-block"-type provision of care services to patients, paired with the collaboration of healthcare providers through a common infrastructure. This combination enables the forming of a decentralized, holistic care cycle. Sharing of patient medical information is pivotal towards reaching this goal; however, preserving medical record integrity and privacy, while at the same time allowing provider interoperability are often conflicting requirements. Blockchains and Smart Contracts can provide the underlying technology to support the decentralized care cycle by addressing patient privacy and medical record integrity, while simultaneously offering efficient interoperability between providers. To demonstrate how this could be achieved, a conceptual medical record access and sharing mechanism is presented which is suitable for a system operating within a regulated healthcare jurisdiction.
Abstract. In this paper we present and evaluate anomaly-based intrusion detection algorithms for detecting physical layer jamming attacks in wireless networks, by seeking changes in the statistical characteristics of the signal-to-noise ratio (SNR). Two types of algorithms are investigated: simple threshold algorithms and algorithms based on the cumulative sum change point detection procedure. The algorithms consider SNR-based metrics, which include the average SNR, minimum SNR, and max-minus-min SNR values in a short window. The algorithms are applied to measurements taken in two locations, one close and one far from the jammer, and evaluated in terms of the detection probability, false alarm rate, detection delay and their robustness to different detection threshold values. Our results show that the cumulative sum detection procedure can improve the detection probability and false alarm rate when measurements are taken far from the jammer, and can improve the robustness for different values of the detection threshold.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.