Visible to the public Physical layer security against cooperative anomaly attack using bivariate data in distributed CRNs

TitlePhysical layer security against cooperative anomaly attack using bivariate data in distributed CRNs
Publication TypeConference Paper
Year of Publication2019
AuthorsSrinu, Sesham, Reddy, M. Kranthi Kumar, Temaneh-Nyah, Clement
Conference Name2019 11th International Conference on Communication Systems Networks (COMSNETS)
KeywordsAnomaly, bivariate data, Cognitive radio, cognitive radio networking, cognitive radio networks, Communication system security, composability, Computational modeling, cooperative anomaly attack, cooperative communication, Cooperative SSDF attacks, distributed spectrum sensing, eigenvalue-based sensing, Eigenvalues and eigenfunctions, mahalanobis distance measure, Metrics, multiple anomaly detection algorithm, OSI network model, physical layer security, pubcrawl, radio spectrum management, resilience, Resiliency, Robust distance measure, Sensors, signal detection, telecommunication security, wireless channels, Wireless communication, wireless communication network, Wireless sensor networks, wireless technology
AbstractWireless communication network (WCN) performance is primarily depends on physical layer security which is critical among all other layers of OSI network model. It is typically prone to anomaly/malicious user's attacks owing to openness of wireless channels. Cognitive radio networking (CRN) is a recently emerged wireless technology that is having numerous security challenges because of its unlicensed access of wireless channels. In CRNs, the security issues occur mainly during spectrum sensing and is more pronounced during distributed spectrum sensing. In recent past, various anomaly effects are modelled and developed detectors by applying advanced statistical techniques. Nevertheless, many of these detectors have been developed based on sensing data of one variable (energy measurement) and degrades their performance drastically when the data is contaminated with multiple anomaly nodes, that attack the network cooperatively. Hence, one has to develop an efficient multiple anomaly detection algorithm to eliminate all possible cooperative attacks. To achieve this, in this work, the impact of anomaly on detection probability is verified beforehand in developing an efficient algorithm using bivariate data to detect possible attacks with mahalanobis distance measure. Result discloses that detection error of cooperative attacks by anomaly has significant impact on eigenvalue-based sensing.
Citation Keysrinu_physical_2019