In terms of using the technology of Cognitive Radio (CR), a Cognitive Sensor Network (CSN) is varied from the conventional Wireless Sensor Networks (WSNs). According to the interaction with the surrounding environment, the transmitter parameters can be modified in the sensor nodes of CSN adaptively. In CSNs, routing is one of the important components. Based on the capability of spectrum-aware, the schemes of routing of CSNs are district from other networks. The changeable spectrum resource dynamically should be understood by the routing scheme to establish a path of reliable forwarding by the adjustment of routing policy adaptively. In CSNs, reliable routing is an essential thing but still not a well-explored problem in CSNs. Packet drops due to spectrum unavailability and buffer overflows seriously affects the connectivity of the nodes. The whole network's lifetime and the data delivery rate are impacted by the prolonging packet drops. To increase the nodes' lifetime, the addressing of this drawback in the phase of routing should be done. Before the making of routing decisions, a new routing technique is proposed named as Drop factor based energy efficient routing technique (DFBEER) with the use of packet drop ratio and power dissipation metric of the spectrum links. With the total number of users in the routing path, the drop factor is computed. Power dissipation is calculated based on the transmitted data packets versus the amount of total consumed energy. This method reduces the drop ratio by avoiding the high drop factor nodes from being participating in the routing process. It always ensures that the data would be handled by the low dropping ratio nodes, thus the network's lifetime is improved.
Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks account for one third of all service downtime incidents. Current DoS/DDoS attacks are not only limited to knocking down online services, but they also disguise other malicious attacks such as delivering malware, data-theft, wire fraud and even extortion. Detection of these attacks is predominantly based on the packet data and metrics derived only from packets. This work proposes a host based DDoS detection framework called BRAIN: BehavioR based Adaptive Intrusion detection in Networks. BRAIN leverages already available Hardware Performance Counters in modern processors to model the application behavior using low-level hardware events. BRAIN combines network statistics and modeled application behavior to detect DDoS attacks using machine learning. Our experiments show that BRAIN can detect multiple types of DDoS attacks, including those are undetectable by existing tools with an accuracy of 99.8% and a false alarm rate of 0%.
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.