Biblio

Cognitive Radio (CR) is an attractive solution in mobile communication for solving the spectrum scarcity problem. Moreover, security concerns are not yet fully satisfied. This article focuses on attacks such as the Primary user emulation attack (PUE) and the jammer attack. These attacks create anomalous spectrum access thereby disturbing the dynamic spectrum usage in the CR networks. A framework based on cross-layer has been designed effectively to determine these attacks in the CR networks. First, each secondary user will sense the spectrum in the physical layer and construct a feature space. Using the extracted features, the clusters are formed effectively for each user. In the network layer, multipath routing is employed to discover the routes for the secondary user. If the node in the path identifies any spectrum shortage, it will verify that location with the help of constructed cluster. If the node does not belong to any of the clusters, then it will be identified as the attacker node. Simulation results and security analysis are performed using the NS2 simulations, which show improvement in detection of the attacks, decrease in the detection delay, and less route dis-connectivity. The proposed cross-layer framework identifies the anomalous spectrum access attack effectively.

The burgeoning Internet of Things (IoT) has offered unprecedented opportunities for innovations and applications that are continuously changing our life. At the same time, the large amount of pervasive IoT applications have posed paramount threats to the user's security and privacy. While a lot of efforts have been dedicated to deal with such threats from the hardware, the software, and the applications, in this paper, we argue and envision that more effective and comprehensive protection for IoT systems can only be achieved via a cross-layer approach. As such, we present our initial design of XLF, a cross-layer framework towards this goal. XLF can secure the IoT systems not only from each individual layer of device, network, and service, but also through the information aggregation and correlation of different layers.

Mobile Ad hoc Network (MANET) is one of the most popular dynamic topology reconfigurable local wireless network standards. Distributed Denial of Services is one of the most challenging threats in such a network. Flooding attack is one of the forms of DDoS attack whereby certain nodes in the network miss-utilizes the allocated channel by flooding packets with very high packet rate to it's neighbors, causing a fast energy loss to the neighbors and causing other legitimate nodes a denial of routing and transmission services from these nodes. In this work we propose a novel link layer assessment based flooding attack detection and prevention method. MAC layer of the nodes analyzes the signal properties and incorporated into the routing table by a cross layer MAC/Network interface. Once a node is marked as a flooding node, it is blacklisted in the routing table and is communicated to MAC through Network/MAC cross layer interface. Results shows that the proposed technique produces more accurate flooding attack detection in comparison to current state of art statistical analysis based flooding attack detection by network layer.