Search

An Unmanned Aerial Vehicle (UAV) network specifies a novel type of Mobile Ad hoc Network (MANET) in which drones serve as nodes and facilitate the retransmission of messages to their final destinations. Aside from its military application, it has recently begun to seep into the civilian sector. Similar to MANET and vehicular ad hoc networks, Flying Ad hoc Networks (FANET) are a subset of ad hoc networks. An FANET is different because it is founded on UAVs. Due to the characteristics of this sort of network, which is defined by a highly changing topology in a 3D environment, we must employ an adjusted configuration method to ensure good routing performance. Therefore, to deal with this problem, a technique that responds to any change in topology by always finding the best route is required. In this work, we propose a new protocol based on the hybrid optimization of the 2-opt heuristic and Honey Badger Algorithm (HBA), called HB-AODV. In order to locate its prey, a badger must move slowly and continuously while using scent markers and mouse-digging skills to catch it. In other words, the most efficient routes in terms of the number of hops are identified. Several simulations were conducted via the 3D version of Network Simulator (NS-2) on different deployment strategies. In comparison to AODV, DSDV, and AntHocNet, the obtained results demonstrated the proposed scheme’s good performance in terms of quality of service metrics.

In this paper a novel collective method for DDoS detection is introduced. The method is distributed and implemented as a multi-agent system, and where local decision is based on an information-based heuristic, namely the entropy. According the calculated entropy a router exchange data with its neighbors aiming at collectively decide if a DDoS is ongoing or not. Most of the works of the literature that are based on the entropy they have used source addresses. The authors' method is based on the entropy of the distances traveled by the packets, so spoofing IP packets will be hard to perform by hackers. Each router combines its decision with those of its neighbors. Such a collective detection allows to apply defense against the attack despite the victim is out of service or cannot perform DDoS mitigation because the traffic is congested in its neighborhood. Conducted experiments using the platform OMNet++ show the potential of the new method for efficient collaborative and distributed detection and mitigation of DDoS attacks.