#Mac address learning during routing software#

IEEE/ACM Transactions on Networking (TON), 13(5), 933–946.

Estimating flow distributions from sampled flow statistics. Miami Beach, Florida, USA.ĭuffield, N., Lund, C., & Thorup, M. In Proceedings of 3rd ACM SIGCOMM conference on Internet measurement. In Proceedings of 5th ACM/ICST international conference on simulation tools and techniques for communications, networks and systems: 5th ACM/ICST international workshop on OMNet++. A multi-channel IEEE 1609.4 and 802.11 p EDCA model for the veins framework. 26th IEEE international conference on computer communications. Dowitcher: Effective worm detection and containment in the internet core. Network anomaly detection and classification via opportunistic sampling. IEEE Network, 23, 5.Īndroulidakis, G., Chatzigiannakis, V., & Papavassiliou, S. Effective discovery of attacks using entropy of packet dynamics. The linear classification was done using the TensorFlow framework and evaluations were performed using the VEINS simulator using the p-persistence broadcast protocol in a US city area. These methods and detection techniques are based on the IEEE 802.11p MAC layer and weighted p-persistence multi-hop routing protocol. We are not concerned with the targeted attacks, but rather explore how the unintentional misbehavior, which can cause statistical multi-hop routing protocols to operate as basic flooding protocols, can be detected and accurately classified.

#Mac address learning during routing software#

Due to the dynamic nature of nodes in VANETs and routing complexity, unintentional misbehavior can also happen due to hardware or software failures in the vehicle. Misbehavior can be caused by a targeted attack, where an attacking vehicle can intentionally send or route malicious packets to harm. Nodes in Vehicular Ad Hoc Networks (VANETs) use broadcast protocols to efficiently disseminate safety information, but nodes do not always behave according to the routing protocols. A novel approach is proposed to detect protocol misbehavior using state-of-the-art machine learning frameworks and entropy.