Summary
Wireless mesh networks (WMNs) have acquired recently enormous attention and momentum; therefore, security aspects have been a fundamental concern for them. Among catastrophic threats on WMNs, Denial‐of‐Service attacks that have become a severe danger because of their plug‐and‐play structural design. Unfortunately, preventing a Denial‐of‐Service attack presents a challenging issue. This fact is induced with the appearance of the source IP addresses spoofing. The resolution key of this issue is to reveal the attack source based on the path through which the attack packet passes. For this, many researchers in IP traceability field propose various methods and techniques to deal with the issue. In this article, we conceive a novel approach named out of band IP traceback approach in WMN (IEEE 802.11s). We create a new architecture using signaling messages for discovering the real source(s) of IP packets. Our solution is based on a security‐oriented signaling protocol. This protocol allows specialized signaling entities to communicate via reliable signaling information. This fact permits us to perform a simple and efficient traceback. In our novel approach, we use 2 radios: the first one transmits normal data packets whereas the second is reserved to exchange IP traceback information. The performance of the proposed scheme is analyzed via simulation analysis using the Network Simulator 3. The simulation results show that our scheme is efficient in dealing with the traceback problem in WMN environments.
In this paper, we propose a failure prediction methodology for quality of service (QoS) degradation prediction for publish/subscribe systems on MANET. Our propose is to use the Auto Regressive Integrated Moving Average (ARIMA) method to predict failure occurrence in the system and to provide optimal QoS provision of applications. Besides, our forecasting algorithm looks for the source behind QoS degradation using the Correlation method. Simulations results are performed to prove the efficiency of the proposed approach. A comparison is done proving that our proposal outperforms the Auto Regression (AR) based prediction approach.
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