Detection and Prevention of Blackhole Attack in the AOMDV Routing Protocol

Mobile ad-hoc network is a collection of dynamically organized nodes where each node acts as a host and router. Mobile ad-hoc networks are characterized by the lack of preexisting infrastructures or centralized administration. So, they are vulnerable to several types of attacks, especially the Blackhole attack. This attack is one of the most serious attacks in this kind of mobile networks. In this type of attack, the malicious node sends a false answer indicating that it has the shortest path to the destination node by increasing the sequence number and decreasing the number of hops. This will have a significant negative impact on source nodes which send their data packets through the malicious node to the destination. This malicious node drop received data packets and absorbs all network traffic. In order overcome this problem, securing routing protocols become a very important requirement in mobile ad-hoc networks. Multipath routing protocols are among the protocols affected by the Blackhole attack. In this paper, we propose an effective and efficient technique that avoids misbehavior of Blackhole nodes and facilitates the discovery for the most reliable paths for the secure transmission of data packets between communicating nodes in the well-known Ad hoc On-demand multi-path routing protocol (AOMDV). We implement and simulate our proposed technique using the ns 2.35 simulator. We also compared on how the three routing protocols AOMDV, AOMDV under Blackhole attack (BHAOMDV), and the proposed solution to counter the Blackhole attack (IDSAOMDV) performs. The results show the degradation on how AOMDV under attack performs, it also presents similarities between normal AOMDV and the proposed solution by isolating misbehaving node which has resulted in increase the performance metrics to the standard values of the AOMDV protocol

An Improved Estimator of the Zenga Index for Heavy-Tailed Distributions

In the present paper, we focus on the Zenga index, the asymptotic normality of the classical estimators has been established in the literature under the classical assumption that the second moment of the loss variable is finite, this condition is very restrictive in practical applications. Such a result has been extended by Greselin et al. (2014) [31] in the case of distributions with infinite second moment. Thus, we base on this framework and propose a reduced-bias estimator for the classical estimators. Finally, we illustrate the efficiency of our approach by some results on a simulation study and compare its performance with other estimators