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Reduction-of-Quality (RoQ) attack is a type of Distributed Denial-of-Service (DDoS) attack that is difficult to detect in current computing systems and networks. These RoQ attacks throttle the throughput heavily and reduce the Quality of Service (QoS) to end systems gradually rather than refusing the clients from the services completely. In this paper, we propose to develop a flow monitoring scheme to defend against such attacks in mobile Ad-hoc networks. Our proposed defense mechanism consists of a flow monitoring table (FMT) at each node to identify the attackers. If the channel continues to be congested because some sender nodes do not reduce their sending rate, it can be found by the destination using the updated FMT. Once the attackers are identified, all packets from those nodes will be blocked. By simulation results, we show that our proposed scheme achieves higher throughput and packet delivery ratio with reduced packet drop for legitimate users.

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In recent years, a considerable number of approaches have been proposed by the researchers to evaluate infectious diseases by examining the digital images of peripheral blood cell (PBC) recorded using microscopes. In this chapter, a semi-automated approach is proposed by integrating the Shannon's entropy (SE) thresholding and DRLS-based segmentation procedure to extract the stained blood cell from digital PBC pictures. This work implements a two-step practice with cuckoo search (CS) and SE-based pre-processing and DRLS-based post-processing procedure to examine the PBC pictures. During the experimentation, the PBC pictures are adopted from the database leukocyte images for segmentation and classification (LISC). The proposed approach is implemented by considering the RGB scale and gray scale version of the PBC pictures, and the performance of the proposed approach is confirmed by computing the picture similarity and statistical measures computed with the extracted stained blood cell with the ground truth image.

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