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Shape recognition and retrieval is a complex task on non-rigid objects and it can be effectively performed by using a set of compact shape descriptors. This paper presents a new technique for generating normalised contour points from shape silhouettes, which involves the identification of object contour from images and subsequently the object area normalisation (OAN) method is used to partition the object into equal part area segments with respect to shape centroid. Later, these contour points are used to derive six descriptors such as compact centroid distance (CCD), central angle (ANG), normalized points distance (NPD), centroid distance ratio (CDR), angular pattern descriptor (APD) and multi-triangle area representation (MTAR). These descriptors are a 1D shape feature vector which preserve contour and region information of the shapes. The performance of the proposed descriptors is evaluated on MPEG-7 Part-A, Part-B and multi-view curve dataset images. The present experiments are aimed to check proposed shape descriptor's robustness to affine invariance property and image retrieval performance. Comparative study has also been carried out for evaluating our approach with other state of the art approaches. The results show that image retrieval rate in OAN approach performs significantly better than that in other existing shape descriptors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Current trending- Internet of things (IoT) is internetworking of an assortment of hardware devices to offer a collection of applications and services. In the present-day world, ransomware cyber-attack has become one of the major attacks in IoT systems. Ransomware is a hazardous malware that targets the user’s computer inaccessible or inoperative, and then requesting the computer victim user to transfer a huge ransom to relapse the damage. At instance, the evolution rate outcomes illustrate that the level of attacks such as Locky and Cryptowall ransomware are conspicuously growing then other ransomware. Thus, these ransomware relations are the latent threat to IoT. To address the issue, this paper presents Two-phase ransomware prediction model based on the behavioral and communication study of Cryptowall ransomware for IoT networks. This proposed Two-phase model equipped with, Phase-1: observes the inward TCP/IP flowing traffic through a monitoring server to avert the ransomware attack The procedure of the monitoring server is to monitor the IoT's TCP/IP. The process of Monitoring TCP/IP is to extract TCP/IP header and routines command and control (C&C) server IP blacklisting to discover the ransomware attacks. In Phase-2: the proposed system will also analyze the application pattern for malicious behavior of the Web and URLs. Several societies have very affluent security tools in their milieu, but their events or logs are not monitored, which make affluent tools ineffective. The process of having efficient security based monitoring server is vital for detecting and controlling the ransomware attack.