Tech-savvy users are striving to bring automation and digitization in their lifestyle to make life more comfortable and efficient; Internet of Things (IoT) is an enabler in this direction. Technology advancements and new business opportunities are rapidly changing the IoT adoption landscape, and thereby security and privacy concerns have also started raising and realizing. The increasing number of IP enabled electronic devices, enormous data generation, and communication traffic have enhanced the attack surface for security and privacy violators. Many security attack scenarios are the result of poor identification and authentication mechanisms of communicating entities. In this paper, we present a secure scheme to perform a business transaction initiated by a smart device in the IoT environment. Scheme performs dynamic authentication of a business transaction while ensuring the privacy of the associated user(s). This scheme relies on Message Authentication Code (MAC) and dynamic key generation method to achieve a secure workflow. In this paper, we present a pluggable Roaming Smart Meters (RSM) concept to demonstrate the applicability of the proposed authentication scheme.

Pertaining to the rapid usage of cloud computing, cloud based approaches are growing as an fascinating domain for numerous malignant tasks. Security is one of the vital issues faced by the cloud computing environment while sharing resources over the internet. Consumers are facing distinct security hazards while using cloud computing platform. Previous works mainly attempted to mitigate the side channels attacks by altering the infrastructure and the internal procedures of the cloud stack. However, the deployments of these alterations are not so easy and could not resist the attacks. In this paper, the authors attempted to solve the issues by enhancing the VM Placement policies in such a way that, it is complex for the invaders to collocate their object. A secure Dynamic VM placement approach is presented for the VM allocations into different servers in the cloud. The performance comparison of the suggested methodology is shows that the proposed approach has better efficiency evaluations such as hit rate, loss rate and resource loss when compared to other V M placement policies.

Texture refers to the variation of gray level tones in a local neighbourhood. The “local” texture information for a given pixel and its neighbourhood is characterized by the corresponding texture unit. Based on the concept of texture unit, this paper describes a new statistical approach to texture analysis, based on average of the both fuzzy left and right texture unit matrix. In this method the “local” texture information for a given pixel and its neighbourhood is characterized by the corresponding fuzzy texture unit. The proposed Average Fuzzy Left and Right Texture Unit (AFLRTU) matrices overcome the disadvantage of FTU by reducing the texture unit from 2020 to 79. The proposed scheme also overcomes the disadvantage of the left and right texture unit matrix (LRTM) by considering the texture unit numbers from all the 4 different LRTM’s instead of the minimum one as in the case of LRTM. The co-occurrence features extracted from the AFLRTU matrix provide complete texture information about an image, which is useful for texture classification. Classification performance is compared with the various fuzzy based texture classification methods. The results demonstrate that superior performance is achieved by the proposed method.