SECURITY IN CLOUD COMPUTING IN INDIAN GOVERNMENT

–Today Security issue is the topmost problem in the cloud computing environment. It leads to serious discomfort to the Governance and end-users. Numerous security solutions and policies are available however practically ineffective in use. Most of the security solutions are centered towards cloud technology and cloud service providers only and no consideration has been given to the Network, accessing, and device securities at the end-user level. The discomfort at the end-user level was left untreated. The security of the various public, private networks, variety of devices used by end-users, accessibility, and capacity of end-users is left untreated. This leads towards the strong need for the possible modification of the security architecture for data security at all levels and secured service delivery. This leads towards the strong need for the possible adaption of modified security measures and provisions, which shall provide secured hosting and service delivery at all levels and reduce the security gap between the cloud service providers and end-users. This paper investigates the study and analyze the security architecture in the Cloud environment of Govt. of India and suggest the modifications in the security architecture as per the changing scenario and to fulfill the future needs for the secured service delivery from central up to the end-user level. Keywords: Cloud Security, Security in GI Cloud, Cloud Security measures, Security Assessment in GI Cloud, Proposed Security for GI cloud

DAM: A Theoretical Framework for SensorSecurity in IoT Applications

As IoT applications are pervasively deployed across multiple domains, the potential impact of their security vulnerabilities are also accentuated. Sensor nodes represent a critical security vulnerability in the IoT ecosystem as they are exposed to the environment and accessible to hackers. When compromised or manipulated, sensor nodes can transmit incorrect data which can have a damaging impact on the overall operation and effectiveness of the system. Researchers have addressed the security vulnerabilities in sensor nodes with several mechanisms being proposed to address them. This paper presents DAM (Detect, Avoid, Mitigate), a theoretical framework to evaluate the security threats and solutions for sensor security in IoT applications and deployments. The framework leads to the classification of sensor security threats and categorization of available solutions which can be used to either detect vulnerabilities and attacks, recover from them or completely avoid them. The proposed framework will be useful for evaluating sensor security in real-world IoT deployments in terms of potential threats and designing possible solution

Deep Neural Network Based Multi-Review Summarization System

E-commerce is prevalent everywhere now-a-days. While shopping from these sites, users generally go through the reviews of the product posted by other users. For a given product, thousands of reviews may be available and it is cumbersome for the user to analyze each and every review. This paper proposes a multi-review summarization method to get a summarized review of products. A deep neural network-based model is employed to create an extractive summary of the reviews collected from online e-commerce sites i.e. Amazon and Flipkart. The deep neural network has been used to obtain the features of the product from multi reviews and cluster the sentences based on learned features. After clustering, a ranking of sentences is done and hence, an extractive summary is generated by selecting top n sentences from each of the clusters formed.

Balanced Multipath Transport Protocol for Mitigating MPTCP Incast in Data Center Networks

Recent data centers provide dense inter-connectivity between each pair of servers through multiple paths. These data centers offer high aggregate bandwidth and robustness by using multiple paths simultaneously. Multipath TCP (MPTCP) protocol is developed for improving throughput, fairly sharing network link capacity and providing robustness during path failure by utilizing multiple paths over multi-homed data center networks. Running MPTCP protocol for latency-sensitive rack-local short flows with many-to-one communication pattern at the access layer of multi-homed data center networks creates MPTCP incast problem. In this paper, Balanced Multipath TCP (BMPTCP) protocol is proposed to mitigate MPTCP incast problem in multi-homed data center networks. BMPTCP is a window-based congestion control protocol that prevents constant growth of each worker’s subflow congestion window size. BMPTCP computes identical congestion window size for all concurrent subflows by considering bottleneck Top of Rack (ToR) switch buffer size and increasing count of concurrently transmitting workers. This helps BMPTCP to avoid timeout events due to full window loss at ToR switch. Based on current congestion situation at ToR switches, BMPTCP adjust transmission rates of each worker’s subflow so that total amount of data transmitted by all concurrent subflows does not overflow bottleneck ToR switch buffer. Simulation results show that BMPTCP effectively alleviates MPTCP incast. It improves goodput, reduces flow completion time as compared to existing MPTCP and EW-MPTCP protocols.

Application of IoT in Different Aspects of Child Care: Literature Review and Classification

Child care is one of the most responsible and rewarding jobs of all, but due to several other obligations of parents it is becoming increasingly stressful to ensure their wellbeing and safety throughout the day. Recently adoption of IoT in different folds of child care has been welcomed with open arms. Some of the most addressed areas of applications in child care involve monitoring infants, tracking a school going child, tracking nutrition intake, constant supervision of their health, providing an interactive play mate to the child in the form of toys equipped with Internet of Toys (IoToys) technology etc. Despite all the glorious advantages, there is a tremendous risk factor involved as children are the most vulnerable section of society. In this paper a systematic literature review is conducted and an attempt is made to critically analyse the dual effect of IoT in complementing the traditional process of child care. However IoT healthcare applications for children have not been explored in this paper as a substantial amount of survey literature already exists. The contribution of this paper is to provide an overall insight to the potential researchers about the issues that needed immediate attention and also intended to benefit the decision making of IoT consumers.

Transformation Invariant Real-time Recognition of Indian Sign Language using Feature Fusion

Sign Language Recognition (SLR) is immerging as current area of research in the field of machine learning. SLR system recognizes gestures of sign language and converts them into text/voice thus making the communication possible between deaf and ordinary people. Acceptable performance of such system demands invariance of the output with respect to certain transformations of the input. In this paper, we introduce the real time hand gesture recognition system for Indian Sign Language (ISL). In order to obtain very high recognition accuracy, we propose a hybrid feature vector by combining shape oriented features like Fourier Descriptors and region oriented features like Hu Moments & Zernike Moments. Support Vector Machine (SVM) classifier is trained using feature vectors of images of training dataset. During experiment it is found that the proposed hybrid feature vector enhanced the performance of the system by compactly representing the fundamentals of invariance with respect transformation like scaling, translation and rotation. Being invariant with respect to transformation, system is easy to use and achieved a recognition rate of 95.79%.

GWO Optimized K-Means Cluster based Oversampling Algorithm

Skewed data distribution prevails in many real world applications. The skewedness is due to imbalance in the class distribution and it deteriorates the performance of the traditional classification algorithms. In this paper, we provide a Grey wolf optimized K-Means cluster based oversampling algorithm to handle the skewedness and solve the imbalanced data classification problem. Experiments are conducted on the proposed algorithm and compared it with the benchmarking popular algorithms. The results reveal that the proposed algorithm outperforms the other benchmarking algorithms.

Improvise Route Discovery and Radio Duty Cycle in WSN using SDN.

In the conventional Wireless Sensor Network, every mote role is to collect data, discover route and then sends the packet to its neighbor to reach a destination, consequently demanding both computation time and power. Pertaining to same, a promising framework is required to mitigate both power consumption and computational costs of the nodes inside the network. It also required sufficient planning and proper execution of the strategies. In this way, an attempted to gain benefits of a Software Defined Network (SDN) approach in Wireless Sensor Networks (WSN) We have proposed a W-SDN framework for the traditional network using OpenFlow protocol and controller. The goal is to investigate the significant impact of SDN in WSN. Result analysis of the designed framework is proposed using the following parameters Radio Duty cycle, flow request, delay, and latency for QoS