Decentralization in every walk of life has resulted in the development of Global Grid networking. Data sharing and access depends on their availability, capability, cost and user requirements. One of the needs for a secure Grid Environment is a strong authentication for users. Since Authentication is the entry point into every network, a novel smart card based authentication scheme has been proposed. The proposed authentication scheme utilizes the biometric data embedded in a smart card along with the ID and password of the user. The Time efficient performance of the proposed scheme in comparison with the existing Secure Socket Layer based authentication scheme is discussed. The attacks which the proposed scheme is able to withstand are also discussed.

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Cloud computing has evolved over the years in providing various services to the end users. The cloud features makes it acceptable by the industries in leveraging most of its applications in to the cloud. Security concerns exist in most cloud platforms and are prone to various attacks. This paper focuses on various security mechanisms that are provided in the enterprises and also discusses few of the common security mechanisms like authentication, authorization, encryption and access control. The methods deployed within each security mechanisms are also analyzed.

In this new era of cloud computing, Intrusion Detection System (IDS) is very essential for the continual monitoring of computing resources for signs of compromise since the number of attack vectors and malware are in increase. Only few IDS datasets are publicly available and those available are outdated, lack cloud‐specific attacks. This article presents a novel dataset based on Virtual Machine Introspected data for the implementation of IDS in cloud. The dataset was generated from the behavioral characteristics of malware and benign sample execution traces on virtual machines using Virtual Machine Introspection (VMI) technique. A vector space model based on system call approach is applied to analyze the behavioral characteristics for the generation of proposed dataset. The purpose of this study is to compare the proposed dataset with existing datasets and evaluate the effectiveness of these datasets by applying Machine Learning (ML) algorithms with 10‐fold cross‐validation. The ML algorithms used in the experiments are C4.5, Random Forest, JRip, NaiveBayes, K‐Nearest Neighbors (KNN), and Support Vector Machine (SVM). The effectiveness of detecting intrusions using proposed dataset is promising compared with other datasets in‐terms of intrusion detection accuracy, recall value, precision, and F1‐score metrics. For example, the intrusion detection accuracy in proposed dataset is 0.11% improved than UNM dataset, 6.28% higher than ADFA dataset, and 1.88% higher than LID dataset with C4.5 algorithm. Therefore, the proposed dataset is best suitable for implementing IDS for cloud.

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