Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Jaidhar, J.

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    Secure data migration between cloud storage systems
    (Institute of Electrical and Electronics Engineers Inc., 2017) Sushma, M.; Jaidhar, J.; Gudisagar, C.; Sahoo, B.R.
    Cloud computing is a trending paradigm that combines several computing concepts and technologies of the Internet to create a platform for more agile, cost effective and reliable model for the public users, business applications and IT infrastructure. There are various requirements that need to be addressed by Cloud Service Provider (CSP) for enabling the cloud services to the users such as security, performance, availability, integrability, customization with minimal cost. If any of these requirements are not met, then the user wishes to switch from current CSP to a new CSP. To achieve that the user has to download all the digital assets, services, IT resources and applications from one CSP and upload into another CSP. This process has many issues like security, vendor management, technical integration, requirement of time and energy resources, etc; The first one being a major concern which we are addressing it in this paper. Here we propose a secure data migration technique to migrate the data from one cloud storage system to another cloud storage system. The proposed approach comprises of mutual authentication, blended with key splitting and sharing methods that ensure pre-migration authentication. The migration of data is then performed by encrypting with symmetric keys, which are shared using RSA Cryptosystem. The security factors such as confidentiality, authorization, authenticity, integrity are ensured by this technique. The proposed methodology is implemented and validated on two OpenStack servers using the Object Storage Service accessed by swiftclient. © 2017 IEEE.
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    Performance Evaluation of Filter-based Feature Selection Techniques in Classifying Portable Executable Files
    (Elsevier B.V., 2018) Shiva Darshan, S.L.; Jaidhar, J.
    The dimensionality of the feature space exhibits a significant effect on the processing time and predictive performance of the Malware Detection Systems (MDS). Therefore, the selection of relevant features is crucial for the classification process. Feature Selection Technique (FST) is a prominent solution that effectively reduces the dimensionality of the feature space by identifying and neglecting noisy or irrelevant features from the original feature space. The significant features recommended by FST uplift the malware detection rate. This paper provides the performance analysis of four chosen filter-based FSTs and their impact on the classifier decision. FSTs such as Distinguishing Feature Selector (DFS), Mutual Information (MI), Categorical Proportional Difference (CPD), and Darmstadt Indexing Approach (DIA) have been used in this work and their efficiency has been evaluated using different datasets, various feature-length, classifiers, and success measures. The experimental results explicitly indicate that DFS and MI offer a competitive performance in terms of better detection accuracy and that the efficiency of the classifiers does not decline on both the balanced and unbalanced datasets. © 2018 The Authors. Published by Elsevier B.V.
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    Assessment of objective functions under mobility in RPL
    (Springer Verlag, 2018) Sanshi, S.; Jaidhar, J.
    Due to the technological advancement in Low-power and Lossy Network (LLN), the sensor node mobility has become a basic requirement. Routing protocol designed for LLN must ensure certain requirements in a mobile environment such as reliability, flexibility, scalability to name a few. To meet the needs of LLN, Internet Engineering Task Force (IETF) released the standard IPv6 Routing Protocol for LLNs (RPL). RPL depends on Objective Function (OF) to select optimized routes from source to destination. However, the standard did not specify which OF to use. In this study, performance analysis of different OFs such as Objective Function zero (OF0), Energy-based Objective Function (OFE), Delay-Efficient Objective Function (OFDE), and Minimum Rank with Hysteresis Objective Function (MRHOF) is carried out under different mobility models, which makes this study unique. The metrics used to measure the performance are latency, packet delivery ratio (PDR), and power consumption. Simulation results demonstrate that under different mobility models, MRHOF achieved better results in terms of PDR and power consumption, while OFDE shows better results in terms of latency compared to other OFs. © Springer Nature Singapore Pte Ltd. 2018.