Browsing by Author "Reddy, K.H.K."

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A hierarchical blockchain architecture for secure data sharing for vehicular networks
(Springer Science and Business Media B.V., 2023) Srinivasan, K.S.; Divakarla, U.; Chandrasekaran, K.; Reddy, K.H.K.
Data sharing is common phenomenon between the stakeholders within an organization and it is vital to sustain in the context of application. Internet of Things (IoT) is an umbrella of all applications’ online services like smart city, smart agriculture, smart grid and smart vehicles. In case of autonomous vehicular network (AVN), the data generated by vehicles, sensing units and road side units (RSU) is sensitive in nature so, secure data sharing (SDS) in AVN is the prime importance. In the area of SDS, Blockchain is gaining popularity which is an immutable distributed ledger technology and emerged as one of the prime solutions towards secure data sharing. As an innovative contribution, we proposedBlockchain based hierarchical secure data sharing model for sharing within vehicular network (VN). The proposed architecture is able to share road traffic related information e.g., road conditions, traffic congestion with the nearby vehicles and other stakeholders in the vehicular network. The performance of the proposed model is analyzed by using a simulation study and the efficacy of the simulated results outperforms than that of existing models. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.
A Novel Approach towards Windows Malware Detection System Using Deep Neural Networks
(Elsevier B.V., 2022) Divakarla, U.; Reddy, K.H.K.; Chandrasekaran, K.
Now-a-day's malicious software is increasing in numbers and at present becomes more harmful for any digital equipment like mobile, tablet, and computers. Traditional techniques like static and dynamic analysis, signature-based detection methods are become absolute and not effective at all. The advanced techniques like code encryption and code packing techniques can be used to hide detection; polymorphic malware is a new class of malware that changes their code structure from time to time to avoid detection, so there is a need for an intelligent system which can efficiently analyze the features of a new, unknown executable file and classify it correctly. There have been learning-based malware detection systems proposed in the literature, but most of those proposed approaches present a high accuracy over a small dataset, whereas the performance is very poor over industry-standard datasets. Operating system like windows is always in prime malware target because of the sheer high number of users. This paper proposes a simple, deep learning-based detection approachthat classifies a specified executable into benign or harmful. It has been trained using EMBER, an industry-level Windows malware dataset and tests with an accuracy of 87.76%. Â© 2023 The Authors. Published by Elsevier B.V.
USING JIFF FOR COLLABORATIVE MEDICAL DATA ANALYSIS WITH SECURE MULTIPARTY COMPUTATION
(Faculty of Engineering, University of Kragujevac, 2025) Divakarla, U.; Chandrasekaran, K.; Reddy, K.H.K.
In collaborative data analysis, secure multiparty computation can be utilised to compute statistical functions in a private manner. For the purpose of developing apps that require safe multi-party collaboration, JIFF is an open-source JavaScript library. We use JIFF to create an application that allows two parties to collaborate on medical data analysis, including the processing of sensitive patient data while maintaining data confidentiality and privacy. A decentralized system that ensures data security and secure computation of private information is provided by the JIFF framework. We assess the system's functionality and privacy-preserving skills, proving that it can effectively protect data privacy while still producing reliable analysis findings. © 2025 Published by Faculty of Engineeringg.