Browsing by Author "Achar, Sandesh"

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Confimizer: A Novel Algorithm to Optimize Cloud Resource by Confidentiality-Cost Trade-Off Using BiLSTM Network
(IEEE, 2023-08-15) Achar, Sandesh; Faruqui, Nuruzzaman; Bodepudi, Anusha; Reddy, Manjunath
The world is expiring a 23% annual data growth rate and is projected to have a total surplus volume of 175 Zettabytes by 2025. It imposes significant challenges for small to medium-sized businesses to allocate funds for large-size data storage. The initial large upfront and maintenance costs have made cloud storage services popular. It comes with confidentiality concerns. Encrypting data before storing it in cloud storage is the most effective solution to this challenge. Encrypting and decrypting large volumes of data allocate massive amounts of expensive resources. Storing in plain text reduces system load and expenditure but introduces confidentiality concerns. This paper proposed a Confimizer, a novel algorithm, to optimize cloud resources and reduce costs by balancing the trade-off between confidentiality and cost. It reduces the system overload by 13.75%, saving 9.20% expenditure. It saves 12.33% storage and reduces API calls by 52.99%. The Confimizer uses an optimized BiLSTM network that classifies data according to the confidentiality level by 84.00% accuracy, 76.92% precision, 74.47% recall, and 75.01 F1 score. The innovative approach, optimized BiLSTM network architecture, and outstanding performance of the Confimizer make it a unique and effective cloud resource optimization algorithm.
Cyber-Physical System Security Based on Human Activity Recognition through IoT Cloud Computing
(MDPI Publications, 2023-04-17) Achar, Sandesh; Faruqui, Nuruzzaman; Whaiduzzaman, Md.; Awajan, Albara; Alazab, Moutaz
Cyber-physical security is vital for protecting key computing infrastructure against cyber attacks. Individuals, corporations, and society can all suffer considerable digital asset losses due to cyber attacks, including data loss, theft, financial loss, reputation harm, company interruption, infrastructure damage, ransomware attacks, and espionage. A cyber-physical attack harms both digital and physical assets. Cyber-physical system security is more challenging than software-level cyber security because it requires physical inspection and monitoring. This paper proposes an innovative and effective algorithm to strengthen cyber-physical security (CPS) with minimal human intervention. It is an approach based on human activity recognition (HAR), where GoogleNet–BiLSTM network hybridization has been used to recognize suspicious activities in the cyber-physical infrastructure perimeter. The proposed HAR-CPS algorithm classifies suspicious activities from real-time video surveillance with an average accuracy of 73.15%. It incorporates machine vision at the IoT edge (Mez) technology to make the system latency tolerant. Dual-layer security has been ensured by operating the proposed algorithm and the GoogleNet–BiLSTM hybrid network from a cloud server, which ensures the security of the proposed security system. The innovative optimization scheme makes it possible to strengthen cyber-physical security at only USD 4.29±0.29 per month.
Edge-Cloud Synergy for AI-Enhanced Sensor Network Data: A Real-Time Predictive Maintenance Framework
(Scopus, 2024-12-11) Sathupadi, Kaushik; Achar, Sandesh; Wadud, M. Abdullah-Al-; Bhaskaran, Shinoy Vengaramkode; Faruqui, Nuruzzaman; Uddin, Jia
Sensor networks generate vast amounts of data in real-time, which challenges existing predictive maintenance frameworks due to high latency, energy consumption, and bandwidth requirements. This research addresses these limitations by proposing an edge-cloud hybrid framework, leveraging edge devices for immediate anomaly detection and cloud servers for in-depth failure prediction. A K-Nearest Neighbors (KNNs) model is deployed on edge devices to detect anomalies in real-time, reducing the need for continuous data transfer to the cloud. Meanwhile, a Long Short-Term Memory (LSTM) model in the cloud analyzes time-series data for predictive failure analysis, enhancing maintenance scheduling and operational efficiency. The framework’s dynamic workload management algorithm optimizes task distribution between edge and cloud resources, balancing latency, bandwidth usage, and energy consumption. Experimental results show that the hybrid approach achieves a 35% reduction in latency, a 28% decrease in energy consumption, and a 60% reduction in bandwidth usage compared to cloud-only solutions. This framework offers a scalable, efficient solution for real-time predictive maintenance, making it highly applicable to resource-constrained, data-intensive environments.