Rasmita Kumari Mohanty’s research while affiliated with VNR Vignana Jyothi Institute of Engineering & Technology and other places

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Publications (11)


The Rise of Generative AI Language Models: Challenges and Opportunities for Wireless Body Area Networks
  • Chapter

December 2024

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6 Reads

Rasmita Kumari Mohanty

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Basim Alhadidi

In the era of information and communication, wireless healthcare networks enable innovative applications to enhance the quality of patients' lives, provide useful monitoring tools for caregivers, and allow timely intervention. However, security concerns are still holding back the widespread adoption of this promising technology. Insecure data communication violates patients’ privacy and may endanger their lives due to improper medical diagnosis or treatment. Traditional security countermeasures, including authentication, encryption, and data integrity, are essential to protect the network from internal threats. This chapter starts with a concise introduction about Wireless Body Area Networks (WBAN) threats, and counter-measures are comprehensively researched with a particular focus on AI-enabled methods. Generative Artificial Intelligence (AI) language models, such as GPT-4, have transformed several domains by offering sophisticated data processing and interaction features. In health monitoring and medical diagnostics, where secure, dependable, and efficient data transmission is essential, WBANs play a critical role, including generative AI models that can simplify processes, improve patient outcomes, and strengthen data analysis. Finally, traditional security is discussed, followed by challenges and applications.



Unified Threat Modeling: Strategies for Comprehensive Risk Assessment in Modern Systems

October 2024

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9 Reads

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1 Citation

Today, in the digital world, the security of systems is crucial because, with continuous information exchange and huge quantities of data being processed, the protection of operation processes and data assets becomes paramount. Threat modeling is an important part of cybersecurity management methodology that looks for any possible threats or weaknesses that can break the system or endanger the environment. This paper is an attempt to analyze all the models of threat modeling by taking STRIDE, PASTA, DREAD, TREK, VAST and Attack Trees as references. An integrated model is suggested that combines the benefits of existing approaches, which includes the adoption of a comprehensive frame to deal with cyber threats. This methodology emphasizes iterative refinement and rigorous testing to ensure the effectiveness of threat mitigation strategies. By incorporating user-friendly web portals and the integration of new technologies, this framework enhances usability and addresses emerging threats.


Decentralized Fundraising: A Web3 Crowdfunding Platform With Blockchain Integration

October 2024

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15 Reads

This chapter introduces a Web3 crowdfunding platform with blockchain integration, enabling decentralized fundraising campaigns and offering an intuitive user experience. The platform includes MetaMask for wallet interface, Solidity for creating and deploying smart contracts, and efficient Ethereum transaction network connectivity. The platform uses the transparency, security, and decentralization of blockchain to transform conventional fundraising. With a user-friendly layout and dynamic user interface, users can establish and take part in fundraising campaigns that are driven by smart contracts. Users may securely link their wallets, manage funds, and sign transactions using the platform's seamless integration of the well-known Ethereum wallet extension MetaMask. To manage wallets, the platform also interfaces with MetaMask, enabling a speedy and secure exchange of Ethereum. Solidity-written smart contracts enforce campaign-specific rules and streamline the contribution-handling process.


Deep Learning for Analyzing User and Entity Behaviors: Techniques and Applications

August 2024

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42 Reads

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4 Citations

User and entity behavior analytics (UEBA) is a critical component of modern cybersecurity strategies aimed at detecting and mitigating security threats within enterprise environments. The system is designed to enhance security through the analysis of user and entity actions. The architecture encompasses data collection and integration techniques, feature extraction, deep learning models, detecting, and analyzing. Data collection strategies acquire statistics from quite a few sources, such as system logs, network site visitors, and application logs, resulting in a comprehensive dataset for analysis. Feature extraction transforms uncooked facts right into a meaningful representation, for reading a deep modelling can locate patterns in person conduct and entity conduct. Long-term and brief-term reminiscence (LSTM) and convolutional LSTM (ConvLSTM) fashions are used to investigate temporal, spatial, and temporal dependence, respectively. It detects irregularities and makes immediate corrections.





Architecture of the projected model
Node Deployment
Clustering and CH selection
TDMA-based Scheduling
Network lifetime analysis

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Sustainable remote patient monitoring in wireless body area network with Multi-hop routing and scheduling: a four-fold objective based optimization approach
  • Article
  • Publisher preview available

March 2023

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114 Reads

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16 Citations

Wireless Networks

Wireless body area networks (WBAN) are essential for patient monitoring and reducing lengths of stay in hospitals. Our research's objective was to create a scheduling strategy and energy-efficient routing for WBANs that was energy-efficient while also enhancing network life. The projected model includes four major phases: (a) Network setup and clustering, (b) Next-hop-node selection (c) Multi-hop routing, and (d) Scheduling. The nodes (sensors embedded in the patient) are clustered during the network creation phase, and the Cluster Head (CH) for data transmission to sink is chosen using the new Modernized Stackelberg Game Theory. Then, the optimal next-hop node for data transmission (from CH to sink) is accomplished via a new optimized four-fold-objectives model, to reduce the network's overall energy usage by balancing the energy use among the sensor nodes. The four-fold objectives like energy, Link quality, Mobility, and Trust Value are taken into consideration during the selection of the optimal path. Moreover, a new Enhanced Glow Worm Swarm Optimization (En-GWSO) model is designed to find the optimal path among the available paths. This En-GWSO model is an extended version of the standard GWSO model. The data reaches the sink via the optimal path, and hence the data transmission delay is reduced. From the sink, the data is transmitted via the routers in a multi-hop fashion. Further, at the receiver end, the scheduling takes place. In the scheduling phase, the data from the sink node is sent in their time slots i.e., Time Division Multiple Access (TDMA) to the concerned receiver (doctor/emergency/hospital). Finally, a comparative evaluation is made to validate the efficiency of the projected model.

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Figure 2:Hybrid CNN-RNN
Multiobjective Routing Protocol Design in WBAN Using Optimized Fuzzy Clustering and Hybrid Deep Learning for Optimal Path Selection

March 2023

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65 Reads

Real-time monitoring of patients' health conditions is now possible because to wireless body area networks (WBANs). In WBANs, nodes are placed inside, on, or around the human body to collect a range of physiological data, such as body temperature, heart rate, and blood pressure. The routing of data packets in WBANs, however, confronts a number of difficulties because of the dynamic nature of the body, including limited power, interference, and movement. Data packet routing can be made more efficient by using a multiobjective routing protocol architecture to handle these problems. In this research work, WBAN's multiobjective routing protocol architecture is made possible by two phases as clustering and optimal path selection.To group nodes based on several objectives, such as energy consumption, transmission delay, and network longevity, the proposed multiobjective routing protocol design in WBAN uses an optimised Fuzzy C-means clustering method. The membership function in Fuzzy C-means clustering is determined using a novel hybrid optimization model HBWBF that combines the Black Widow Optimization (BWO) and the Bacterial Foraging Optimization Algorithm (BFOA). The hybrid deep learning approach for determining the optimal path between clusters combines Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).The proposed model is implemented in MATLAB. To validate the efficiency of the proposed model, a comparative evaluation is performed.


Citations (4)


... Existing systems like User and Entity Behavior Analytics (UEBA) provide foundational capabilities in this domain [6]. These tools analyze data from logs, network traffic, and other sources to identify unusual behaviors. ...

Reference:

Autonomous Identity-Based Threat Segmentation in Zero Trust Architectures
Deep Learning for Analyzing User and Entity Behaviors: Techniques and Applications
  • Citing Chapter
  • August 2024

... Their successful solution is also associated with the involvement of the functionality of all levels of the OSI (Open System Interconnection) model. In this regard, the term "secure routing" appeared when determining routes; in addition to QoS indicators, it is necessary to consider network security indicators [9][10][11][12]. The traditional approach is to use an option where the composite protocol metric will additionally take into account another indicator related to network security. ...

A Network Reliability based Secure Routing Protocol (NRSRP) for Secure Transmission in Wireless Body Area Network
  • Citing Conference Paper
  • June 2023

... Employing a network of peripheral and injectable gauges, Wireless Body Area Networks (WBANs) provide an ongoing record of physiological data, marking a significant development in both personal exercise and treatment. These networks offer real-time data on characteristics like blood pressure, blood sugar levels, and sports participation, which is helpful in managing chronic diseases, post-operative care, and Overall well-being [2]. The information is then transmitted to a sink node, and the hub makes sure that the received data is forwarded to a remote professional via the world wide web for a medical assessment [2]. ...

Sustainable remote patient monitoring in wireless body area network with Multi-hop routing and scheduling: a four-fold objective based optimization approach

Wireless Networks

... The structure of a WBAN is designed to enable continuous and noninvasive monitoring of a patient's health status. This can be particularly useful for people with chronic medical conditions or those requiring close monitoring during surgery recovery [27]. Few recent applications have seen the development and deploy-Sensors 2023, 23, 7435 7 of 36 ment of WBANs to improve the lives of people with mental and physical disabilities, specific diseases, and pregnant women. ...

A Survey on Emerging Technologies in Wireless Body Area Network
  • Citing Conference Paper
  • October 2022