Thermal-energy-efficient-secured-link reliable and delay-aware routing protocol (TESLDAR) for wireless body area networks

Mohanty, Rasmita Kumari; Sahoo, Satya Prakash; Kabat, Manas Ranjan

doi:10.1080/1206212x.2024.2380675

International Journal of Computers and Applications

2024

DOI: 10.1080/1206212x.2024.2380675

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Thermal-energy-efficient-secured-link reliable and delay-aware routing protocol (TESLDAR) for wireless body area networks

Rasmita Kumari Mohanty,

Satya Prakash Sahoo,

Manas Ranjan Kabat

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2024

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Unified Threat Modeling

Mohanty,

Padmaja,

Kanaparthi

et al. 2024

Advances in Educational Technologies and Instructional Design

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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.

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Unified Threat Modeling

Mohanty,

Padmaja,

Kanaparthi

et al. 2024

Advances in Educational Technologies and Instructional Design

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Deep Learning for Analyzing User and Entity Behaviors

Mohanty,

Kumar,

Padmaja

et al. 2024

Advances in Business Strategy and Competitive Advantage

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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.

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Thermal-energy-efficient-secured-link reliable and delay-aware routing protocol (TESLDAR) for wireless body area networks

Cited by 2 publications

References 22 publications

Unified Threat Modeling

Unified Threat Modeling

Deep Learning for Analyzing User and Entity Behaviors

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