Recent studies have highlighted the importance of implementing clustering schemes in Wireless Body Area Networks (WBANs) to address challenges such as scalability, network topology changes, spectrum scarcity, and management. However, many existing approaches focus only on conventional performance metrics and overlook the integration of spectrum trading and efficient spectrum utilization. This paper proposes a novel clustering control scheme based on fuzzy logic and Nash equilibrium to enhance scalability, network stability, and resource management in WBANs. Our approach employs multi-criteria decision-making to optimize cluster head (CH) selection and routing strategies using reinforcement learning to achieve quality of service (QoS). Additionally, a secure lightweight Diffie–Hellman key exchange is used to protect data transmission. The proposed protocol outperforms existing protocols, including TAFLR, EQRSRL, and SEBA, in terms of throughput (3.2 kbps), packet delivery ratio (93%), delay (0.31 s), cluster efficiency (95%), and energy consumption (0.43 J).

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.

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.