Insider threats represent one of the most critical challenges in modern cybersecurity. These threats arise from individuals within an organization who misuse their legitimate access to harm the organization's assets, data, or operations. Traditional security mechanisms, primarily designed for external attackers, fall short in identifying these subtle and context-aware threats. In this paper, we propose a novel framework for real-time detection of insider threats using behavioral analytics combined with deep evidential clustering. Our system captures and analyzes user activities, applies context-rich behavioral features, and classifies potential threats using a deep evidential clustering model that estimates both cluster assignment and epistemic uncertainty. The proposed model dynamically adapts to behavioral changes and significantly reduces false positives. We evaluate our framework on benchmark insider threat datasets such as CERT and TWOS, achieving an average detection accuracy of 94.7% and a 38% reduction in false positives compared to traditional clustering methods. Our results demonstrate the effectiveness of integrating uncertainty modeling in threat detection pipelines. This research provides actionable insights for deploying intelligent, adaptive, and robust insider threat detection systems across various enterprise environments.

We develop and evaluate two novel purpose-built deep learning (DL) models for synthesis of the arterial blood pressure (ABP) waveform in a cuff-less manner, using a single-site photoplethysmography (PPG) signal. We train and evaluate our DL models on the data of 209 subjects from the public UCI dataset on cuff-less blood pressure (CLBP) estimation. Our transformer model consists of an encoder-decoder pair that incorporates positional encoding, multi-head attention, layer normalization, and dropout techniques for ABP waveform synthesis. Secondly, under our frequency-domain (FD) learning approach, we first obtain the discrete cosine transform (DCT) coefficients of the PPG and ABP signals, and then learn a linear/non-linear (L/NL) regression between them. The transformer model (FD L/NL model) synthesizes the ABP waveform with a mean absolute error (MAE) of 3.01 (4.23). Further, the synthesis of ABP waveform also allows us to estimate the systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. To this end, the transformer model reports an MAE of 3.77 mmHg and 2.69 mmHg, for SBP and DBP, respectively. On the other hand, the FD L/NL method reports an MAE of 4.37 mmHg and 3.91 mmHg, for SBP and DBP, respectively. Both methods fulfill the AAMI criterion. As for the BHS criterion, our transformer model (FD L/NL regression model) achieves grade A (grade B).

Address Resolution Protocol (ARP) spoofing remains a critical threat to IoT networks, enabling attackers to intercept, modify, or disrupt data transmission by exploiting ARP's lack of authentication. The decentralized and resource-constrained nature of IoT environments amplifies this vulnerability, making conventional detection mechanisms ineffective at scale. This paper introduces an intelligent, multi-layered machine learning framework designed to detect ARP spoofing in real-time IoT deployments. Our approach combines feature engineering based on ARP header behavior, traffic flow analysis, and temporal packet anomalies with a hybrid detection pipeline incorporating decision trees, ensemble models, and deep learning classifiers. We propose a hierarchical architecture to prioritize lightweight models at edge gateways and deeper models at centralized nodes to balance detection accuracy and computational efficiency. The system is validated on both simulated IoT traffic and the CICIDS2017 dataset, achieving over 97% detection accuracy with low false positive rates. Comparative evaluations with signature-based and rule-based systems demonstrate the robustness and generalizability of our approach. Our results show that intelligent machine learning integration enables proactive ARP spoofing detection tailored for IoT scenarios, laying the groundwork for scalable and autonomous network security solutions.

Wireless Body Area Network (WBAN) refers to short-range, wireless communications near or inside a human body. WBAN is emerging solution to cater the needs of local and remote health care related facility. Medical and non-medical applications have been revolutionarily under consideration for providing a healthy and gratify service to the humanity. Being very critical in communication from body it faces a lot of challenges which are to be tackled for the safety of life and benefit of the user. There is variety of challenges faced by WBANs. WBAN is favorite playground for attackers due to its usability in various applications. This article provides systematic overview of main challenges in WBANs in security perspectives.

This paper presents a protocol for enhancement of life time of WBAN network as well other protocol related issues such as throughput, path loss, and residual energy. Bio-sensors are used for deployment on human body. Poisson distribution and equilibrium model techniques have been used for attaining the required results. Multi-hop network topology and random network node deployment used to achieve minimum energy consumption and longer network lifetime.

A determination of the $C\!P$-even fraction $F_+$ in the decay $D^0 \to K^+K^-\pi^+\pi^-$ is presented. Using $2.93$ fb$^{-1}$ of $e^+e^-\to\psi(3770)\to D\bar{D}$ data collected by the BESIII detector, one charm meson is reconstructed in the signal mode and the other in a $C\!P$ eigenstate or the decay $D\to K_{S, L}^0\pi^+\pi^-$. Analysis of the relative rates of these double-tagged events yields the result $F_+ = 0.730 \pm 0.037 \pm 0.021$, where the first uncertainty is statistical and the second is systematic. This is the first model-independent measurement of $F_+$ in $D^0 \to K^+K^-\pi^+\pi^-$ decays.

Network security has become the biggest concern in the area of cyber security because of the exponential growth in computer networks and applications. Intrusion detection plays an important role in the security of information systems or networks devices. The purpose of an intrusion detection system (IDS) is to detect malicious activities and then generate an alarm against these activities. Having a large amount of data is one of the key problems in detecting attacks. Most of the intrusion detection systems use all features of datasets to evaluate the models and result in is, low detection rate, high computational time and uses of many computer resources. For fast attacks detection IDS needs a lightweight data. A feature selection method plays a key role to select best features to achieve maximum accuracy. This research work conduct experiments by considering on two updated attacks datasets, UNSW-NB15 and CICDDoS2019. This work suggests a wrapper based Genetic Algorithm (GA) features selection method with ensemble classifiers. GA select the best feature subsets and achieve high accuracy, detection rate (DR) and low false alarm rate (FAR) compared to existing approaches. This research focuses on multi-class classification. Implements two ensemble methods: stacking and bagging to detect different types of attacks. The results show that GA improve the accuracy significantly with stacking ensemble classifier.

Industrial Internet of Things (IIoT) systems have become integral to smart manufacturing, yet their growing connectivity has also exposed them to significant cybersecurity threats. Traditional intrusion detection systems (IDS) often rely on centralized architectures that raise concerns over data privacy, latency, and single points of failure. In this work, we propose a novel Federated Learning-Enhanced Blockchain Framework (FL-BCID) for privacy-preserving intrusion detection tailored for IIoT environments. Our architecture combines federated learning (FL) to ensure decentralized model training with blockchain technology to guarantee data integrity, trust, and tamper resistance across IIoT nodes. We design a lightweight intrusion detection model collaboratively trained using FL across edge devices without exposing sensitive data. A smart contract-enabled blockchain system records model updates and anomaly scores to establish accountability. Experimental evaluations using the ToN-IoT and N-BaIoT datasets demonstrate the superior performance of our framework, achieving 97.3% accuracy while reducing communication overhead by 41% compared to baseline centralized methods. Our approach ensures privacy, scalability, and robustness-critical for secure industrial operations. The proposed FL-BCID system provides a promising solution for enhancing trust and privacy in modern IIoT security architectures.

This paper aims to examine the composition of various spherically symmetric star models which are coupled with anisotropic configuration in $f(\mathcal{R},\mathcal{T},\mathcal{Q})$ gravity, where $\mathcal{Q}=\mathcal{R}_{\chi\xi}\mathcal{T}^{\chi\xi}$. We discuss the physical features of compact objects by employing bag model equation of state and construct the modified field equations in terms of Krori-Barua ansatz involving unknowns ($A,B,C$). The observational data of 4U 1820-30,~Vela X-I,~SAX J 1808.4-3658,~RXJ 1856-37 and Her X-I is used to calculate these unknowns and bag constant $\mathfrak{B_c}$. Further, we observe the behavior of energy density, radial and tangential pressure as well as anisotropy through graphical interpretation for a viable model $\mathcal{R}+\varrho\mathcal{Q}$ of this gravity. For a particular value of the coupling constant $\varrho$, we study the behavior of mass, compactness, redshift and the energy bounds. The stability of the considered stars is also checked by using two criteria. We conclude that our developed structure in this gravity is in well-agreement with all the physical requirements.

In this modern technological era, categorization and ranking of research journals is gaining popularity among researchers and scientists. It plays a significant role for publication of their research findings in a quality journal. Although, many research works exist on journal categorization and ranking, however, there is a lack of research works to categorize and predict the journals using suitable machine learning techniques. This work aims to categorize and predict various academic research journals. This work suggests a hybrid predictive model comprising of five steps. The first step is to prepare the dataset with twenty features. The second step is to pre-process the dataset. The third step is to apply an appropriate clustering algorithm for categorization. The fourth step is to apply appropriate feature selection techniques to get an effective subset of features. The fifth step involves some ensemble plus non ensemble methods to train the model. The model is trained on a full set of features, and a selected subset of features is obtained by applying three feature selection techniques. After model training, the prediction results are evaluated in terms of precision, recall, and accuracy. The results can help the researchers and the practitioners in predicting the journal category.

This paper focuses on the analysis of static spherically symmetric anisotropic solutions in the presence of electromagnetic field through the gravitational decoupling approach in $f(\mathcal{R},\mathcal{T},\mathcal{R}_{\gamma\upsilon}\mathcal{T}^{\gamma\upsilon})$ gravity. We use geometric deformation only on radial metric function and obtain two sets of the field equations. The first set deals with isotropic fluid while the second set yields the influence of anisotropic source. We consider the modified Krori-Barua charged isotropic solution for spherical self-gravitating star to deal with the isotropic system. The second set of the field equations is solved by taking two different constraints. We then investigate physical acceptability of the obtained solutions through graphical analysis of the effective physical variables and energy conditions. We also analyze the effects of charge on different parameters, (i.e., mass, compactness and redshift) for the resulting solutions. It is found that our both solutions are viable as well as stable for specific values of the decoupling parameter $\varphi$ and charge. We conclude that a self-gravitating star shows more stable behavior in this gravity.

In this paper, we consider isotropic solution and extend it to two different exact well-behaved spherical anisotropic solutions through minimal geometric deformation method in $f(R,T,R_{\rho\eta}T^{\rho\eta})$ gravity. We only deform the radial metric component that separates the field equations into two sets corresponding to their original sources. The first set corresponds to perfect matter distribution while the other set exhibits the effects of additional source, i.e., anisotropy. The isotropic system is resolved by assuming the metric potentials proposed by Krori-Barua while the second set needs one constraint to be solved. The physical acceptability and consistency of the obtained solutions are analyzed through graphical analysis of effective matter components and energy bounds. We also examine mass, surface redshift and compactness of the resulting solutions. For particular values of the decoupling parameter, our both solutions turn out to be viable and stable. We conclude that this curvature-matter coupling gravity provides more stable solutions corresponding to a self-gravitating geometry.

This paper is the first to present a novel, non-contact method that utilizes orthogonal frequency division multiplexing (OFDM) signals (of frequency 5.23 GHz, emitted by a software defined radio) to radio-expose the pulmonary patients in order to differentiate between five prevalent respiratory diseases, i.e., Asthma, Chronic obstructive pulmonary disease (COPD), Interstitial lung disease (ILD), Pneumonia (PN), and Tuberculosis (TB). The fact that each pulmonary disease leads to a distinct breathing pattern, and thus modulates the OFDM signal in a different way, motivates us to acquire OFDM-Breathe dataset, first of its kind. It consists of 13,920 seconds of raw RF data (at 64 distinct OFDM frequencies) that we have acquired from a total of 116 subjects in a hospital setting (25 healthy control subjects, and 91 pulmonary patients). Among the 91 patients, 25 have Asthma, 25 have COPD, 25 have TB, 5 have ILD, and 11 have PN. We implement a number of machine and deep learning models in order to do lung disease classification using OFDM-Breathe dataset. The vanilla convolutional neural network outperforms all the models with an accuracy of 97%, and stands out in terms of precision, recall, and F1-score. The ablation study reveals that it is sufficient to radio-observe the human chest on seven different microwave frequencies only, in order to make a reliable diagnosis (with 96% accuracy) of the underlying lung disease. This corresponds to a sensing overhead that is merely 10.93% of the allocated bandwidth. This points to the feasibility of 6G integrated sensing and communication (ISAC) systems of future where 89.07% of bandwidth still remains available for information exchange amidst on-demand health sensing. Through 6G ISAC, this work provides a tool for mass screening for respiratory diseases (e.g., COVID-19) at public places.

In this paper, conformal fractional order discretization [31, 35, 36] is used to analyze bifurcation analysis and stability of a predator-prey system. A continuous model has been discretized into a discrete one while preserving the fractional-order dynamics. This allows us to look more closely at the stability properties of the system and bifurcation phenomena, including period-doubling and Neimark-Sacker bifurcation. Through numerical and theoretical methods, this research investigated how the modification in system parameters affects the overall dynamics, which may have implications for ecological management and conservation strategies.

Cyberterrorism poses a formidable threat to digital infrastructures, with increasing reliance on encrypted, decentralized platforms that obscure threat actor activity. To address the challenge of analyzing such adversarial networks while preserving the privacy of distributed intelligence data, we propose a Privacy-Aware Federated Graph Neural Network (PA-FGNN) framework. PA-FGNN integrates graph attention networks, differential privacy, and homomorphic encryption into a robust federated learning pipeline tailored for cyberterrorism network analysis. Each client trains locally on sensitive graph data and exchanges encrypted, noise-perturbed model updates with a central aggregator, which performs secure aggregation and broadcasts global updates. We implement anomaly detection for flagging high-risk nodes and incorporate defenses against gradient poisoning. Experimental evaluations on simulated dark web and cyber-intelligence graphs demonstrate that PA-FGNN achieves over 91\% classification accuracy, maintains resilience under 20\% adversarial client behavior, and incurs less than 18\% communication overhead. Our results highlight that privacy-preserving GNNs can support large-scale cyber threat detection without compromising on utility, privacy, or robustness.

Knowledge-based or Artificial Intelligence techniques are used increasingly as alternatives to more classical techniques to model ENVIRONMENTAL SYSTEMS. Use of Artificial Intelligence (AI) in environmental modelling has increased with recognition of its potential. In this paper we examine the DIFFERENT TECHNIQUES of Artificial intelligence with profound examples of human perception, learning and reasoning to solve complex problems. However with the increase of complexity better methods are required. Keeping in view of the above some researchers introduced the idea of hybrid mechanism in which two or more methods can be combined which seems to be a positive effort for creating a more complex; advanced and intelligent system which has the capability to in- cooperate human decisions thus driving the landscape changes.