In the realm of addressing the primary unfeasible issue in the cybersecurity in smart grids to ensures protection of critical infrastructure, a novel “Anomaly-Graph Fusion Isolation Framework (AG-FIF)” was introduced. In the existing, unauthorized access to smart meter data due to weak encryption and poor authentication in smart grid systems, while scalability challenges hinder real-time processing. Hence a novel, Integrated Anomaly Detection Ensemble (IADE) was introduced in this approach, offers strong defence against manipulation of scalability problems in smart grid settings with Support Vector Machine (SVM) for classification purpose, Random Forest for integration purpose and Autoencoders for reconstruction purpose. Moreover, the smart grid information networks are being swamp with DDoS attacks due to bad management of resources, while standard methods have a difficulty in detection and relief in dynamic distributed environment. Thus, a novel Graph-Enhanced Isolation Tree Framework (GEITF) was invented, which controlling network resources and lessening the effects of dispersed and dynamic DDoS attacks with Graph Convolutional Networks (GCNs) for modelling purpose, Isolation Forest for detection purpose and Decision trees for classification purpose. The AG-FIF framework delivers 98.55% accuracy, 97.35% F1-Score, low FNR (1.01%), FPR (1.03%), and excels in resource management, robustness, and scalability.

In the context of Internet of Things (IoT) intelligent systems, the latest research regarding delegation using an access control model has gained attention, reflecting the need for models to support more functionalities in relation to hierarchical delegation. With respect to delegation procedures within access control, issues arise after delegation concerning the permissions to others with respect to revocation. Redundancy and conflict arising from delegation can occur depending on the delegation policies used within the hierarchical structure. This article discusses implementation of positive delegation represented by “YES” and negative delegation represented by “NO”. Furthermore, we also consider permission to delegate positively and negatively represented by (YES and NO). These challenges are addressed by creating additional features in a hierarchical policy model (HPol). The implementation was created using Python (ver. 3.10) code to verify the advantages of the approach, through experimentation under different scenarios. The model also has the capability to manage and adapt features of the Internet of Things (IoT) to a blockchain architecture, enhancing security and verification during the delegation process and increasing the scalability of Internet of Things (IoT) intelligent environment systems.

This study suggests a hybrid optimization model and a new deep learning technique to create an effective deepfake detection system. During the preprocessing stage, deepfake database images are improved by employing Gaussian Filter and Histogram Equalization and are ready for analysis. The recently proposed Improved Local Ternary Patterns (I-LTP) approach collects textural and temporal information for feature extraction. The advanced GeoFisherNet, which effectively integrates spatial and temporal properties, is then utilized to fuse these data. The Marine Predator Customized White Shark Optimizer (MCWO), a hybrid approach that combines the White Shark Optimization Algorithm (WSO) and Marine Predator Algorithm (MPA), is used to find the most discriminative features during the feature selection phase. Lastly, ConvoReinAutoNet (CRAN), a revolutionary deep learning architecture that combines Convolutional Neural Networks (CNN), Deep Reinforcement Learning (DRL), and Autoencoders, is applied to the fused and optimized features in the classification phase to make precise detection decisions. The Python implementation of the suggested system shows better detection efficiency and accuracy of two data splits (70% and 80%) are 98.78% and 99.42% than the current methods.

Steganography conceals ”secrets” within an convenient and expedient multimedia carrier. The carrier could be text (i.e., not plain text), images, audio andor video files (i.e., carrier channels). The fact that concealed information is contained in the otherwise ordinary and mundane carrier file is known only by the sender-receiver pair. Only they share the existence of the secret. Images are the most popular (i.e., multimedia) carriers because of their inherent property that enables better obfuscation. Content adaptive image steganography is a new trend in the field for messaging secrets inside unsuspected image file transfers. As the name suggests, the embedding locations are altered adaptively depending on the image content that optimizes the decision of choosing a location inside the carrier so that an embedding is not discernible (i.e., additive distortion is minimized). Herein, we critique the various approaches used for content-adaptive image steganography which can be broadly categorized as CNNbased, GAN-based, along with minimizing additive distortion function-based. We provide a brief historical account toward better anticipating the future research opportunities in terms of properties, and evaluation metrics. A summary table of these past and future directions is provided. Moreover, we highlight trends along with their concomitant advantages and disadvantages toward identifying opportunity gaps.

This research introduces a pioneering framework for securing communication networks for 6G communication network, through the utilization of a Dual Key Extraction (DKE)-based Non-Orthogonal Multiple Access (NOMA) technique. With the advent of the 6G networks that are anticipated to provide ultra-fast data transfer, connect numerous devices, and have minimum latency, there are formidable security challenges such as increased espionage activities and hacking. The core of this approach involves the generation of two keys, where the first key is intricately linked to channel randomness, and the second key is derived based on distance considerations. Among the generated keys, the optimal keys utilized for data encryption is selected dynamically based on the new Ant Fly Optimizer (AFO) approach. In addition, the encryption is performed using the new two- tier security model that encapsulates the Two-fish encryption (TFE) and the new Elliptic Curve Diffie-Hellman (ECDH). In addition, the error correction within the encrypted data is accomplished via Galois approach. In addition during the transmission stage, the integration of non-binary Raptor codes for handling packet loss and errors in the data channels, while the Galois Field-Based Error Correction is applied for ensuring mathematical consistency and error resilience in cryptographic encoding.. These elements collectively contribute to fortifying the security of communication channels against potential eavesdropping threats. The proposed system establishes a secure communication channel between the Base Station (BS) and end-users, minimizing the risk of unauthorized access. To address potential errors, such as bit flipping, a critical bit reconciliation technique, namely Raptor codes, is integrated dense 6G environments. The implementation of secured 6G model is performed using the MATLAB platform. The proposed model achieved the key mismatch rate of 0.0001% for SNR value of 30dB, which is lower than the existing techniques.

At present, Content-Based Medical Image Retrieval Systems (CBMIRS) are a novel and potentially useful technology though they lack clinical validation. The study aims to assess how CBMIRS helps in interpretation of chest X-ray (CXR) images in patients who have lung disease. This paper proposes Lung-CBMIR, a new hybrid model that aims to enhance retrieval precision and computational complexity for lung disease X-ray images. The new system combines Mask AttnR-CNNpro, an improved segmentation model that uses attention mechanisms to precisely segment lung areas. Feature extraction is done through Local Binary Patterns (LBP) for texture features, shape descriptors for geometric pattern, and DenseNet+, which utilizes three dense blocks and strategic pooling methods to achieve deep feature extraction. The Bobcat-Fish Hybrid Optimizer (BFHO) method proposed in this paper integrates Bobcat Optimization exploration ability with the exploitation capability of Catch Fish Optimization for optimal selection of features. There is also a novel hybrid distance metric, combining Mahalanobis and Cosine distances, that improves image similarity measurement. Furthermore, rank the images based on their relevance to the query and compile them into a feature vector. Lastly, the DeepCL-Net classifier, which is a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, facilitates effective classification of lung illnesses like pneumonia, infiltrates, and lung nodules. The proposed Lung-CBMIR system is found to attain an accuracy of 98.75%, F1-score of 98.13%, and MCC of 0.9801, better than state-of-the-art models like CNN-AE of 95.58% and VGG-19 of 96.81%. The results confirm that the proposed system greatly improves retrieval accuracy, lowers computational complexity, and yields a strong tool for lung disease diagnosis in CBMIR tasks. The abbreviation and their concern description are manifested in Table 1.