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.

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.

Cooperative Intelligent Transportation Systems (C-ITS) cannot work effectively if they do not have both efficient traffic management and solid security. We put forward in this paper an original framework that takes advantage of the Next Generation Simulation (NGSIM) dataset to improve traffic flow and system security by identifying False Data Injection Attacks (FDIA). By applying leading machine learning algorithms to authentic traffic data, we generate models that support improved vehicle coordination as well as provide assistance with security vulnerabilities in C-ITS systems. We are concentrating our method on the optimization of traffic dynamics by making intelligent decisions, while keeping the system secure from malicious cyber attacks. Analyses of the NGSIM data revealed that our proposed approaches produced important advancements in traffic flow efficiency and the accuracy of anomaly detection. Results prove that our framework minimizes congestion and concurrently enhances the reliability and security of collaborative vehicle systems. This investigation proposes a practical approach for fusing traffic optimization with cybersecurity, improving smart city evolution and the future of autonomous vehicles and vehicle connectivity.

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.

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.