Traditional optimization methods are ineffective in contexts where interference patterns, device density, and mobility dynamics change rapidly for future 6G networks. This work introduces a federated orchestration system that combines big language models into the control loop for semantic interpretation and causally based decision-making. The system generates constraint-aware policy drawings, structures semantic graphs from raw telemetry, and assesses candidate topology alterations using a reasoning-augmented optimization engine. Before deployment, a probabilistic verification module guarantees that each intervention fulfills tight performance requirements, and a continuous counterfactual evaluation process turns successful decisions into lightweight edge controllers. Simulations of dense urban, event-driven, and emergency mobility situations show reduced signaling overhead, delay, and instability and good service-level compliance. The results show that semantic reasoning and federated optimization can create interpretable, adaptive, and trustworthy self-organizing 6G infrastructures in process. Simulations suggest 40–55% telemetry compression, 18–30% latency reduction in congested regimes, and verifiable SLA compliance above 0.99 probability. Beyond metrics, the architecture offers interpretability, federated privacy preservation, and long-horizon adaptability sets. The result is a pathway toward 6G infrastructures that are not only faster, but context-aware, cautious, and continuously improving in process.

Zero-day malware still slips past the best detection systems because most models need thousands of labeled examples before they learn anything useful. That dependency is exactly the weak point: by the time enough samples accumulate, the damage is already spreading. Traditional few-shot approaches promise quicker adaptation, yet they often reduce rich forensic evidence into flat feature vectors and end up overfitting to byte-level quirks rather than behavioral signals. This work takes a different path and develops a systematic pipeline that treats malware traces as structured evidence, feeding them through a sequence of five meta-learning extensions designed to survive the scarcity of zero-day samples. We begin with EpiForge, which fabricates realistic few-shot episodes from evidence-graphs and injects controlled novelties without breaking causal consistency, ensuring the training tasks resemble true zero-day strangeness. These episodes drive BayesMAML-E, a hierarchical Bayesian meta-learner that encodes evidence-type priors, producing task-conditioned initializations and calibrated uncertainty estimates. The output then flows into CoShaRE, which sparsifies decisions by learning counterfactual Shapley-regularized masks retaining only causally sufficient evidence and generating counter-examples that test decision stability. From there, OptiQuill decides how to spend scarce resources, balancing sandbox runs and labeling efforts using a budget-aware Lagrangian bandit that targets maximum downstream meta-learning gains. Finally, CausalFADE distills the learned behavior into compact automata and executable rules, turning black-box predictions into forensic signatures that analysts can trust and re-use for the process. Across all five stages, we see evidence of measurable impact: 5-shot accuracy improves by more than 10% points over standard MAML, calibration error falls to near 2%, and label and compute budgets are cut substantially. The result is not just faster adaptation but also auditable, causally grounded signatures that close the loop between evidence collection, learning, and deployment. This work appears to offer a path toward zero-day detection that is both technically feasible and operationally sustainable in process.

This study aims to evaluate the clinical efficacy of machine learning and deep learning models, particularly EfficientNetB7, for the early diagnosis of Alzheimer's disease using neuroimaging and clinical data. Machine learning and deep learning approaches to evaluate complex data, including neuroimaging and clinical information, aiding in the timely detection of Alzheimer's disease and delivering precise diagnoses. Machine learning algorithms, through comprehensive analysis of diverse data sources, have the capacity to discern intricate patterns and features that may elude human observation. Through training on extensive datasets, these models achieve heightened accuracy and improved generalization in diagnosing Alzheimer's disease. Deep learning, a subset of machine learning characterized by neural networks with numerous layers, enhances the capacity to decipher and extract nuanced patterns from extensive and intricate datasets. The development of reliable machine learning and deep learning models holds significant potential for enhancing early detection, a pivotal factor for timely interventions and appropriate treatment plans. Early diagnosis can lead to more effective disease management, maybe improving the standard of life for those suffering from Alzheimer's disease and reducing the strain on the healthcare system and carers. In a performance evaluation, the EfficientNetB7 model demonstrated exceptional accuracy, achieving a score of 99.36%. This underscores its superior performance in comparison to other models. Furthermore, it displayed a validation accuracy of 49.48%, showcasing its robustness and capability. The model maintained a validation loss of 2.9, while the training loss was impressively low at 0.017. When benchmarked against other machine learning models, EfficientNetB7 consistently outperforms, underscoring its potential for elevating diagnostic accuracy in Alzheimer's disease detection.

In the modern era, space infrastructure acts as a vital node in numerous sectors impacting generations making it a critical infrastructure. Thus it proves to be a crucial and effortless target for any nation during cyber warfare. The role of Cybersecurity and Infrastructure Security Agency (CISA) in identifying the critical infrastructures so as to check the necessary requirements and rectify the weaker points. This review study focuses mainly on the study of variant categories including critical infrastructures across the globe. The essence of this study points towards the shortcomings of the space infrastructure including the ground, space and link module cyber threats. The study also points out Common Vulnerability Exposures (CVEs) to discover a potential threat and assess the magnitude of risk. This study, therefore, presents a meticulously drafted methodology that identifies concerns, module threats, and case studies of cyber threats and suggests recommendations for space infrastructure protection from different space agencies across the globe for their better management. Thereafter, the review propounds generic solutions to mitigate cyber threats to space infrastructure. The study tries to bring the focus on the area of space infrastructure, that, according to this review study, should also be considered as a focal area under the broad umbrella of categories of critical infrastructures.

Malware forensics is a field in digital forensics that is dedicated to collecting evidence of a malware attack on a system. Malware is also executable code, but unlike regular executables, it is designed to perform unauthorized actions on victim computers. Being an executable, the malware also performs designated tasks through several processes that can leave its traces (digital footprints) throughout different components of the operating system, such as registries, file systems, DLL calls, API calls, memory usage, CPU utilization, network traffic, etc. Some of these traces can exist even after the removal of the malware and can be used as evidence to verify the occurrence of an attack. The proposed malware forensic analysis technique combines both the static and dynamic features of the malware to trace its evidence. This information is collected using the Cuckoo sandbox, which executes the malware sample files in a virtual environment to generate a JSON-formatted analysis report. The Python script is used to extract the non-volatile features from the JSON format analysis report. However, combining both features results in a very long and sparse feature vector, which negatively affects the classifier’s performance. Therefore, the principal component analysis (PCA) algorithm is adopted to reduce the dimensionality of the feature vectors. The generated reduced dimensionality feature vectors are used to train the extreme learning machine (ELM). The ELM can be trained in a fraction of the time compared to the support vector machine (SVM), while achieving similar accuracy. This makes it a favorable choice, especially for malware forensic analysis, where the classifier needs to train with a large amount of data and requires quick decision-making capabilities. The performance of the proposed techniques is evaluated using 90 malicious code samples containing 41 Trojans, 28 worms, and 21 bots. To demonstrate the superiority of the proposed algorithm, a comparison with some state-of-the-art classification methods, named Hidden Markov Model (HMM), Support Vector Machine (SVM), Artificial Neural Network (ANN), and ELM, was also performed. The comparison results show that the proposed technique achieves approximately 11% higher precision, 10% higher recall, and a 10.5% higher F-score than the competitors.

Social media platforms have profoundly transformed how humans interact, offering new avenues for communication and self-expression. Yet, this digital revolution has also led to the emergence of social media crimes, presenting unique challenges. This research comprehensively explores these crimes, delving into their various types, evolving trends, and wide-ranging impact on individuals, communities, and civil rights. Through a multifaceted research approach, this study meticulously investigates social media crimes, drawing from extensive literature reviews, real-world cases, legal documents, and expert insights. The research also examines the roles played by social media platforms themselves, shedding light on their policies, data-sharing practices, and enforcement methods. Both qualitative and quantitative methodologies are employed to categorize and trace the evolution of social media crimes, addressing issues such as cyberbullying, identity theft, and the dissemination of extremist content. The findings underscore the profound implications of social media crimes on individuals and society, with marginalized communities and younger generations bearing the brunt of these consequences. The paper emphasizes the need for clear guidelines governing the use of social media in intelligence gathering, particularly in cases related to community organizing and public protests. In conclusion, this research highlights the pressing necessity for legislative and technological measures to combat social media crimes and protect civil rights. It advocates for educational programs to equip law enforcement, legal professionals, and individuals with the knowledge to navigate the digital landscape responsibly while preserving fundamental rights in the digital age.

Chatbots and intelligent virtual assistants are two AI-powered technologies that have gained increasing attention in recent years due to their potential to enhance the user experience and improve business efficiency. While both technologies aim to provide personalized and context-aware assistance to users, they differ in terms of their design, features, and capabilities. This research paper presents a comparative analysis of chatbots and intelligent virtual assistants, examining their effectiveness in different contexts and applications. We conducted a systematic literature review of studies that compare chatbots and intelligent virtual assistants and identified the key differences between the two technologies. Our findings show that chatbots are typically rule-based and use predefined responses, whereas intelligent virtual assistants use machine learning and natural language processing to provide more personalized and context-aware responses. We also found that the success of both technologies depends on their ability to provide a seamless and natural conversational experience for users, and that the design and implementation of chatbots and intelligent virtual assistants play a crucial role in their effectiveness. Overall, this research paper provides a comprehensive understanding of the strengths and limitations of chatbots and intelligent virtual assistants and offers insights into their effective use in different industries and applications.

In the evolving cyber threat landscape, one of the most visible and pernicious challenges is malware activity detection and analysis. Traditional detection and analysis methods face threats of data high-dimensionality, lack of strength against adversarial attacks, and non-efficient use of unlabeled data samples. In this context, we propose a comprehensive framework that applies machine learning methods to enhance evidence collection and malware activity analysis. The approach of our proposed model innovatively uses several advanced machine learning methods. First, in order to reduce the dimensionality of raw malware activity data by 50%, while at the same timestamp preserving critical information, as evidenced by minimal reconstruction error, we apply an autoencoder-based feature learning technique. This technique assists in the extraction of compact, informative, and feature representations covering both global and local discriminative patterns for accurate malware detection. With the addition of Gradient Boosted Decision Trees (GBDT) to features derived from Convolutional Neural Networks (CNN), we further improve the capability of the model. The hybrid model combines the outlier robustness and heterogeneous data handling capability of GBDTs with the hierarchical feature extraction capability of CNNs, resulting in a significant improvement in performance, with an F1-score of 0.95 on a validation set. In order to defend from evasion attacks, we incorporate adversarial training using Generative Adversarial Networks (GANs). It enables effective counteraction against adversarial strategies, reducing adversarial success rates by 60%. The model is trained using adversarial examples, and its parameters are optimized to minimize classification loss across both the normal and distorted inputs, thereby enhancing robustness. Expanding the applicability of the framework, we use semi-supervised self-training using Variational Autoencoders (VAEs) to use both labeled and unlabeled datasets & samples. This approach not only improves anomaly detection by 30% but also allows the model to learn probabilistic latent representations, thereby revealing underlying data structures. Finally, we address the challenge of temporal malware activity analysis through Long Short-Term Memory (LSTM) networks augmented with an attention mechanism. This configuration allows the model to be able to detect and adapt to evolving attack patterns, thus, by 25%, significantly improving the zero-day attack detection.

Quantum Computing is not only one of the fastest-growing technologies in today's technophilic world but also a powerful and probable substitute for the classic computers in the future. The efficient harnessing of the principles of quantum theory makes quantum computers the upcoming reality of the computing world, with its effectiveness in being superfast in solving real-world complex problems adding points in its favor. However, for every bright side of a thing there exists a darker side too, and this is the case with quantum computers as well.

The detection and localization of malwares using spatial and temporal data patterns require the development of efficient deep learning models. These models employ various techniques such as feature extraction, feature selection, data classification, and post-processing to achieve their objectives. While numerous high-efficiency malware analysis models have been presented, most of them are designed for application-specific purposes, which limits their scalability to multiple domains. Additionally, only a few of these models have been designed to identify malware locations. To address these issues and improve malware detection scalability and localization performance, this article proposes a novel augmented convolutional model (ACM) for intelligent cross-domain malware analysis using forensic neural networks (FNNs). The model is evaluated on multiple malware datasets, including Electro RAT, Pegasus, SkyGoFree, Viking Horde, Bat Skull, Yesmile, Wirenet, Jigsaw, Satana, and Tapaoux. The proposed model achieved an average accuracy of 98.5% in classifying these malwares, making it useful for real-time malware analysis. The model also achieved an average localization accuracy of 79.6% across these datasets, assisting forensic experts in obtaining an approximate estimate of malware locations in input data streams. The proposed ACMFNN method demonstrated better performance compared to recently proposed malware detection models, with 8% better precision, 6.5% better recall, and 9.4% better classification accuracy. Furthermore, the proposed approach had 15% better localization accuracy, 19% better localization precision, and 14% better localization recall, due to the augmented convolutional model. These results indicate that the proposed model is applicable for a wide variety of malware detection and localization application deployments.