Online Social Networks (OSNs) have become very important communication and digital identity platforms in the last few years. But compromised user accounts have become an increasing menace that endangers privacy, data integrity, and network trust. Existing compromised account detection methods primarily rely on static profile attributes, graph-based relationships, or content analysis, which often fail to capture subtle behavioral deviations exhibited by compromised legitimate accounts. This limitation motivates the need for a behavior-centric detection framework capable of identifying anomalous user activities using dynamic behavioral and security-related features. In this paper, a machine learning-based behavioral analysis framework is presented to promptly recognize compromised accounts. The approach does not consider contents or graph structures but rather looks for inconsistency in behaviors such as irregular posting patterns, abrupt changes in activity, and a change of devices or credentials. The proposed framework integrates behavior-driven feature engineering, class imbalance handling using SMOTE-based resampling, and comparative evaluation of multiple interpretable supervised machine learning classifiers to detect compromised accounts based on behavioral anomalies rather than content or network topology. A number of features were gathered from users’ interactions, profile activities, and session behavior, then the dataset was processed and multiple traditional supervised machine learning classifiers were evaluated and compared to identify the most effective model for compromised account detection. The framework was evaluated using a behavior-oriented benchmark dataset curated from publicly available social network repositories (SNAP, UCI Machine Learning Repository, and NetworkRepository), representing realistic Facebook-like user behavioral patterns. Due to the imbalance in the distribution of classes, resampling was carried out to solve the problem. Among the evaluated models, the best-performing classifier achieved an accuracy of 88.0
Quantum teleportation and superdense coding are two different protocols that were developed to transmit a limited number of qubits and two classical bits, respectively. Therefore, they are inefficient for transmitting messages containing a massive number of individual qubits or classical bits. Thus, it is necessary to scale up these two protocols in larger systems to enable them to transmit massive messages. To achieve this goal, it is essential to integrate each of these two protocols within architectures that contain a quantum main memory that functions as a buffer capable of storing both quantum and classical data and managing a massive amount of data efficiently during transmission operations. Q1RAM is a cutting-edge quantum main memory and the fastest quantum main memory available, as it has a time complexity of O(1) for accessing memory locations. In this paper, two architectural schematics are proposed based on integrating Q1RAM as a core component with the quantum teleportation and superdense coding protocols. The first architectural scheme is developed to scale up the quantum teleportation protocol, making it efficient for transmitting massive messages of separable individual qubits. The second architectural schema is proposed, consisting of two Q1RAM chips, two quantum processing units, two of the classical RAMs, and a quantum gateway. This proposed schema is developed to scale up the superdense coding protocol so that it can transmit massive classical messages containing large amounts of data. The experimental simulation results validate the architectural schematics for scaling up the quantum teleportation and the superdense coding protocols for large-scale quantum communications.
We study a recently published dataset of 396 real-world cloud architectures on AWS from a wide range of industries. We focus on those architectures that contain HPC, edge or serverless components and investigate how they are built, which services they use, the types of storage used, quantify architectural complexity, assess the use of machine learning services, and distill insights regarding how cloud applications in these three domains are being built in the cloud continuum. Our data and analysis can be valuable for researchers and practitioners seeking to build and optimize cloud architectures and to help guide new research.
Routing in Internet of Things (IoT) networks becomes increasingly challenging when non-cooperative nodes participate in packet forwarding, as they reduce delivery reliability, increase communication delay, and consume network resources inefficiently. Existing trust-based and game-theoretic routing approaches generally rely on static trust evaluation or fixed hierarchical structures, limiting their ability to adapt to dynamic network conditions. To address these limitations, this paper proposes a dynamic game-theoretic trust-tree framework for trust-aware routing in IoT networks. The proposed framework constructs and continuously updates a hierarchical trust tree through a two-player bargaining game, where routing decisions are made using a trust-aware utility function that jointly considers packet forwarding reliability, residual energy, communication cost, and node trustworthiness. Unlike conventional hierarchical trust models, the proposed framework dynamically reorganizes the trust hierarchy according to node behavior, enabling adaptive parent selection and more reliable packet forwarding. The routing framework is mathematically formulated and evaluated using The ONE simulator under different network scenarios. Experimental results show that the proposed method improves the packet delivery ratio by approximately 13–16
The rapid expansion of Big Data has transformed data-driven decision-making across diverse sectors, including healthcare, cybersecurity, smart cities, and the Internet of Things (IoT). However, traditional data management platforms struggle to address the increasing volume, velocity, variety, and veracity of modern data ecosystems, resulting in scalability constraints and inefficiencies. These challenges necessitate distributed, cloud-native, and energy-efficient architectures capable of supporting Artificial Intelligence (AI) and Machine Learning (ML)–driven analytics. This paper presents a systematic literature review (SLR) of emerging Big Data analytics frameworks centered on the Hadoop ecosystem and its modern extensions, including Apache Spark, Flink, Kafka, and data lakehouse technologies. Following the PRISMA 2020 guidelines, the review analyzes 138 primary studies published between 2015 and 2025, with 60 studies selected through rigorous inclusion and quality assessment criteria. The analysis evaluates architectural evolution from batch-oriented MapReduce systems to unified batch-stream processing and cloud-native data platforms, emphasizing performance optimization, resource efficiency, and sustainable computing practices. The findings reveal significant progress in integrating AI and ML pipelines with Hadoop-based infrastructures for applications such as real-time fraud detection, anomaly detection, predictive healthcare analytics, renewable energy forecasting, and intelligent urban management. Particular attention is given to explainable AI (XAI), federated data processing, and green computing strategies that enhance transparency, trust, and environmental sustainability. This review proposes a structured taxonomy of Hadoop-based Big Data systems, categorizes emerging research trends, benchmarks modern frameworks, and identifies open research challenges. The study provides theoretical, practical, and policy-level implications to guide researchers, system architects, and decision-makers in selecting scalable, low-latency, and energy-aware Big Data solutions aligned with next-generation digital transformation and sustainable development objectives. Not applicable, as this study is not a clinical trial.
