Task-Resource mapping is the biggest challenge in cloud environments, especially when dealing with dynamic workloads and multi-objective constraints such as lowering cost, time, and maximizing utilization. A solution based on a metaheuristic-based multi-objective cloud scheduling (MMHEMOCS) is proposed in this paper. First, a novel integration of the crow search algorithm with electric fish optimization is proposed with dynamic parameters. This proposed dynamic CSA-EFO is further designed for multi-objective scheduling, and a new fitness function is also designed. This proposed framework is simulated across homogeneous and heterogeneous cloud environments. The experimentation is conducted on a set of resources with configuration settings as per the environments, with a set of different tasks. The performance metrics, such as energy consumption, resource utilization, cost, and execution time, are used to assess the proposed method. The comparisons with different optimization algorithms and their hybrid approaches are also performed. All the comparison results have shown the efficacy of MMHEMOCS for both homogeneous and heterogeneous environments. Also, the comparison of the proposed framework for homogeneous and heterogeneous environments has shown that the proposed algorithm handles both environments very well and performs better.

In the contemporary landscape of digital health infrastructure, the amalgamation of blockchain technology and quantum computing effectively addresses the dual dilemmas of data integrity and future resilience in security measures. Blockchain technology is based on the decentralized and tamper-proof ledger system that facilitates interoperable, auditable, and transparent mechanisms for the sharing of healthcare data. The healthcare sector is particularly susceptible to security breaches, owing to its dependence on sensitive, long-lasting patient records that are subjected to stringent regulatory frameworks such as HIPAA, HL7, and DISHA. The majority of blockchains with smart contracts are secured by traditional cryptographic algorithms like RSA, ECDSA, and SHA-256. However, these algorithms are vulnerable to Shor's and Grover's algorithms, which makes the urgent shift to post-quantum cryptography — such as lattice-based, hash-based, or multi vibration algorithms — critical for the security of both blockchain and smart contracts. The performance, storage, and transition times of quantum safe algorithms are impacted by their increased key sizes and signature lengths, particularly in the healthcare industry. Even if health data that has been encrypted is secure now, adversaries with quantum skills could harvest it and decode it later ("harvest now, decrypt later" attacks). Thus, EHR systems need to provide both long-term and forward secrecy. Hybrid encryption algorithms mix quantum-safe and conventional techniques. In order to provide safe, decentralized EHR management that is immune to quantum attacks, the proposed Patient-Centric Blockchain with Quantum Cryptography (PCBQC) system combines blockchain technology with the NIST-recommended post-quantum algorithms Dilithium, Kyber, and HIBE. Through hierarchical encryption and blind signatures, it guarantees patient-controlled access while preserving privacy and granular permission. A useful, consent-driven interface for encrypted data exchange is provided by the system's implementation, which makes use of Flask and ReactJS. The improved quantum resistance, scalability, and communication security of PCBQC are highlighted by comparing it with conventional cryptosystems.

The proliferation of dynamic network applications, from intelligent transportation systems to industrial IoT, has exposed the critical limitations of traditional static and reactively adaptive network topologies. These legacy systems, characterized by siloed optimization and a lack of cross-layer awareness, struggle to meet the stringent demands for ultra-low latency, high reliability, and efficient resource utilization. This paper proposes a novel framework for an Adaptive Network Topology based on Real-Time Multi-Spectral Analysis to address this fundamental challenge. Our solution introduces a hierarchical architecture integrating a Multi-Spectral Fusion and Prediction (MSFP) algorithm, which uses a hybrid Graph Attention Network and Gated Recurrent Unit (GAT-GRU) model to create a predictive state of the network. It also uses the Predictive Spectral-Topology Co-optimization (PSTC) algorithm, a Deep Reinforcement Learning (DRL) agent that prescriptively co-optimizes spectrum access and network routing. Evaluated through extensive simulations in NS-3 across urban vehicular (VANET), satellite, and industrial optical wireless sensor network (OWSN) scenarios, the proposed framework demonstrates statistically significant performance gains over state-of-the-art baselines. Results show an average improvement of 6.6% in Packet Delivery Ratio (PDR), a 27.5% reduction in end-to-end latency, a 14.7% increase in spectrum utilization efficiency, and a 28.5% reduction in control overhead. This research conclusively demonstrates that the synergistic, predictive co-optimization of the physical and network layers is a paradigm shift essential for building the autonomous, high-performance networks of the future.

Ransomware represents a critical and evolving threat to global cyber security, employing advanced dropout techniques that make traditional signature-based defenses ineffective. This article proposes a new structure for real-time behavioral analysis to respond to ransomware (R2BAR), which integrates machine learning with threat intelligence feeds to allow proactive detection and automated mitigation. The structure employs a set approach, combining a light gradient increase model (XGBoost) for an efficient initial screening and a short-term memory network (LSTM) for deep sequential analysis of API call patterns. It increases detection accuracy by dynamically correlating behavior with real-time threat intelligence. A critical innovation is the incorporation of an AI (XAI) component using Shape values, which generates transparent justifications for detection decisions, promoting confidence and allowing effective human supervision. Experimental evaluation shows that the structure reaches a 98.1% score and an area under the ROC curve of 0.998, maintaining a low of 2.35 seconds of response time (TTR), effectively interrupting encryption before significant data loss occurs. The results validate that the proposed solution addresses the main limitations of existing methods, balancing high accuracy, operational speed and interpretability, and providing a robust plan for next-generation autonomous ransomware defense systems.

Blockchain technology has revolutionized digital data management by providing a decentralized, unchanging and transparent structure for transaction recording. However, as blockchain systems are increasingly implemented in sensitive domains, such as finance, medical assistance, and supply chain management, the demand for advanced data security solutions becomes imperative. Traditional cryptographic algorithms such as RSA and EAS have been widely used to ensure digital communications, but each comes with limitations of scalability, computational efficiency and vulnerability to emerging threats. To face these challenges, this study proposes a new hybrid cryptographic structure that integrates RSA and AES algorithms to improve the safety and efficiency of blockchain-based data transactions. The proposed structure takes advantage of the capacity of RSA and AES for acute symmetric encryption to establish a safe and efficient encryption procedure in the blockchain environment. A wide architecture and algorithm model of the hybrid system is developed and implemented. Performance of this structure is evaluated using major standards such as encryption/decryption time, throughput, memory use and cryptonet resistance. Experimental results suggest that hybrid RSA-AES model, compared to standalone cryptographic methods, improves data privacy and transactions with minimal computational overhead compared to cryptographic methods. The integration of both algorithms significantly increases the speed of encryption, maintaining strong security protocols, making the system suitable for real-time blockchain applications. This research contributes to the growing field of secure blockchain structures, offering a scalable and practical solution that addresses the gaps in cryptographic efficiency and data protection. The results indicate that the proposed model can be an effective tool for strengthening blockchain transactions against sophisticated cyber threats. Future works will explore the integration of post-quantum cryptographic techniques and their adaptability to decentralized applications for long-term security sustainability.

Detecting both known and unknown cyberattacks remains a central challenge for intrusion detection systems (IDS). Signature-based IDS are effective for known threats but fail against novel attacks, while anomaly-based IDS can detect unknowns as outliers yet cannot recognize them if they reappear in new forms. Hybrid IDS improve coverage by combining both approaches, but most existing designs still treat unknown attacks as one-time anomalies without capturing their behavioral patterns. This limitation is critical in adaptive threat landscapes where attackers continuously refine their methods to mimic legitimate traffic, making stealthy intrusions especially difficult to detect. In this paper, we propose a Behavior-Based Hybrid IDS (Behavior-HIDS) that integrates (i) a GA-optimized Support Vector Machine (SVM) for supervised detection of known attacks, with (ii) a two-stage anomaly detection pipeline (Isolation Forest and Extended Isolation Forest) for identifying unseen threats. Crucially, our system goes beyond detection by clustering unknown attacks into coherent behavioral groups using autoencoder embeddings and HDBSCAN. These clusters are incorporated into retraining, thereby creating a form of behavioral memory that enables the IDS to recognize future variants of previously unseen attacks. Comprehensive experiments on NSL-KDD, CICIDS2017, and UNSW-NB15 confirm that Behavior-HIDS consistently outperforms classical hybrid IDS approaches. On NSL-KDD, it achieves 92.49% accuracy and 92.30% F1-score, with similar improvements observed on the other datasets. By combining anomaly detection with behavioral learning, our framework advances IDS design toward adaptive and evolving defense mechanisms.