Ransomware, a significant cybersecurity threat, encrypts files and causes substantial damage, making early detection crucial yet challenging. This paper introduces a novel multi-phase framework for early ransomware detection, designed to enhance accuracy and minimize false positives. The framework addresses the limitations of existing methods by integrating operational data with situational and threat intelligence, enabling it to dynamically adapt to the evolving ransomware landscape. Key innovations include (1) data augmentation using a Bi-Gradual Minimax Generative Adversarial Network (BGM-GAN) to generate synthetic ransomware attack patterns, addressing data insufficiency; (2) Incremental Mutual Information Selection (IMIS) for dynamically selecting relevant features, adapting to evolving ransomware behaviors and reducing computational overhead; and (3) a Deep Belief Network (DBN) detection architecture, trained on the augmented data and optimized with Uncertainty-Aware Dynamic Early Stopping (UA-DES) to prevent overfitting. The model demonstrates a 4% improvement in detection accuracy (from 90% to 94%) through synthetic data generation and reduces false positives from 15.4% to 14%. The IMIS technique further increases accuracy to 96% while reducing false positives. The UA-DES optimization boosts accuracy to 98.6% and lowers false positives to 10%. Overall, this framework effectively addresses the challenges posed by evolving ransomware, significantly enhancing detection accuracy and reliability.

Multi-tenancy is essential for scalable IoT–Cloud systems; however, it introduces complex security vulnerabilities at the intersection of shared cloud infrastructures and resource-constrained IoT environments. This systematic review evaluates next-generation security frameworks designed to enforce tenant isolation without violating the strict latency (<10 ms) and energy bounds of lightweight sensors. Adhering to PRISMA guidelines, we analyze selected high-quality studies to categorize intersectional threats, including cross-tenant data leakage, side-channel attacks, and privilege escalation. Our analysis identifies a critical, unresolved conflict: existing mitigation strategies often incur a 12% computational and communication overhead, creating a significant barrier for real-time applications. Furthermore, we critically analyze emerging technologies, including Zero Trust Architectures (ZTA), adaptive Artificial Intelligence (AI), blockchain, and Post-Quantum Cryptography (PQC). We find that direct PQC deployment is currently infeasible for LPWAN protocols due to key-size constraints (1.6 KB) that exceed typical payload limits. To address these challenges, we propose a novel multi-layer security design principle that offloads heavy isolation and cryptographic workloads to hardware-accelerated edge gateways, thereby maintaining tenant isolation without compromising real-time performance. Finally, this review serves as a roadmap for future research, highlighting federated learning and hardware enclaves as essential pathways for securing next-generation multi-tenant IoT ecosystems.

The rapid convergence of the Internet of Things (IoT) and cloud computing has intensified reliance on multi-tenancy, a model that enables resource sharing to enhance scalability and reduce costs. However, this shared infrastructure introduces significant security vulnerabilities, particularly at the intersection of IoT's resource-constrained devices and the cloud shared environment. While existing literature has addressed IoT or cloud security separately, a significant research gap exists in analyzing the specific risks of multi-tenancy in these integrated systems. This review synthesizes recent research on mitigation techniques to address security and privacy challenges in multi-tenant IoT-cloud environments. We provide a comprehensive classification of threats, including inter-tenant data leakage, side-channel vulnerabilities, and privilege escalation. Our analysis reveals a persistent security-performance trade-off that limits the widespread adoption of robust defenses in resource-constrained IoT environments. Current mitigation techniques, including access control models and AI-driven detection systems, incur significant computational overhead. This makes them impractical for numerous IoT applications with constrained processing and energy resources. This review analyzes the limitations of existing approaches and identifies key architectural gaps. In this paper, we present a roadmap of emerging solutions to resolve this security-performance trade-off. This work emphasizes the integration of Zero Trust Architectures (ZTA) for continuous verification, adaptive AI for real-time threat detection, blockchain for immutable audit trails, and the adoption of Post-Quantum Cryptography (PQC) as essential strategies to secure the next generation of mul-ti-tenant IoT-cloud infrastructures.