Billions of connected devices now stream sensitive data into cloud services every second, from heart-rate monitors in hospitals to payment terminals in smart cities. A new systematic literature review published in Cluster Computing by Boubakeur Annane, Sarra Cherbal, Abdelhak Zier, Lemia Louail and Adel Alti examines how artificial intelligence is being recruited to defend this vast, sprawling infrastructure. Covering research published between 2021 and 2025, the study synthesizes recent advances in AI-based security for the Internet of Things (IoT) and Mobile Cloud Computing (MCC), two paradigms whose convergence has transformed healthcare, finance and transportation while simultaneously multiplying the attack surface available to adversaries.

The core problem the review addresses is structural. Mobile cloud computing lets resource-constrained smartphones and IoT sensors offload computation and storage to remote data centers, but that offloading means sensitive data leaves the device and crosses networks the owner does not control. Every handoff, from a wearable sensor to a fog node to a central cloud, is an opportunity for interception, tampering or impersonation. Traditional defenses such as static encryption and perimeter firewalls were designed for stable, well-defined networks, not for ecosystems in which devices join and leave constantly, protocols vary wildly, and the computing continuum stretches from tiny battery-powered sensors to hyperscale data centers.

Artificial intelligence has emerged as the most promising countermeasure precisely because it is adaptive. Machine learning models can learn the normal rhythm of a network, the typical traffic patterns of a sensor, the usual authentication behavior of a user, and flag deviations that would escape rule-based systems. The review catalogs how this capability is being applied across the security stack: authentication schemes that verify identities using learned behavioral biometrics, intrusion detection systems that classify malicious traffic with deep neural networks, and anomaly detection engines that spot compromised devices before they can spread infection. Unlike earlier surveys that treated cloud and IoT security separately, this study explicitly covers AI-based solutions for both environments, arguing that the two can no longer be defended in isolation.

Intrusion detection receives perhaps the deepest treatment. The reviewed literature shows a clear evolution from classical machine learning classifiers toward deep learning architectures, including convolutional neural networks for traffic analysis, recurrent and transformer-based models for sequential attack patterns, and hybrid CNN-LSTM ensembles evaluated on heterogeneous datasets. Generative adversarial networks appear in a dual role: they can synthesize realistic attack samples to augment scarce training data, improving detector robustness, but they also represent a threat, since adversaries can use them to craft evasive malware. Federated learning adds another layer of sophistication, allowing multiple organizations or edge nodes to train shared detection models without pooling raw data, preserving privacy while improving collective threat intelligence.

Authentication and identity management form the second pillar. The review documents lightweight deep learning models designed to secure authentication in mobile cloud settings, blockchain-based schemes that create auditable, tamper-evident records of who accessed what, and zero-trust architectures that refuse to grant implicit trust to any device or user regardless of network location. Biometric frameworks hosted in the cloud, decentralized identity management for multi-domain computing continua, and attribute-based encryption for anonymous authentication all feature in the recent literature. The common thread is a shift away from single-point verification toward continuous, context-aware assessment of trust, an approach that AI is uniquely positioned to deliver because it can weigh dozens of behavioral and environmental signals in real time.

Data protection mechanisms constitute the third major category. The survey examines encryption advances tailored to constrained devices, including lightweight ciphers and fully homomorphic encryption schemes that allow computation on encrypted IoT data without ever decrypting it. Blockchain technology recurs here as well, used to guarantee data integrity and create trust between IoT devices, mobile applications and cloud backends. Smart contracts enable secure sharing of health data in mobile cloud-based e-health systems, while hybrid machine learning techniques optimize secure data transmission and routing. The authors note that these mechanisms must be evaluated against the specific security requirements they address, confidentiality, integrity, availability, authentication and privacy, since no single solution covers all of them simultaneously.

Yet the review is candid about the gaps. The first is lightweight deployment: most state-of-the-art AI security models are computationally heavy, demanding memory, energy and processing power that battery-operated IoT sensors simply do not possess. Studies measuring the energy consumption of on-device machine learning models for intrusion detection reveal a hard trade-off between detection accuracy and battery life. The second gap is cross-layer integration. Security solutions tend to be designed for a single layer of the stack, the network layer, the application layer, or a specific cloud service model, leaving seams between layers that attackers exploit. The third is contextual security: defenses that ignore the specific context of a deployment, a hospital ward versus a smart factory versus a connected vehicle, fail to adapt to radically different risk profiles and regulatory constraints.

The threat landscape itself is evolving faster than the defenses. The reviewed literature documents IoT botnet-driven distributed denial-of-service attacks, DNS flooding, man-in-the-middle attacks on multi-cloud applications, side-channel attacks that leak secrets from hardware, and black hole attacks that silently swallow network traffic. Adversarial machine learning adds a meta-threat: data poisoning and backdoor attacks can corrupt the very AI models deployed as guardians, turning the defense into a vulnerability. The authors highlight this as a critical research direction, since an intrusion detector trained on poisoned data may systematically ignore the attacker’s traffic while appearing to perform flawlessly on benchmarks.

Looking forward, the review outlines several directions the field must pursue. Generative AI is beginning to be applied to IoT security, from quantum-enhanced encryption algorithms for healthcare applications to attribute-based encryption schemes assisted by generative models. Artificial intelligence as a service is emerging as a delivery model, making sophisticated security analytics available to fog and edge deployments that could never run them locally. The authors call for security solutions that span the full computing continuum, from dew and edge devices through fog nodes to the cloud, with AI orchestrating protection across all layers and adapting to context automatically. They also emphasize the need for standardized performance evaluation metrics for cloud, fog and edge security, since the field currently lacks common benchmarks for comparing competing approaches.

What makes this survey significant is its timing and its breadth. The convergence of AI, IoT and mobile cloud computing is no longer speculative; it underpins smart hospitals, fintech platforms and intelligent transportation networks already in operation. By systematically mapping five years of research, classifying solutions by the security requirements they fulfill, and naming the unresolved problems, lightweight models, cross-layer defense, contextual awareness, and the security of the AI itself, the study provides both a snapshot of where the field stands and a roadmap for where it must go. As the authors make clear, the same adaptive intelligence that makes AI a powerful defender also makes it a target, and securing the guardian may prove to be the defining security challenge of the connected decade ahead.

Subject of Research: AI-based security solutions for Internet of Things and mobile cloud computing environments

Article Title: AI-enabled IoT mobile cloud security: trends and challenges

Article References: Annane, B., Cherbal, S., Zier, A., Louail, L., & Alti, A. (2026). AI-enabled IoT mobile cloud security: trends and challenges. Cluster Computing, 29(12), Article 704. https://doi.org/10.1007/s10586-026-06527-6

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06527-6

Keywords: artificial intelligence, Internet of Things, mobile cloud computing, cybersecurity, intrusion detection, authentication, federated learning, blockchain, encryption, systematic literature review, edge computing, zero trust