A Mini Review on IoT-Cloud Security Threats and Quantum-Resilient Countermeasures in Smart Healthcare

The integration of Internet of Things (IoT) devices with cloud platforms is transforming patient care through real-time monitoring and advanced data analytics. However, this connectivity introduces critical security vulnerabilities that can have life-threatening consequences. This mini review summarizes emerging security vulnerabilities in IoT-cloud healthcare environments, data breaches, replay attacks and adversarial AI manipulation. It evaluates current countermeasures, including lightweight cryptography, API security and intrusion detection. While current mitigation strategies provide a foundational level of security, the healthcare sector must adopt a more resilient posture urgently. This includes embracing future needs like Post-Quantum Cryptography (PQC) for long-term data protection and implementing Zero-Trust architectures to ensure patient safety in an increasingly connected world.

Keywords:IoT and cloud security; Smart healthcare; Post-quantum cryptography; Information security

The modern healthcare ecosystem increasingly relies on a network of IoT-enabled medical devices to monitor and manage patient health. These systems utilize wearable sensors, Remote Patient Monitoring (RPM) devices and cloud-based Electronic Health Records (EHRs) to provide real-time diagnostics and inform treatment recommendations [1]. While this integration enhances clinical efficiency and real-time care, it also broadens the attack surface. The dual challenge of resource-constrained IoT hardware and complex cloud infrastructures leaves patient information and device integrity at significant risk. Recent breaches and manipulated diagnostic models demonstrate how cyberattacks can shift from digital harm to physical consequences [1].

The deployment of IoT-cloud systems in healthcare gives rise to several critical risks, each with the potential to cause significant harm.

Unauthorized access and data breaches

Insecure APIs or weak authentication mechanisms create pathways for unauthorized access to EHRs, exposing sensitive patient information [1]. A comprehensive analysis of healthcare data breaches revealed that hacking and IT-related incidents were the most common causes [2]. The impact of such a breach includes the severe violation of patient privacy, the potential for identity theft and a breakdown of trust between patients and healthcare providers.

Replay attacks on medical IoT devices

Medical IoT devices like smart insulin pumps and pacemakers depend on wireless communication for remote monitoring and configuration [1]. This exposes them to replay attacks, where an adversary intercepts legitimate signals and retransmits them to manipulate the device into performing an unauthorized action. For instance, an attacker could resend a command to an infusion pump to give an unprescribed dose of medication, potentially leading to deadly consequences [3]. This effectively turns a life-saving device into a potential weapon.

Adversarial AI and diagnostic manipulation

AI models used in diagnostic imaging or physiological analysis are vulnerable to adversarial inputs, where attackers slightly alter sensor data or medical images to mislead classification systems. For instance, maliciously perturbed ECG data or blood glucose levels can lead to incorrect cardiac diagnoses [1]. Research has demonstrated that adversarial attacks on medical image classification systems can lead to significant misdiagnoses [4].

To counter these threats, several security solutions have been implemented in smart healthcare, though they often represent foundational steps rather than comprehensive defences.

Lightweight cryptography

To address the resource constraints of medical devices, lightweight encryption algorithms like Elliptic Curve Cryptography (ECC) and PRESENT are used to ensure data confidentiality while minimizing power consumption [1]. These algorithms ensure confidentiality and integrity with minimal computational overhead, although they remain vulnerable to emerging quantum decryption capabilities. Consequently, there is a shift toward quantum-resilient alternatives. For instance, Al-Mekhlaf et al. [5] propose replacing these traditional schemes with a five-phase IoMT framework based on lightweight lattice cryptography. That offers robust protection against quantum adversaries without overwhelming low-resource devices [5].

Secure APIs and cloud interfaces

To prevent unauthorized data access, healthcare APIs are increasingly secured using OAuth 2.0 token-based authentication, API gateways and rate limiting [1]. Secure API design principles, input validation, HTTPS enforcement and privilege minimization remain essential to mitigating injection and DoS attacks within EHR systems.

AI-based intrusion detection systems

Machine learning models are deployed to monitor IoT device activity and detect anomalies in data transmission that could indicate an attack, including replay attempts or adversarial manipulations [1]. However, these defensive AI systems are themselves potential targets for sophisticated adversarial attacks [6].

To build a resilient and trustworthy healthcare ecosystem, the following future demands must be addressed.

Transition to PQC

Patient health data must remain confidential for decades. As the development of quantum computing threatens to break current encryption standards, the healthcare industry must prioritize the adoption of PQC to ensure the long-term security of sensitive medical records [1]. Researchers emphasize the importance of developing these quantum-resistant security measures now to prepare for the future threat landscape [7]. To navigate this transition, Alhakami proposes a hierarchical framework utilizing multi-criteria decision-making (AHP- TOPSIS) to help practitioners prioritize quantum-level security factors [8].

Adoption of a zero-trust model

A zero-trust security model, where no device or user is trusted by default, is essential for healthcare environments. By requiring every entity to continuously authenticate before accessing cloud services or other devices, this model can significantly mitigate the risk of unauthorized access and enhance the resilience of the entire integrated system [9]. Ultimately, ensuring robust cybersecurity is no longer just a technical requirement but a fundamental component of modern patient safety and the ethical practice of medicine.

AI resilience and explainability

As AI becomes central to diagnosis and monitoring, security resilience must include adversarial training, ensemble learning and Explainable AI (XAI) frameworks. These techniques improve model transparency and robustness, reducing the likelihood of undetected manipulation in diagnostic pipelines.

The healthcare industry faces a dual imperative: maintaining operational effectiveness while safeguarding life-critical data and devices. Traditional defences, lightweight encryption and intrusion detection provide a baseline, but they are insufficient against nextgeneration threats. The future of secure IoT-cloud healthcare relies on quantum-resilient encryption, zero-trust enforcement and AI robustness. Cybersecurity in healthcare is no longer a support layer, but it is an ethical and clinical essential to patient safety and trust in digital medicine.

1. Almutairi M, Sheldon FT (2025) IoT-cloud integration security: A survey of challenges, solutions and directions. Electronics 14(7): 1394.
2. Seh AH, Zarour M, Alenezi M, Sarkar AK, Agrawal A, et al. (2020) Healthcare data breaches: Insights and implications. Healthcare 8(2): 133.
3. Granda CMM, Alemán JLF, Gea JM, Berná JA (2023) Security vulnerabilities in healthcare: An analysis of medical devices and software. Medical C Biological Engineering C Computing 62(1): 257-273.
4. Tsai MJ, Lin PY, Lee ME (2023) Adversarial attacks on medical image classification. Cancers 15(17): 4228.
5. Mekhlaf ZA, Saare M, Altmemi J, Shareeda MA, Mohammed B, et al. (2025) A quantum-resilient lattice-based security framework for internet of medical things in healthcare systems. Journal of King Saud University Computer and Information Sciences 37(6): 126.
6. Güngör O, Li E, Shang Z, Guo Y, Chen J, et al. (2024) Rigorous evaluation of machine learning-based intrusion detection against adversarial attacks. IEEE International Conference on Cyber Security and Resilience.
7. Olutimehin AT, Joseph SA, Ajayi AJ, Metibemu OC, Balogun AY, et al. (2025) Future-proofing data: Assessing the feasibility of post-quantum cryptographic algorithms to mitigate ‘harvest now, decrypt later’ Archives of Current Research International 25(3): 60-80.
8. Alhakami H (2024) Enhancing iot security: Quantum-level resilience against threats. Computers, Materials and Continua 78(1): 329-356.
9. Sarkar S, Choudhary G, Shandilya SK, Hussain A, Kim H (2022) Security of zero trust networks in cloud computing: A comparative review. Sustainability 14(18): 11213.