Asian Journal of Research in Computer Science

Artificial Intelligence and Post-Quantum Cryptography for Healthcare Cybersecurity: A Critical Review of Techniques, Architectures, and Open Challenges

2026-09-26T12:18:03+00:00

Aims: This critical review examines how artificial intelligence (AI) and post-quantum cryptography (PQC) can be combined to strengthen cybersecurity in healthcare information systems, with an emphasis on the Internet of Medical Things (IoMT), clinical data platforms, zero-trust access control and cryptographic migration.

Study Design: The review uses a structured literature-search and evidence-mapping approach. It emphasises peer-reviewed sources in computer science, cybersecurity, healthcare informatics and cryptography, as well as authoritative standards.

Methodology: Evidence was organised into healthcare/IoMT cybersecurity, AI-enabled detection and response, PQC and migration, and integrated security architecture. NIST, FDA and IETF material was used to distinguish finalised standards from emerging work. Studies were compared by security function, deployment layer, resource overhead, interoperability, clinical risk and empirical validation.

Results: AI and PQC address different but complementary requirements. AI supports anomaly detection, behavioural analytics, alert prioritisation and migration assistance, but is exposed to evasion, poisoning, drift and explainability problems. NIST FIPS 203, FIPS 204 and FIPS 205 provide finalised standards for ML-KEM, ML-DSA and SLH-DSA. Major deployment barriers include constrained devices, larger keys and signatures, legacy dependencies, interoperability, implementation leakage, regulation and limited real-world validation. A five-layer reference architecture and a phased migration architecture are proposed.

Conclusion: Healthcare organisations should begin cryptographic discovery and risk-based PQC migration while strengthening AI security controls. Future work should prioritise hardware-aware PQC benchmarks, adversarially robust AI, clinically safe automated responses, cryptographic agility and multi-site validation.

A Multi-Layer Security Evaluation Framework for Hardening Kubernetes in Devsecops Pipelines: A Quantitative Experimental Evaluation

2026-09-26T06:44:58+00:00

Background: Kubernetes is widely used to orchestrate containerised workloads in DevSecOps environments; however, its complex configuration, access-control mechanisms, software supply chain, and runtime operations create multiple security challenges. Existing security controls often address these risks independently, creating a need for an integrated and measurable framework that evaluates prevention, software-supply-chain integrity, and runtime detection and response within a unified Kubernetes environment.

Aims: To design, implement, and quantitatively evaluate a Multi-Layer Security Evaluation Framework (MSEF) that hardens Kubernetes workloads across prevention, software-supply-chain integrity, and runtime detection and response.

Study Design: A controlled quantitative experimental design using comparative baseline and hardened Kubernetes environments.

Place and Duration of Study: A cloud-based experimental testbed implemented on Google Kubernetes Engine (GKE) and operated through a DevSecOps toolchain during the 2026 evaluation period.

Methodology: The Prevention Layer used OPA Gatekeeper and Kubernetes Pod Security Admission to measure Manifest Blocking Rate (MBR), Network Policy Enforcement Rate (NPER), and Secrets Management Enforcement Rate (SMER); the Integrity Layer used Kyverno and Sigstore Cosign to measure Secrets Management Enforcement Rate (SMER) and Signature Policy Rejection Rate (SPR); and the Detection Layer used Falco, Falcosidekick, and a Kubernetes Runtime Evaluation (KRE) handler to evaluate Runtime Detection Rate (RDR), Mean Time to Detect (MTTD), Operational False Positive Rate (OFPR), and Runtime Response Success Rate (RRSR). Layer-level effectiveness composite scores were defined for prevention, integrity, and detection. Terraform set up and configured the cloud infrastructure, ArgoCD reconciled platform services and workloads, and GitHub Actions deployed the repeatable evaluation.

Results: The final validated prevention experiments achieved MBR = 1.00, SMER = 1.00, and NPER = 1.00; the integrity experiment achieved SPR = 1.00; and, in the detection experiment, Falco detected all five evaluated runtime attack windows (RDR = 1.00). The measured MTTD was 23.00 seconds, and no operational false-positive windows (OFPR = 0.00) were observed in the five benign workload windows. The initial MBR of 0.95 exposed a secret-access RBAC policy gap that was subsequently corrected. The recorded RRSR run produced 0.78 because Falco event-to-KRE response-rule alignment was still under validation; this value is therefore treated as an integration result rather than evidence of successful automated remediation. Layer-level effectiveness composite scores for prevention and integrity are 100%, while detection is 90%.

Conclusion: MSEF's unique contribution to knowledge is the multi-layer integration of controls with explicit experiments, quantitative metrics, feedback-driven policy refinement, and browser-based reporting, rather than the deployment of individual security tools, because it provides a replicable and measurable defence-in-depth model for Kubernetes security.

Self-healing Microservice Architecture Using Autonomous AI Agents for Cloud Applications

2026-09-28T13:37:41+00:00

Cloud-native microservice applications provide scalability and deployment flexibility but remain susceptible to service failures, cascading faults, and delayed recovery. This study presents a Self-Healing Microservice Architecture (SHMA) that combines Kubernetes-based container orchestration, autonomous AI agents, a binary failure-prediction component, and reinforcement-learning-based recovery selection. The prototype was implemented with Spring Boot, Docker, Kubernetes, TensorFlow, Prometheus, Grafana, and PostgreSQL. Evaluation considered Mean Time to Recovery (MTTR), availability, recovery accuracy, response time, throughput, transaction success, and failure-prediction metrics. In the reported controlled testbed, MTTR decreased from 240 s to 48 s, availability increased from 98.2% to 99.8%, recovery accuracy increased from 84% to 98%, and service downtime decreased from 12 min to 2 min. The reported 1,000-observation confusion matrix yielded 95.7% accuracy, 96.5% precision, 95.2% recall, and 95.8% F1-score for failure prediction. In the increasing-workload experiment, response time remained at or below 1.18 s through 1,200 concurrent users, peak observed throughput reached 1,620 requests/s, and the successful transaction rate was 99.9%. These findings provide descriptive evidence that combining predictive monitoring with policy-driven recovery can improve fault-management responsiveness in a controlled Kubernetes environment. The results are interpreted within the limits of a single-cluster evaluation and aggregate point estimates, and broader generalisation requires repeated and multi-environment validation.

A Retrieval-augmented Generation Framework for Policy-grounded Course Advising in Higher Education

2026-10-01T10:08:22+00:00

Course advising in higher education requires students and advisers to integrate information from course catalogues, programme requirements, departmental handbooks, prerequisite rules, and institutional regulations. This study presents a Retrieval-Augmented Generation (RAG) framework for policy-grounded undergraduate course advising at Babcock University, Nigeria. The prototype combines a structured academic database, an indexed institutional knowledge base, a large language model, and a separate validation layer for prerequisite and credit-load constraints. The implementation includes student-profile management, natural-language querying, knowledge-base administration, authentication, and chat-log review. Evaluation used 18 institutional source documents segmented into 246 indexed chunks, 30 simulated student profiles, 60 advisory queries, and benchmark judgements from three academic advisers. The qualitative beta evaluation examined policy retrieval, policy grounding, prerequisite and credit-limit validation, response clarity, ambiguity handling, advising usefulness, and observed failure conditions. The findings provide formative evidence that the framework can retrieve relevant institutional information, incorporate evidence into advisory responses, and apply deterministic checks to explicit academic rules. However, context fragmentation and ambiguity in source documents remained important limitations, and the evaluation did not provide statistical performance estimates. The framework is therefore positioned as a decision-support tool that retains human oversight for ambiguous or high-stakes advising cases and requires larger controlled evaluation before measurable advising performance can be established.