Examining Privacy and Trust Issues at the Edge of Isomorphic IoT Architectures: Case Liquid AI

Advanced Information Security and Assurance: A Literature Review of Zero Trust Architecture, Artificial Intelligence, and Cybersecurity Resilience

The quick uptake of cloud computing, artificial intelligence (AI), Internet of Things (IoT), and hybrid workplace solutions has changed the cybersecurity needs in a way that renders the traditional notion of perimeter defense inefficient in addressing sophisticated attack scenarios such as ransomware, insider threats, and advanced persistent threats (APTs). This literature review analyzes advancements in the field of Advanced Information Security and Assurance from 2016 to 2026. Key advancements include NIST SP 800-207 published in 2020, widespread use of Zero Trust Architecture (ZTA), and incorporation of AI into security analytics. The reviewed sources show that Zero Trust drastically minimizes attack surfaces using continuous authentication, least-privilege access, and microsegmentation. Also, AI is beneficial in improving threat detection through predictive analytics, behavioral anomalies detection, and automation of the incident response process. On the other hand, AI also poses emerging risks such as adversarial machine learning, automated API reconnaissance, AI phishing scams, and intelligent malware. Additionally, cyber resilience, explainable AI, and adaptive governance are the identified research areas important for protecting future digital infrastructures. While significant advances have been made, there are still many challenges related to complexity, interoperability, staffing shortage, privacy, and governance.

Zero Trust Architecture

This paper synthesizes reported evidence on ZTA across three research questions: effectiveness relative to perimeter models, the implementation challenges enterprises face, and the security contribution of individual components.

Federated Learning and LLM-Driven Threat Intelligence for Zero Trust IoT Architecture

A Federated Learning and LLM-Driven Threat Intelligence for Zero Trust IoT Architecture is presented, with FL for anomaly detection integrating privacy-preserving distributed learning, continuous identity verification, and LLM-driven autonomous threat response into a unified pipeline.

Cybersecurity in the IoT Era: Protecting the Expanding Internet of Things

The Internet of Things (IoT) is transforming industries and daily life by connecting billions of devices, enabling smart homes, cities and industrial systems. This rapid expansion, however, introduces significant cybersecurity vulnerabilities, leaving IoT systems increasingly exposed to both established and emerging attack techniques. This paper presents a structured critical review of IoT cybersecurity, distinguished from prior general surveys by three contributions: first, a cross-layer mapping of named, dated case studies to the specific Security-by-Design principles that would have mitigated them; second, a comparative, feasibility-based evaluation of lightweight cryptographic primitives and blockchain consensus protocols for resource-constrained devices, rather than a descriptive overview; and third, a critical appraisal of the operational limitations of AI-based and blockchain-based defences, including adversarial manipulation, data scarcity and energy cost, set against the claims commonly made for these technologies. We examine the current state of IoT security across the perception, network and application layers; the common vulnerabilities that affect these systems, from insecure device design and weak default credentials to unencrypted communications; and the real-world consequences of these flaws through recent, named case studies, including the Aisuru botnet which is active since 2024 and 2024 vulnerability disclosures affecting Mitsubishi Electric and OMRON industrial controllers. We argue that securing the IoT ecosystem requires sustained, coordinated effort from manufacturers, regulators and end-users, and we identify where current technological and regulatory responses fall short of that goal.

AI-enabled IoT mobile cloud security: trends and challenges

A Comprehensive Survey of AI Security Threats in IoT and Edge Cloud Systems

This survey research work proposes a lifecycle-based understanding of AI security threats and proposed a unified taxonomy for the four major categories of threats observed in real-world settings, namely, data poisoning and backdoor attacks on learning model updates, adversarial attacks on model outputs through input manipulation, privacy leakage through model-based queries, and model extraction for intellectual property theft and creation of rogue replicas of learning models.