Federated Learning in Smart Home Intrusion Detection: Securing the Learning Process with Blockchain

Fri, 03 Apr 2026 05:52:32 +0000

The rapid expansion of Internet of Things (IoT) devices has fundamentally reshaped traditional homes into highly interconnected smart environments, driving unprecedented convenience while simultaneously exposing critical cybersecurity vulnerabilities. Conventional centralized Intrusion Detection Systems (IDS) are increasingly inadequate for smart homes, owing to inherent privacy issues, latency, and susceptibility to single points of failure. Federated Learning (FL) offers a promising, privacy-preserving paradigm by enabling collaborative model training without the exchange of raw user data; however, FL remains vulnerable to model poisoning and integrity attacks. This comprehensive review systematically examines the integration of blockchain technology as a means to secure FL-based IDS in smart home contexts. We critically evaluate diverse blockchain frameworks, consensus protocols, and enhancement strategies that underpin the development of trustworthy, transparent, and resilient security architectures. Specifically, the review addresses two core research questions: (1) In what ways are blockchain frameworks and consensus mechanisms leveraged to secure FL-based IDS? and (2) What enhancement techniques and hybrid models are implemented to optimize the performance and efficiency of these integrated systems? Our synthesis reveals that permissioned blockchains—particularly those employing Proof of Authority (PoA) and Practical Byzantine Fault Tolerance (PBFT) consensus mechanisms—when combined with edge computing and lightweight architectural designs, deliver robust security while meeting the resource constraints characteristic of IoT devices. This technological synergy not only strengthens the resilience of the FL process but also advances the development of next-generation, secure smart home ecosystems.

Establishing A Strategic Cybersecurity Platform Amid the Rapid Rise of Artificial Intelligence to Mitigate Cybersecurity Threats

Mon, 15 Jun 2026 08:21:32 +0000

The rapid expansion of the virtual world and the increasing integration of artificial intelligence (AI) have intensified cybersecurity threats, posing significant risks to national security, economic stability, and societal well-being. As digital transformation accelerates across sectors, cyber threats continue to grow in complexity and impact, often exceeding traditional security capabilities. This study analyzes the contemporary cybersecurity threat landscape globally and regionally, with a focus on Jordan and selected Arab countries, and examines the strategic importance of developing an integrated cybersecurity platform to address emerging virtual threats. Using a qualitative analytical approach, the study conducts a systematic review of peer-reviewed literature, international cybersecurity reports, and global indices (2015–2024). A thematic framework is applied to identify threat trends, institutional readiness gaps, and strategic enablers across governance, technology, human capacity, and cooperation dimensions. Findings indicate rising AI-driven cyber threats with notable economic, political, and social implications. Despite progress in several Arab countries, particularly GCC states, challenges remain in coordination, proactive risk management, and platform integration. The study proposes a conceptual cybersecurity platform integrating regulation, advanced technologies, human capital development, and international cooperation to enhance national and organizational resilience.

Network and Communication Technologies, Issue: Vol.10, No.1

Federated Learning in Smart Home Intrusion Detection: Securing the Learning Process with Blockchain

Establishing A Strategic Cybersecurity Platform Amid the Rapid Rise of Artificial Intelligence to Mitigate Cybersecurity Threats