## 1 Introduction  
Despite their potential to transform digital ecosystems, distributed ledger technologies are not immune to critical threats that challenge their security, scalability, and privacy guarantees. Traditional distributed ledger technologies (DLTs) like blockchain face issues such as Sybil attacks, 51% attacks, double-spending, data immutability abuse, privacy leakage, and scalability limitations, especially when deployed in dynamic and resource-constrained environments like the IoT [1, 2, 3]. These threats undermine trust, delay adoption, and complicate regulatory compliance, highlighting the need for more resilient, lightweight, and adaptive Distributed Ledger Technology (DLT) architectures. In recent years, the combined use of blockchain and machine learning has emerged as a promising approach to strengthening IoT security [4; 5; 6]. DLT-based systems offer decentralized identity management and tamper-evident audit trails, while ML enables real-time anomaly detection, intrusion detection, and intelligent threat response. Together, they provide complementary strengths for securing resource-constrained IoT environments. Prior studies have applied these technologies in sectors such as healthcare, supply chains, and wireless sensor networks, showing improvements in trust, transparency, and attack resilience. However, limitations remain regarding their scalability, latency, and energy efficiency--motivating the exploration of next-generation DLT architectures, such as Holochain. Holochain represents the beginning of a new era for open-source decentralized DLTs that can integrate with ML, offering a transformative alternative to traditional blockchain systems. Unlike blockchain, which relies on bulky data storage and centralized consensus mechanisms, Holochain is designed to operate without the need for global synchronization or resource-intensive data storage, making it a more efficient and scalable solution for decentralized applications [7]. By utilizing an agent-centric model and Distributed Hash Tables (DHTs), Holochain allows for seamless P2P communication and data exchange without the overhead typically associated with blockchain technologies. Figure 1 illustrates the three-layered architecture of IoT systems, consisting of the Perception/Data Collection Layer, Network/Data Processing Layer, and Application/Data Access Layer. This layered model provides a clear framework for understanding the flow of data in decentralized IoT environments, from device-level sensing and data generation to edge/cloud processing and ultimately user-facing applications involving analysis and decision-making. As the IoT continues to grow and proliferate across various industries, the need for decentralized, secure, and scalable solutions to manage and share IoT data has become increasingly critical. Each layer of the IoT architecture is susceptible to specific security threats and vulnerabilities that adversaries can exploit. At the Perception Layer, attacks such as data spoofing, clone node injection, and unauthorized device access can corrupt sensor readings and disrupt data integrity. Solutions in the literature include lightweight cryptographic schemes, secure boot mechanisms, and tamper-proof hardware modules. DLTs can mitigate these risks through decentralized identity management and immutable device logs. By assigning cryptographically verifiable identities to IoT devices and maintaining tamper-evident audit trails, DLTs ensure that only legitimate devices can participate in the network and that all interactions are transparently recorded. This enhances device trustworthiness and supports secure data provenance from the moment of collection. The Network Layer faces attacks like Sybil, sinkhole, selective forwarding, and Denial of Service (DoS), which threaten data transmission and network availability. Intrusion Detection Systems (IDSs) and trust-based routing protocols are among the proposed techniques in the literature to secure this layer.