The widespread adoption of distributed systems, driven by the growth of the Internet of Things (IoT), edge computing, and cloud infrastructure, has substantially expanded the attack surface of modern digital ecosystems. These environments, characterized by high heterogeneity, large data volumes, and stringent latency requirements, make real-time threat detection a complex task. Traditional, pre-dominantly centralized security mechanisms reveal clear limitations in scalability and response time in the face of increasingly dynamic attack patterns. In this context, Artificial Intelligence (AI) and Machine Learning have emerged as essential enablers for more effective intrusion detection. At the same time, the concept of “super nodes” is gaining prominence: strategically positioned network elements with enhanced computational capabilities that act as intelligent intermediaries between edge devices and the central cloud. This study presents a bibliometric analysis of the use of AI-based super nodes for real-time threat detection. The analysis focuses on a sample of 300 publications indexed in the Lens.org database (2015–2025), selected according to the PRISMA 2020 guidelines. Through descriptive indicators and network analysis (such as keyword co-occurrence), research trends, the-matic structures, and emerging directions in this field are identified.

As quantum computing advances, secure communications must evolve to adapt. Neuropil is a lightweight protocol created by Pi-lar GmbH for secure exchanges in IoT systems. It currently relies on classical cryptographic techniques, including X25519 for key exchange and Ed25519 for authentication, which are vulnerable to quantum attacks. This study shows how lattice-based CRYSTALS Kyber and CRYSTALS Dilithium can be added to Neuropil without changing its protocol architecture. The work begins with a study of post-quantum cryptography. It looks at the NIST standardisation process, and chooses Kyber-512 for key encapsulation and Dilithium-2 for digital signatures, because they match the AES-128 security level with small keys, signatures and ciphertext sizes that fit IoT devices. Integration keeps the original 1024-byte handshake, places a Kyber ciphertext and a Dilithium signature in the next join message, and turns on the extra code only when the build flag NP_USE_OQS is enabled. A hybrid solution was implemented in C using the liboqs library that combines classical and post-quantum algorithms. Tests indicate reliable operation with only a small increase in connection setup time, and no significant effect on the traffic. A built-in cluster driver that starts 10 in-process nodes shows that the hybrid build increases the mean of the join round-trip time from 4.9 ms to 70.4 ms, and adds about 70 kilobytes of start-up traffic. After it settles, the message rate and neighbour count match the classical run. The analysis shows that forging the leftover Ed25519 fingerprint within the 5 ms Handshake gap would need about one trillion physical qubits, far beyond today’s quantum-computing roadmap plans. Evaluations confirm compliance with NIST post-quantum standards and the resilience of the implementation. Because Neuropil keeps backward compatibility, devices that use only classical crypto work as before, while updated nodes get quantum-safe keys. Future work will test regular rotation of Kyber key pairs to give full forward secrecy, speed up heavy math with SIMD and FPGA hardware, and run trials across many regions with thousands of nodes to check real network impact. These results demonstrate that Neuropil can be upgraded to have post-quantum security, remaining efficient and compatible with existing deployments.

Recently, consumers have seen multiple products being advertised as smart home. These products promise to make our homes more comfortable, safe, automated, and remotely controlled. To this new reality of processing information it was given the name IoT (Internet of Things). Many news headlines have been published exposing serious security vulnerabilities in many IoT devices, with some of them being exploited to make one of the largest DDoS attacks recorded. In this paper we present a method developed with the purpose of identifying high risk vulnerabilities in smart home IoT devices, giving application examples of actual vulnerabilities found in two commercially available devices. This method uses several open source tools to identify vulnerabilities in some of these IoT devices. Besides, we will also present some topics related to the main threats and vulnerabilities that affect smart home IoT devices.

The increasing adoption of IoT devices highlights the urgent need for robust security solutions, particularly on low-end MCUs. However, these platforms typically lack the advanced security mechanisms found in higher-end processors, making them highly vulnerable to attacks. This paper presents an interoperable solution that enables Trusted Execution Environments (TEEs) on low-end MCUs, leveraging platform-specific hardware security primitives available in both ARMv7-M and RISC-V architectures. By complementing these primitives with software-based isolation techniques, the proposed solution achieves strong domain isolation even in resource-constrained devices. Additionally, the adoption of the GlobalPlatform API provides a common abstraction layer that decouples application development from platform-specific details, allowing trusted applications to run seamlessly across different MCU platforms. In the evaluation, we assessed the solutions through performance microbenchmarks and real-world applications, demonstrating their feasibility and the trade-offs between security and performance across different workloads.

The increasing number of Internet of Things devices and the increasing adoption of smart homes have led to an increase in risk related to cybersecurity. The purpose of this dissertation is to examine these limitations and propose innovative solutions for anomaly detection using machine learning (ML) methods. The objectives and motivations for this work, which focuses on improving home network security, are explained in Chapter 1, which also provides a review of smart homes and their connections with cybersecurity issues. In Chapter 2, "State of the Art," the rise of IoT in everyday use and related safety concerns are addressed. In addition, it covers over basic concepts like machine learning strategies and the way these interact with intrusion detection systems (IDS). In order to mitigate increasing threats it considers that integrating ML with cybersecurity in IoT systems is important. The machine learning techniques selected for this project are presented in Chapter 3, with a focus on creating a reliable anomaly detection pipeline.Comprehensive data pre-processing, including cleaning, merging, normalization, and analysis, ensures sure the data is suitable for model training. In Chapter 4, training individual and ensemble models will be addressed along with an analysis of performance metrics in scenarios using binary and multi-class classification.Z-Score normalization is one strategy that is frequently used to handle unbalanced datasets. It has been demonstrated to perform better than Min-Max, especially when applied to the UNSW-NB15 dataset. The implementation of APIs using Streamlit for real-time visualisation and FastAPI for backend integration with ML models will be discussed in Chapter 5.This combination enables the ability to anticipate cyberattacks and visually represent anomalies in an effective way. The testbed built to automate cyberattacks and extract important features for model training will be discussed in detail in Chapter 6. The results of the evaluations, that compare the performance of the individual models and the ensemble, are presented in Chapter 7. The ensemble performed better than expected, especially when it came to identifying anomalies in multi-class environments. It achieved this with high accuracy and a significant reduction in false positives and negatives. Chapter 8 ends with an overview of the project’s conclusions and contributions.The main findings emphasize the significance of selecting normalization strategies and the advantages of using ensemble models to improve attack detection.

Recently, consumers have seen multiple products being advertised as smart home. These products promise to make our homes more comfortable, safe, automated, and remotely controlled. To this new reality of processing information it was given the name IoT (Internet of Things). Many news headlines have been published exposing serious security vulnerabilities in many IoT devices, with some of them being exploited to make one of the largest DDoS attacks recorded. In this paper we present a method developed with the purpose of identifying high risk vulnerabilities in smart home IoT devices, giving application examples of actual vulnerabilities found in two commercially available devices. This method uses several open source tools to identify vulnerabilities in some of these IoT devices. Besides, we will also present some topics related to the main threats and vulnerabilities that affect smart home IoT devices.