The k-Nearest Neighbors (kNN) method, established in 1951, has since evolved into a pivotal tool in data mining, recommendation systems, and Internet of Things (IoT), among other areas. This paper presents a comprehensive review and performance analysis of modifications made to enhance the exact kNN techniques, particularly focusing on kNN Search and kNN Join for high-dimensional data. We delve deep into 31 kNN search methods and 12 kNN join methods, providing a methodological overview and analytical insight into each, emphasizing their strengths, limitations, and applicability. An important feature of our study is the provision of the source code for each of the kNN methods discussed, fostering ease of experimentation and comparative analysis for readers. Motivated by the rising significance of kNN in high-dimensional spaces and a recognized gap in comprehensive surveys on exact kNN techniques, our work seeks to bridge this gap. Additionally, we outline existing challenges and present potential directions for future research in the domain of kNN techniques, offering a holistic guide that amalgamates, compares, and dissects existing methodologies in a coherent manner. Graphical Abstract

Intrusion Detection Systems (IDS) are essential for securing computer networks by identifying and mitigating potential threats. However, traditional IDS face challenges related to scalability, privacy, and computational demands as network data complexity increases. Federated Learning (FL) has emerged as a promising solution, enabling collaborative model training on decentralized data sources while preserving data privacy. Each participant retains local data repositories, ensuring data sovereignty and precluding data sharing. Leveraging the FL framework, participants locally train machine learning models on their respective datasets, subsequently transmitting model updates to a central server for aggregation. The central server then disseminates the aggregated model updates to individual participants, collectively striving to bolster intrusion detection capabilities. This article presents a comprehensive survey of FL applications in IDS, covering core concepts, architectural approaches, and aggregation strategies. We evaluate the strengths and limitations of various FL methodologies for IDS, addressing privacy and security concerns and exploring privacy-preserving techniques and security protocols. Our examination of aggregation strategies within the FL framework for IDS aims to highlight their effectiveness, limitations, and potential enhancements.

Detecting anomalies, intrusions, and security threats in the network (including Internet of Things) traffic necessitates the processing of large volumes of sensitive data, which raises concerns about privacy and security. Federated learning, a distributed machine learning approach, enables multiple parties to collaboratively train a shared model while preserving data decentralization and privacy. In a federated learning environment, instead of training and evaluating the model on a single machine, each client learns a local model with the same structure but is trained on different local datasets. These local models are then communicated to an aggregation server that employs federated averaging to aggregate them and produce an optimized global model. This approach offers significant benefits for developing efficient and effective intrusion detection system (IDS) solutions. In this research, we investigated the effectiveness of federated learning for IDSs and compared it with that of traditional deep learning models. Our findings demonstrate that federated learning, by utilizing random client selection, achieved higher accuracy and lower loss compared to deep learning, particularly in scenarios emphasizing data privacy and security. Our experiments highlight the capability of federated learning to create global models without sharing sensitive data, thereby mitigating the risks associated with data breaches or leakage. The results suggest that federated averaging in federated learning has the potential to revolutionize the development of IDS solutions, thus making them more secure, efficient, and effective.

The rapid expansion of IoT devices has introduced significant cybersecurity risks, as attackers increasingly exploit these networks’ vulnerabilities. To counter this threat, this paper presents the Privacy-Enhanced IoT Defence System (PEIoT-DS), a novel solution that emphasises data privacy while delivering high-performance intrusion detection. PEIoT-DS use federated learning to create a comprehensive intrusion detection model without necessitating the transmission of raw data to a central server. IoT devices only contribute model updates, which are then combined to improve the global model. While allowing devices to benefit from the network’s collective insights, this decentralised learning methodology safeguards data privacy. Using a real-world IoT dataset and two popular federated learning algorithms—Federated Average and Federated Average with Momentum—the study assesses the effectiveness of PEIoT-DS. The findings show that, in comparison to Federated Average, Federated Average with Momentum produces faster convergence and better intrusion detection accuracy. Our PEIoT-DS approach offers a reliable intrusion detection system for IoT networks while maintaining privacy.

The Internet of Things (IoT) has become increasingly popular, and opened new possibilities for applications in various domains. However, the IoT also poses security challenges due to the limited resources of the devices and its dynamic network topology. Routing attacks on 6LoWPAN-based IoT devices can be particularly challenging to detect because of its unique characteristics of the network. In recent years, several techniques have been proposed for detecting routing attacks, including anomaly detection. These techniques leverage different features of network traffic to identify and classify routing attacks. This paper focuses on routing attacks that target the Routing Protocol for Low-Power and Lossy Networks (RPL), which are widely used in 6LoWPAN-based IoT systems. The attacks discussed in this paper can be categorized as either inherited from Wireless Sensor Networks or exploiting vulnerabilities unique to RPL (known as RPL-specific attacks). The paper describes various RPL attacks, including Flood Attacks, Data-DoS/DDoS Attacks, Wormhole Attacks, RPL Rank Attacks, Blackhole Attacks, Version Attacks, and Sinkhole Attacks. In this paper, a novel Hybrid Intrusion Detection System (HIDS) that combines a decision tree classifier and a one-class Support Vector Machine classifier is proposed to detect routing attacks. The HIDS draws on the strengths of both a Signature Intrusion Detection System (SIDS) and an Anomaly-based Intrusion Detection System (AIDS) to identify routing attacks with a high degree of accuracy and a low false alarm rate. The routing dataset, which features genuine IoT network traffic and various kinds of routing attacks, was used to test the proposed HIDS. According to the findings, the hybrid IDS proposed in this study outperforms SIDS and AIDS approaches, with higher detection rates and lower false positive rates.

Cardiovascular disease (CVD) is the leading global cause of death, highlighting the urgent need for early, accurate, and interpretable diagnostic tools. However, many AI-based heart disease prediction models lack transparency, hindering their acceptance in clinical settings. This study proposes XAI-HD, a hybrid framework integrating machine learning (ML), deep learning (DL), and explainable AI (XAI) techniques for heart disease detection. The framework systematically addresses key challenges, including class imbalance, missing data, and feature inconsistency, through advanced preprocessing and class-balancing methods such as OSS, NCR, SMOTEN, ADASYN, SMOTETomek, and SMOTEENN. Comparative performance evaluations across multiple datasets (CHD, FHD, SHD) demonstrate that XAI-HD reduces classification error rates by 20–25% compared to traditional ML-based models, achieving superior accuracy, precision, recall, and F1-score. Additionally, SHAP and LIME-based feature importance analysis enhances model interpretability, fostering trust among medical professionals. The proposed framework holds significant real-world applicability, including seamless integration into hospital decision support systems, electronic health records (EHR), and real-time cardiac risk assessment platforms. Unlike conventional AI-driven cardiovascular risk prediction models, XAI-HD offers a more balanced, interpretable, and computationally efficient solution, ensuring both predictive accuracy and practical feasibility in clinical environments. Statistical validation using Wilcoxon signed-rank tests confirms the performance gains, and complexity analysis shows the framework is scalable for large-scale deployment.