A Survey on Key Agreement and Authentication Protocol for Internet of Things Application

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 61642 - 61666

Published: Jan. 1, 2024

The Internet of Things (IoT) represents a dynamic infrastructure, leveraging sensing and network communication technology to establish ubiquitous connectivity among people, machines, objects. Due its end devices' limited computing resources storage space, it is not feasible merely transpose traditional internet security technologies directly IoT endpoints. Maintaining while concurrently ensuring performance particularly challenging endeavor. This paper provides review key agreements authentication protocols pivotal the IoT. First, this survey discusses applications that need agreement strengthen their current research on these application fields. Subsequently, engages in an in-depth exploration phase involved scheme agreement, including examination cryptographic techniques employed within processes. also thoroughly studies scheme's services, potential attacks, formal analysis informal ensure resilience against such threats. study aims provide profound understanding recent applications. It strives contribute towards strengthening systems for applications, sustainability face evolving

Language: Английский

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XI2S-IDS: An Explainable Intelligent 2-Stage Intrusion Detection System

Future Internet, Journal Year: 2025, Volume and Issue: 17(1), P. 25 - 25

Published: Jan. 8, 2025

The rapid evolution of technologies such as the Internet Things (IoT), 5G, and cloud computing has exponentially increased complexity cyber attacks. Modern Intrusion Detection Systems (IDSs) must be capable identifying not only frequent, well-known attacks but also low-frequency, subtle intrusions that are often missed by traditional systems. challenge is further compounded fact most IDS rely on black-box machine learning (ML) deep (DL) models, making it difficult for security teams to interpret their decisions. This lack transparency particularly problematic in environments where quick informed responses crucial. To address these challenges, we introduce XI2S-IDS framework—an Explainable, Intelligent 2-Stage System. framework uniquely combines a two-stage approach with SHAP-based explanations, offering improved detection interpretability low-frequency Binary classification conducted first stage followed multi-class second stage. By leveraging SHAP values, enhances decision-making, allowing analysts gain clear insights into feature importance model’s rationale. Experiments UNSW-NB15 CICIDS2017 datasets demonstrate significant improvements performance, notable reduction false negative rates attacks, while maintaining high precision, recall, F1-scores.

Language: Английский

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Analysis of Extreme Learning Machines (ELMs) for intelligent intrusion detection systems: A survey

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 253, P. 124317 - 124317

Published: May 27, 2024

Language: Английский

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An explainable multi-modal model for advanced cyber-attack detection in industrial control systems

Internet of Things, Journal Year: 2024, Volume and Issue: 25, P. 101092 - 101092

Published: Feb. 3, 2024

Language: Английский

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Two-step data clustering for improved intrusion detection system using CICIoT2023 dataset

e-Prime - Advances in Electrical Engineering Electronics and Energy, Journal Year: 2024, Volume and Issue: 9, P. 100673 - 100673

Published: July 5, 2024

The issue of network security is an important and delicate when it comes to the privacy organizations individuals, especially sensitive information transmitted across these networks. importance intrusion detection systems, which a very component protecting reducing damage resulting from attacks penetrations has increased due adoption most recent regulations on advanced web services, whether government banking e-mail, or e-marketing. goal this paper construct system using deep learning algorithms based new dataset named CICIoT2023. proposed model addresses challenges associated with datasets in terms high dimensionality by adopting methods reduce their size improve efficiency. A clustering technique for method combination between optimization algorithm static tools was proposed. evaluated determine its efficiency several evaluation measures. results show that comparison earlier research conducted same datasets, suggested performs better attack detection. As result, offers level trust.

Language: Английский

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Recent advances in anomaly detection in Internet of Things: Status, challenges, and perspectives

Computer Science Review, Journal Year: 2024, Volume and Issue: 54, P. 100665 - 100665

Published: Aug. 23, 2024

Language: Английский

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An explainable multi-objective hybrid machine learning model for reducing heart failure mortality

PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2682 - e2682

Published: Feb. 25, 2025

As the world grapples with pandemics and increasing stress levels among individuals, heart failure (HF) has emerged as a prominent cause of mortality on global scale. The most effective approach to improving chances individuals' survival is diagnose this condition at an early stage. Researchers widely utilize supervised feature selection techniques alongside conventional standalone machine learning (ML) algorithms achieve goal. However, these approaches may not consistently demonstrate robust performance when applied data that they have encountered before, struggle discern intricate patterns within data. Hence, we present Multi-objective Stacked Enable Hybrid Model (MO-SEHM), aims find out best subsets numerous different sets, considering multiple objectives. (SEHM) plays role classifier integrates multi-objective method, Non-dominated Sorting Genetic Algorithm II (NSGA-II). We employed HF dataset from Faisalabad Institute Cardiology (FIOC) evaluated six ML models, including SEHM without NSGA-II for experimental purposes. Pareto front (PF) demonstrates our introduced MO-SEHM surpasses other obtaining 94.87% accuracy nine relevant features. Finally, Local Interpretable Model-agnostic Explanations (LIME) explain reasons individual outcomes, which makes model transparent patients stakeholders.

Language: Английский

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Optimizing Intrusion Detection for IoT: A Systematic Review of Machine Learning and Deep Learning Approaches With Feature Selection and Data Balancing

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2025, Volume and Issue: 15(2)

Published: March 28, 2025

ABSTRACT As the Internet of Things (IoT) continues expanding its footprint across various sectors, robust security systems to mitigate associated risks are more critical than ever. Intrusion Detection Systems (IDS) fundamental in safeguarding IoT infrastructures against malicious activities. This systematic review aims guide future research by addressing six pivotal questions that underscore development advanced IDS tailored for environments. Specifically, concentrates on applying machine learning (ML) and deep (DL) technologies enhance capabilities. It explores feature selection methodologies aimed at developing lightweight solutions both effective efficient scenarios. Additionally, assesses different datasets balancing techniques, which crucial training models perform accurately reliably. Through a comprehensive analysis existing literature, this highlights significant trends, identifies current gaps, suggests studies optimize frameworks ever‐evolving landscape.

Language: Английский

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Multi-attention DeepCRNN: an efficient and explainable intrusion detection framework for Internet of Medical Things environments

Knowledge and Information Systems, Journal Year: 2025, Volume and Issue: unknown

Published: April 5, 2025

Language: Английский

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Secure AI for 6G Mobile Devices: Deep Learning Optimization Against Side-Channel Attacks

IEEE Transactions on Consumer Electronics, Journal Year: 2024, Volume and Issue: 70(1), P. 3951 - 3959

Published: Feb. 1, 2024

Deep learning-driven side-channel analysis (SCA) is a promising approach to analytic profiling. Recent studies have shown that neural networks can successfully attack defended targets, even with small number of traces. However, developing requires fine-tuning hyperparameters, which challenging and time-consuming, especially for complex networks. This study proposes an AutoSCA framework uses Bayesian optimization automate deep learning hyperparameter tuning SCA. The implemented using two popular network architectures: the multi-layer perceptron (MLP) convolutional (CNN). improves performance measurements, has potential applications in 6G communication-based mobile devices. was trained evaluated ASCAD CHES CTF datasets. experimental results showed CNN-based outperformed MLP-based other state-of-the-art models, terms low time complexity higher accuracy. Results suggest effective regardless dataset, architecture, or type leaky prototype defeating contemporary attacks. Applying against attacks consumer electronics significantly enhance security user data privacy increasingly connected.

Language: Английский

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