HoleMal: A lightweight IoT malware detection framework based on efficient host-level traffic processing

Computers & Security, Год журнала: 2025, Номер unknown, С. 104360 - 104360

Опубликована: Фев. 1, 2025

Язык: Английский

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Construction of a BIM smart building collaborative design model combining the Internet of Things

Nonlinear Engineering, Год журнала: 2025, Номер 14(1)

Опубликована: Янв. 1, 2025

Abstract The research aims to solve the problem of data synchronization and redundancy in building information model co-design with blockchain technology. A hyper-ledger fabric federated blockchain, combined a revolving door compression algorithm, is used for construction an intelligent model. Experiments showed that method outperformed other technologies terms throughput response time, block-out time reduced by 19.31% transaction increased 12.38%. proposes innovative cycle division mechanism utilizes algorithm maintenance model, thereby enhancing security design efficiency collaboration. This positive significance design. However, limitation study only blockchain-based designed, further development example validation are needed future.

Язык: Английский

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Feature selection for IoT botnet detection using equilibrium and Battle Royale Optimization

Computers & Security, Год журнала: 2024, Номер 147, С. 104060 - 104060

Опубликована: Авг. 23, 2024

Язык: Английский

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A new a flow-based approach for enhancing botnet detection using convolutional neural network and long short-term memory

Knowledge and Information Systems, Год журнала: 2025, Номер unknown

Опубликована: Апрель 16, 2025

Язык: Английский

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Enhancing IoT security: A comparative study of feature reduction techniques for intrusion detection system

Intelligent Systems with Applications, Год журнала: 2024, Номер 23, С. 200407 - 200407

Опубликована: Июнь 15, 2024

Internet of Things (IoT) devices are extensively utilized but susceptible to cyberattacks, posing significant security challenges. To mitigate these threats, machine learning techniques have been implemented for network intrusion detection in IoT environments. These commonly employ various feature reduction methods, prior inputting data into models, order enhance the efficiency processes meet real-time requirements. This study provides a comprehensive comparison selection (FS) and extraction (FE) systems (NIDS) environments, utilizing TON-IoT BoT-IoT datasets both binary multi-class classification tasks. We evaluated FS including Pearson correlation Chi-square, FE such as Principal Component Analysis (PCA) Autoencoders (AE), across five classic models: Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), k-Nearest Neighbors (kNN), Multi-Layer Perceptron (MLP). Our analysis revealed that generally achieve higher accuracy robustness compared with RF paired AE delivering superior performance despite computational demands. DTs most effective smaller sets, while MLPs excel larger sets. Chi-square is identified efficient method, balancing efficiency, whereas PCA outperforms runtime efficiency. The also highlights methods more complex less sensitive set size, show improvements informative features. Despite costs they demonstrate greater capability detect diverse attack types, making them particularly suitable findings crucial academic research industry applications, providing insights optimizing NIDS networks.

Язык: Английский

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3DLBS-BCHO: a three-dimensional deep learning approach based on branch splitter and binary chimp optimization for intrusion detection in IoT

Cluster Computing, Год журнала: 2024, Номер 28(2)

Опубликована: Ноя. 26, 2024

Язык: Английский

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Botnet detection in the internet-of-things networks using convolutional neural network with pelican optimization algorithm

Automatika, Год журнала: 2023, Номер 65(1), С. 250 - 260

Опубликована: Дек. 27, 2023

Hackers nowadays employ botnets to undertake cyberattacks towards the Internet of Things (IoT) by illegally exploiting scattered network's resources computing devices. Several Machine Learning (ML) and Deep (DL) methods for detecting botnet (BN) assaults in IoT networks have recently been proposed. However, training set, severely imbalanced network traffic data degrades classification performances state-of-the-art ML as well DL algorithm, particularly classes with very few samples. The Convolutional Neural Network -Pelican Optimization System (CNN-POA) is a relied attack detection algorithm developed this research. Meanwhile, typical evaluation markers are used compare overall performance proposed CNN-POA additional frequently employed algorithms. simulation results suggest that method effective dependable intrusion attacks. Experiments revealed suggested approach outperformed number current metaheuristic algorithms, an accuracy 99.5%.

Язык: Английский

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An Improved hybrid Salp Swarm Optimization and African Vulture Optimization Algorithm for Global Optimization Problems and Its Applications in Stock Market Prediction

Research Square (Research Square), Год журнала: 2023, Номер unknown

Опубликована: Апрель 7, 2023

Abstract Optimization is necessary for solving and improving the solution of various complex problems. Every meta-heuristic algorithm can have a weak point, multiple mechanisms methods be used to overcome these weaknesses. We use hybrid algorithms arrive at an efficient algorithm. This paper presents new intelligent approach by hybridizing using different simultaneously without significantly increasing time complexity. For this purpose, two algorithms, Salp Swarm Optimization(SSO) African Vulture Algorithm (AVOA) been hybridized. And improve optimization process Modified Choice Function Learning Automata mechanisms. In addition, other mechanisms, named Opposition-Based (OBL) β-hill climbing (BHC) technique, presented integrated with AVOA-SSA Fifty-two standard benchmarks were test evaluate Finally, improved version Extreme Machine(ELM) classifier has real stock market data prediction. The obtained results indicate excellent acceptable performance in `solving problems able achieve high-quality solutions.

Язык: Английский

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AI/ML driven intrusion detection framework for IoT enabled cold storage monitoring system

Security and Privacy, Год журнала: 2024, Номер 7(5)

Опубликована: Апрель 18, 2024

Abstract An IoT‐based monitoring system remotely controls and manages intelligent environments. Due to wireless communication, deployed sensor nodes are more vulnerable attacks. intrusion detection is an efficient mechanism detect malicious traffic prevent abnormal activities. This article suggests framework for the cold storage system. The temperature main parameter that affects environment harms stored products. A node injects false data manipulates forwards manipulated data. It also floods neighbor nodes. In this work, generated collected detection. Two machine learning techniques have been applied: supervised (Bayesian rough set) unsupervised (micro‐clustering). proposed method shows better performance than existing methods.

Язык: Английский

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An ensemble system for machine learning IoT intrusion detection based on enhanced artificial hummingbird algorithm

The Journal of Supercomputing, Год журнала: 2024, Номер 81(1)

Опубликована: Ноя. 1, 2024

Язык: Английский

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