A multi-objective mutation-based dynamic Harris Hawks optimization for botnet detection in IoT

Internet of Things, Год журнала: 2023, Номер 24, С. 100952 - 100952

Опубликована: Сен. 24, 2023

Язык: Английский

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Anomaly-based intrusion detection system in the Internet of Things using a convolutional neural network and multi-objective enhanced Capuchin Search Algorithm

Journal of Parallel and Distributed Computing, Год журнала: 2023, Номер 175, С. 1 - 21

Опубликована: Янв. 9, 2023

Язык: Английский

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Botnets Unveiled: A Comprehensive Survey on Evolving Threats and Defense Strategies

Transactions on Emerging Telecommunications Technologies, Год журнала: 2024, Номер 35(11)

Опубликована: Окт. 20, 2024

ABSTRACT Botnets have emerged as a significant internet security threat, comprising networks of compromised computers under the control command and (C&C) servers. These malevolent entities enable range malicious activities, from denial service (DoS) attacks to spam distribution phishing. Each bot operates binary code on vulnerable hosts, granting remote attackers who can harness combined processing power these hosts for synchronized, highly destructive while maintaining anonymity. This survey explores botnets their evolution, covering aspects such life cycles, C&C models, botnet communication protocols, detection methods, unique environments operate in, strategies evade tools. It analyzes research challenges future directions related botnets, with particular focus evasion techniques, including methods like encryption use covert channels reinforcement botnets. By reviewing existing research, provides comprehensive overview origins evolving tactics, evaluates how counteract activities. Its primary goal is inform community about changing landscape in combating threats, offering guidance addressing concerns effectively through highlighting methods. The concludes by presenting directions, using strengthen aims guide researchers developing more robust measures combat effectively.

Язык: Английский

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Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review

Internet of Things and Cyber-Physical Systems, Год журнала: 2024, Номер 4, С. 258 - 267

Опубликована: Янв. 1, 2024

The significance of intrusion detection systems in networks has grown because the digital revolution and increased operations. method classifies network traffic as threat or normal based on data features. Intrusion system faces a trade-off between various parameters such accuracy, relevance, redundancy, false alarm rate, other objectives. paper presents systematic review Internet Things (IoT) using multi-objective optimization algorithms (MOA), to identify attempts at exploiting security vulnerabilities reducing chances attacks. MOAs provide set optimized solutions for process highly complex IoT networks. This identification multiple objectives detection, comparative analysis their approaches, datasets used evaluation. show encouraging potential enhance conflicting detection. Additionally, current challenges future research ideas are identified. In addition demonstrating new advancements techniques, this study gaps that can be addressed while designing

Язык: Английский

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MTV-SCA: multi-trial vector-based sine cosine algorithm

Cluster Computing, Год журнала: 2024, Номер 27(10), С. 13471 - 13515

Опубликована: Июнь 28, 2024

Язык: Английский

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A robust intrusion detection system based on a shallow learning model and feature extraction techniques

PLoS ONE, Год журнала: 2024, Номер 19(1), С. e0295801 - e0295801

Опубликована: Янв. 24, 2024

The escalating prevalence of cybersecurity risks calls for a focused strategy in order to attain efficient resolutions. This study introduces detection model that employs tailored methodology integrating feature selection using SHAP values, shallow learning algorithm called PV-DM, and machine classifiers like XGBOOST. efficacy our suggested is highlighted by employing the NSL-KDD UNSW-NB15 datasets. Our approach dataset exhibits exceptional performance, with an accuracy 98.92%, precision recall 95.44%, F1-score 96.77%. Notably, this performance achieved utilizing only four characteristics, indicating efficiency approach. proposed achieves 82.86%, 84.07%, 77.70%, 80.20% dataset, six features. research findings provide substantial evidence enhanced compared traditional deep-learning across all metrics.

Язык: Английский

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A Hybrid Multi-population Optimization Algorithm for Global Optimization and Its Application on Stock Market Prediction

Computational Economics, Год журнала: 2024, Номер unknown

Опубликована: Май 24, 2024

Язык: Английский

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Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications

Biomimetics, Год журнала: 2023, Номер 9(1), С. 9 - 9

Опубликована: Дек. 25, 2023

Feature selection is becoming a relevant problem within the field of machine learning. The feature focuses on small, necessary, and sufficient subset features that represent general set features, eliminating redundant irrelevant information. Given importance topic, in recent years there has been boom study problem, generating large number related investigations. this, this work analyzes 161 articles published between 2019 2023 (20 April 2023), emphasizing formulation performance measures, proposing classifications for objective functions evaluation metrics. Furthermore, an in-depth description analysis metaheuristics, benchmark datasets, practical real-world applications are presented. Finally, light advances, review paper provides future research opportunities.

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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How Is the Objective Function of the Feature Selection Problem Formulated?

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 3 - 13

Опубликована: Янв. 1, 2025

Язык: Английский

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