A new intrusion detection system based on SVM–GWO algorithms for Internet of Things

Ghasemi, Hamed; Babaie, Shahram

doi:10.1007/s11276-023-03637-6

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2024

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Cited by 3 publications

References 39 publications

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A Smart Coherent Security Model (SCSM) using Intelligent Optimization and Ensemble Deep Learning Mechanisms for Healthcare-IoT Networks

Venkatesan,

Ramakrishnan

2024

2024 Control Instrumentation System Conference (CISCON)

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A Smart Coherent Security Model (SCSM) using Intelligent Optimization and Ensemble Deep Learning Mechanisms for Healthcare-IoT Networks

Venkatesan,

Ramakrishnan

2024

2024 Control Instrumentation System Conference (CISCON)

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Intrusion Detection System in Mobile Cloud Computing Using Bat Optimization Algorithm-Support Vector Machine

Alabdeli,

Yamsani,

et al. 2024

2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)

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SEDAT: A Stacked Ensemble Learning-Based Detection Model for Multiscale Network Attacks

Feng,

Yang,

Sun

et al. 2024

Electronics

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Anomaly detection for network traffic aims to analyze the characteristics of network traffic in order to discover unknown attacks. Currently, existing detection methods have achieved promising results against high-intensity attacks that aim to interrupt the operation of the target system. In reality, attack behaviors that are commonly exhibited are highly concealed and disruptive. In addition, the attack scales are flexible and variable. In this paper, we construct a multiscale network intrusion behavior dataset, which includes three attack scales and two multiscale attack patterns based on probability distribution. Specifically, we propose a stacked ensemble learning-based detection model for anomalous traffic (or SEDAT for short) to defend against highly concealed multiscale attacks. The model employs a random forest (RF)-based method to select features and introduces multiple base learning autoencoders (AEs) to enhance the representation of multiscale attack behaviors. In addressing the challenge of a single model’s inability to capture the regularities of multiscale attack behaviors, SEDAT is capable of adapting to the complex multiscale characteristics in network traffic, enabling the prediction of network access behavior. Comparative experiments demonstrate that SEDAT exhibits superior detection capabilities in multiscale network attacks. In particular, SEDAT achieves an improvement of at least 5% accuracy over baseline methods for detecting multiscale attacks.

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A new intrusion detection system based on SVM–GWO algorithms for Internet of Things

Cited by 3 publications

References 39 publications

A Smart Coherent Security Model (SCSM) using Intelligent Optimization and Ensemble Deep Learning Mechanisms for Healthcare-IoT Networks

A Smart Coherent Security Model (SCSM) using Intelligent Optimization and Ensemble Deep Learning Mechanisms for Healthcare-IoT Networks

Intrusion Detection System in Mobile Cloud Computing Using Bat Optimization Algorithm-Support Vector Machine

SEDAT: A Stacked Ensemble Learning-Based Detection Model for Multiscale Network Attacks

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