Introduced a new method for enhancement of intrusion detection with random forest and <scp>PSO</scp> algorithm

Ajdani, Mahdi; Ghaffary, Hamid Reza

doi:10.1002/spy2.147

Cited by 19 publications

(11 citation statements)

References 18 publications

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“…Specifically, various Machine Learning (ML) and Deep Learning (DL) models have been developed to classify network traffic data in IoT networks. These models learn the discriminating features of benign traffic and malicious traffic using different architectures such as Random Forest (RF) [ 18 ], Support Vector Machine (SVM) [ 19 ], Deep Neural Network (DNN) [ 20 ], Recurrent Neural Network (RNN) [ 21 ], Long Short-Term Memory (LSTM) [ 22 ] and Gated Recurrent Unit (GRU) [ 23 ]. For an in-depth understanding, comprehensive reviews and surveys on the application of ML and DL in intrusion detection are presented in [ 24 , 25 , 26 , 27 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

Popoola

Adebisi

Ande

et al. 2021

Sensors

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Nowadays, hackers take illegal advantage of distributed resources in a network of computing devices (i.e., botnet) to launch cyberattacks against the Internet of Things (IoT). Recently, diverse Machine Learning (ML) and Deep Learning (DL) methods were proposed to detect botnet attacks in IoT networks. However, highly imbalanced network traffic data in the training set often degrade the classification performance of state-of-the-art ML and DL models, especially in classes with relatively few samples. In this paper, we propose an efficient DL-based botnet attack detection algorithm that can handle highly imbalanced network traffic data. Specifically, Synthetic Minority Oversampling Technique (SMOTE) generates additional minority samples to achieve class balance, while Deep Recurrent Neural Network (DRNN) learns hierarchical feature representations from the balanced network traffic data to perform discriminative classification. We develop DRNN and SMOTE-DRNN models with the Bot-IoT dataset, and the simulation results show that high-class imbalance in the training data adversely affects the precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), geometric mean (GM) and Matthews correlation coefficient (MCC) of the DRNN model. On the other hand, the SMOTE-DRNN model achieved better classification performance with 99.50% precision, 99.75% recall, 99.62% F1 score, 99.87% AUC, 99.74% GM and 99.62% MCC. Additionally, the SMOTE-DRNN model outperformed state-of-the-art ML and DL models.

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Section: Introductionmentioning

confidence: 99%

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

Popoola

Adebisi

Ande

et al. 2021

Sensors

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“…The results indicated that the proposed algorithm resulted in significant improvement in the network intrusion detection by 4 to 6% and decreased the false alarm rate by 5–1% from the same models’ corresponding values without pretrains in similar datasets. In [ 20 ], a new method has been introduced for enhancing intrusion detection performance. With increasing network attacks and intrusions in computer networks, the importance of developing security policies, documenting existing threats, and preventing individuals from violating security policies to secure information was identified.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel hybrid hunger games algorithm for intrusion detection systems based on nonlinear regression modeling

Mohammadi

Babagoli

2023

Int. J. Inf. Secur.

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Along with the advancement of online platforms and significant growth in Internet usage, various threats and cyber-attacks have been emerging and become more complicated and perilous in a day-by-day base. Anomaly-based intrusion detection systems (AIDSs) are lucrative techniques for dealing with cybercrimes. As a relief, AIDS can be equipped with artificial intelligence techniques to validate traffic contents and tackle diverse illicit activities. A variety of methods have been proposed in the literature in recent years. Nevertheless, several important challenges like high false alarm rates, antiquated datasets, imbalanced data, insufficient preprocessing, lack of optimal feature subset, and low detection accuracy in different types of attacks have still remained to be solved. In order to alleviate these shortcomings, in this research a novel intrusion detection system that efficiently detects various types of attacks is proposed. In preprocessing, Smote-Tomek link algorithm is utilized to create balanced classes and produce a standard CICIDS dataset. The proposed system is based on gray wolf and Hunger Games Search (HGS) meta-heuristic algorithms to select feature subsets and detect different attacks such as distributed denial of services, Brute force, Infiltration, Botnet, and Port Scan. Also, to improve exploration and exploitation and boost the convergence speed, genetic algorithm operators are combined with standard algorithms. Using the proposed feature selection technique, more than 80 percent of irrelevant features are removed from the dataset. The behavior of the network is modeled using nonlinear quadratic regression and optimized utilizing the proposed hybrid HGS algorithm. The results show the superior performance of the hybrid algorithm of HGS compared to the baseline algorithms and the well-known research. As shown in the analogy, the proposed model obtained an average test accuracy rate of 99.17%, which has better performance than the baseline algorithm with 94.61% average accuracy.

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“…Bagging follows "voting" and "regression averaging" methods for solving the classification problems. Random forest (RF) is an example of bagging that is widely used in design of an IDS [52]. e structure of bagging algorithm is very much similar to the structure of general ensemble learning and has been shown in Figure 1.…”

Section: Baggingmentioning

confidence: 99%

An Ensemble-Based Multiclass Classifier for Intrusion Detection Using Internet of Things

Rani

Gill

Gulia

et al. 2022

Computational Intelligence and Neuroscience

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Internet of Things (IoT) is the fastest growing technology that has applications in various domains such as healthcare, transportation. It interconnects trillions of smart devices through the Internet. A secure network is the basic necessity of the Internet of Things. Due to the increasing rate of interconnected and remotely accessible smart devices, more and more cybersecurity issues are being witnessed among cyber-physical systems. A perfect intrusion detection system (IDS) can probably identify various cybersecurity issues and their sources. In this article, using various telemetry datasets of different Internet of Things scenarios, we exhibit that external users can access the IoT devices and infer the victim user’s activity by sniffing the network traffic. Further, the article presents the performance of various bagging and boosting ensemble decision tree techniques of machine learning in the design of an efficient IDS. Most of the previous IDSs just focused on good accuracy and ignored the execution speed that must be improved to optimize the performance of an ID model. Most of the earlier pieces of research focused on binary classification. This study attempts to evaluate the performance of various ensemble machine learning multiclass classification algorithms by deploying on openly available “TON-IoT” datasets of IoT and Industrial IoT (IIoT) sensors.

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Introduced a new method for enhancement of intrusion detection with random forest and PSO algorithm

Cited by 19 publications

References 18 publications

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

A novel hybrid hunger games algorithm for intrusion detection systems based on nonlinear regression modeling

An Ensemble-Based Multiclass Classifier for Intrusion Detection Using Internet of Things

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