Selection of efficient machine learning algorithm on Bot-IoT dataset for intrusion detection in internet of things networks

Kerrakchou, Imane; Hassan, Adil Abou El; Chadli, Sara; Emharraf, Mohamed; Saber, M.

doi:10.11591/ijeecs.v31.i3.pp1784-1793

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…ANN [46] 99.42% DL-RL [47] 97.29% Proposed method (RF with statistical feature and ADASYN) 99.9985 WUSTL Dataset CNN-LSTM [48] 99% MLP [49] 96.39% CNN-Focal [50] 93.08% Proposed method (RF with RFE and ADASYN) 99.9777…”

Section: Unsw Datasetmentioning

confidence: 99%

A Machine Learning-Based Framework with Enhanced Feature Selection and Resampling for Improved Intrusion Detection

Malik,

Waqas Khan,

Rizwan

et al. 2024

Mathematics

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Intrusion Detection Systems (IDSs) play a crucial role in safeguarding network infrastructures from cyber threats and ensuring the integrity of highly sensitive data. Conventional IDS technologies, although successful in achieving high levels of accuracy, frequently encounter substantial model bias. This bias is primarily caused by imbalances in the data and the lack of relevance of certain features. This study aims to tackle these challenges by proposing an advanced machine learning (ML) based IDS that minimizes misclassification errors and corrects model bias. As a result, the predictive accuracy and generalizability of the IDS are significantly improved. The proposed system employs advanced feature selection techniques, such as Recursive Feature Elimination (RFE), sequential feature selection (SFS), and statistical feature selection, to refine the input feature set and minimize the impact of non-predictive attributes. In addition, this work incorporates data resampling methods such as Synthetic Minority Oversampling Technique and Edited Nearest Neighbor (SMOTE_ENN), Adaptive Synthetic Sampling (ADASYN), and Synthetic Minority Oversampling Technique–Tomek Links (SMOTE_Tomek) to address class imbalance and improve the accuracy of the model. The experimental results indicate that our proposed model, especially when utilizing the random forest (RF) algorithm, surpasses existing models regarding accuracy, precision, recall, and F Score across different data resampling methods. Using the ADASYN resampling method, the RF model achieves an accuracy of 99.9985% for botnet attacks and 99.9777% for Man-in-the-Middle (MITM) attacks, demonstrating the effectiveness of our approach in dealing with imbalanced data distributions. This research not only improves the abilities of IDS to identify botnet and MITM attacks but also provides a scalable and efficient solution that can be used in other areas where data imbalance is a recurring problem. This work has implications beyond IDS, offering valuable insights into using ML techniques in complex real-world scenarios.

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Section: Unsw Datasetmentioning

confidence: 99%

A Machine Learning-Based Framework with Enhanced Feature Selection and Resampling for Improved Intrusion Detection

Malik,

Waqas Khan,

Rizwan

et al. 2024

Mathematics

View full text Add to dashboard Cite

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A Hybrid Technique Based on RF-PCA and ANN for Detecting DDoS Attacks IoT

Hayder Jalo,

Mohsen Heydarian

2024

IJDS

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The increasing reliance on smart products has increased vulnerabilities in Internet of Things (IoT) traffic, which poses significant security risks. These vulnerabilities allowed some hackers to exploit them, which led to system performance degradation. Attacks can lead to these vulnerabilities to various undesirable outcomes, including data leakage, economic losses, data breaches, operational disruptions, and damage to the company's reputation. To address these security challenges, network intrusion detection alarms play a crucial role in assessing system security. In recent years, the proliferation of intelligent and soft computing-based algorithmic and structural frameworks has been evident. However, previous studies have faced challenges related to comprehensiveness, zero-day attacks, realism, and data interpretation. In light of these concerns, this study proposes to design a neural network for proactive detection of attacks. Moreover, we propose to use a hybrid system called RF-PCA to facilitate dimensionality reduction and help classifiers. Notably, this is the first application of a BOT-IoT data set in such an approach. The study also includes a discussion of relevant IoT terms in the context of our work. The proposed method uses high-level data features to represent and draw conclusive conclusions. To evaluate its effectiveness, an experiment was conducted using Python as the programming environment, achieving a remarkable detection rate of 99.73%.

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Selection of efficient machine learning algorithm on Bot-IoT dataset for intrusion detection in internet of things networks

Cited by 2 publications

References 25 publications

A Machine Learning-Based Framework with Enhanced Feature Selection and Resampling for Improved Intrusion Detection

A Machine Learning-Based Framework with Enhanced Feature Selection and Resampling for Improved Intrusion Detection

A Hybrid Technique Based on RF-PCA and ANN for Detecting DDoS Attacks IoT

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