Distributed Denial of Service Attack Detection Using Machine Learning and Class Oversampling

Shafin, Sakib Shahriar; Prottoy, Sakir Adnan; Abbas, Saif; Hakim, Safayat Bin; Chowdhury, Abdullahi; Rashid, Md. Mamunur

doi:10.1007/978-3-030-82269-9_19

Cited by 13 publications

(2 citation statements)

References 11 publications

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“…4 And with the passage of time, in the medical sector, ML techniques are getting popularity, being effective for decisionmaking. 5,6 Using different kinds of ML algorithms is noticeable in RNA sequence data for different types of detection and to find out the correlation of sequences, 7 as well as for showing the effectiveness of machine learning algorithms in detecting splice variants from RNA sequence data. 8 Such as: To identify and classify cancers early on, different computer algorithms have been used on microarray data sets.…”

Section: Introductionmentioning

confidence: 99%

High throughput biological sequence analysis using machine learning-based integrative pipeline for extracting functional annotation and visualization

Al Amin,

Naznin,

Yeasmin

et al. 2024

F1000Res

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The Differential Gene Expression (DGE) approach to find out the expressed genes relies on measures such as log-fold change and adjusted p-values. Although fold change is commonly employed in gene expression studies, especially in microarray and RNA sequencing experiments to quantify alterations in a gene’s expression level, a limitation and potential hazard of relying on fold change in this context is its inherent bias. As a consequence, it might incorrectly categorize genes that have significant differences but minor ratios, resulting in poor detection of mutations in genes with high expression levels. In contrast, machine learning offers a more comprehensive view, adept at capturing the non-linear complexities of gene expression data and providing robustness against noise that inspired us to utilize machine learning models to explore differential gene expression based on feature importance in Type 2 Diabetes (T2D), a significant global health concern, in this study. Moreover, we validated biomarkers based on our findings expressed genes with previous studies to ensure the effectiveness of our ML models in this work which led us to go through to analysis pathways, gene ontologies, protein-protein interactions, transcription factors, miRNAs, and drug predictions to deal with T2D. This study aims to consider the machine learning technique as a good way to know about expressed genes profoundly not relying on the DGE approach, and to control or reduce the risk of T2D patients by helping drug developer researchers.

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Section: Introductionmentioning

confidence: 99%

High throughput biological sequence analysis using machine learning-based integrative pipeline for extracting functional annotation and visualization

Al Amin,

Naznin,

Yeasmin

et al. 2024

F1000Res

View full text Add to dashboard Cite

show abstract

“…We also employ PCA for effective dimensionality reduction, allowing us to manage the complex nature of network data. Simultaneously, our use of SMOTE addresses the challenge of class imbalance, a common issue in network security datasets [18], [19], [20]. To do In-depth comparative evaluation, the evaluation method involves a rigorous analysis of the models based on accuracy, precision, recall, and F1 score.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Comparison of Machine Learning Techniques for DDoS Attack Classification in Network Environments

Airlangga

2024

INFEB

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This research presents a comparative analysis of machine learning techniques for classifying Distributed Denial of Service (DDoS) attacks within network traffic. We evaluated the performance of three algorithms: Logistic Regression, Decision Tree, and Random Forest, including their scaled-feature counterparts. The study utilized a robust methodology incorporating advanced data preprocessing, feature engineering, and Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. The models were rigorously tested using a cross-validation framework, assessing their accuracy, precision, recall, and F1 score. Results indicated that the Random Forest algorithm outperformed the others, demonstrating superior predictive accuracy and consistency, albeit with higher computational costs. Logistic Regression, when feature-scaled, showed significant improvement in performance, highlighting the importance of data normalization in models sensitive to feature scaling. Decision Trees provided a quick and interpretable model, though slightly less accurate than the Random Forest. The research findings highlight the trade-offs between predictive performance and computational efficiency in selecting machine learning models for cybersecurity applications. The study contributes to the cybersecurity domain by elucidating the efficacy of ensemble techniques in DDoS attack classification and underscores the potential for model improvement through scaling and data balancing.

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An Explainable Feature Selection Framework for Web Phishing Detection with Machine Learning

Shafin

2024

Data Science and Management

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Distributed Denial of Service Attack Detection Using Machine Learning and Class Oversampling

Cited by 13 publications

References 11 publications

High throughput biological sequence analysis using machine learning-based integrative pipeline for extracting functional annotation and visualization

High throughput biological sequence analysis using machine learning-based integrative pipeline for extracting functional annotation and visualization

Analysis and Comparison of Machine Learning Techniques for DDoS Attack Classification in Network Environments

An Explainable Feature Selection Framework for Web Phishing Detection with Machine Learning

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