Towards an Optimized Ensemble Feature Selection for DDoS Detection Using Both Supervised and Unsupervised Method

Saha, Sajal; Priyoti, Annita Tahsin; Sharma, Aakriti; Haque, Anwar

doi:10.3390/s22239144

Cited by 9 publications

(6 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, both supervised and unsupervised learning approaches are used to assess the quality of each feature subset and identify the best-performing one. Our experiment shows that the EnFS technique beats individual FS and offers a universally optimal feature set for AI models [8]. Traditional techniques cannot be used with SDN due to the design differences between the two networking paradigms.…”

Section: Introductionmentioning

confidence: 80%

Feature-Selection-Based DDoS Attack Detection Using AI Algorithms

Raza,

Sheikh,

Hwang

et al. 2024

Telecom

View full text Add to dashboard Cite

SDN has the ability to transform network design by providing increased versatility and effective regulation. Its programmable centralized controller gives network administration employees more authority, allowing for more seamless supervision. However, centralization makes it vulnerable to a variety of attack vectors, with distributed denial of service (DDoS) attacks posing a serious concern. Feature selection-based Machine Learning (ML) techniques are more effective than traditional signature-based Intrusion Detection Systems (IDS) at identifying new threats in the context of defending against distributed denial of service (DDoS) attacks. In this study, NGBoost is compared with four additional machine learning (ML) algorithms: convolutional neural network (CNN), Stochastic Gradient Descent (SGD), Decision Tree, and Random Forest, in order to assess the effectiveness of DDoS detection on the CICDDoS2019 dataset. It focuses on important measures such as F1 score, recall, accuracy, and precision. We have examined NeTBIOS, a layer-7 attack, and SYN, a layer-4 attack, in our paper. Our investigation shows that Natural Gradient Boosting and Convolutional Neural Networks, in particular, show promise with tabular data categorization. In conclusion, we go through specific study results on protecting against attacks using DDoS. These experimental findings offer a framework for making decisions.

show abstract

Section: Introductionmentioning

confidence: 80%

Feature-Selection-Based DDoS Attack Detection Using AI Algorithms

Raza,

Sheikh,

Hwang

et al. 2024

Telecom

View full text Add to dashboard Cite

show abstract

“…In the analysis of intestinal microbiota in SLE patients, characterizing microbiota data at the taxonomic level and removing redundant and irrelevant features is necessary to reduce noise. ML algorithms offer three feature selection methods: filtering, packaging, and embedding ( Saha et al., 2022 ). This study uses embedded-based Elastic Net and packaging-based Boruta algorithms due to the differences in the subset of features selected by different methods.…”

Section: Methodsmentioning

confidence: 99%

Gut microbiota landscape and potential biomarker identification in female patients with systemic lupus erythematosus using machine learning

Song,

Wu,

Yan

et al. 2023

Front. Cell. Infect. Microbiol.

View full text Add to dashboard Cite

ObjectivesSystemic Lupus Erythematosus (SLE) is a complex autoimmune disease that disproportionately affects women. Early diagnosis and prevention are crucial for women’s health, and the gut microbiota has been found to be strongly associated with SLE. This study aimed to identify potential biomarkers for SLE by characterizing the gut microbiota landscape using feature selection and exploring the use of machine learning (ML) algorithms with significantly dysregulated microbiotas (SDMs) for early identification of SLE patients. Additionally, we used the SHapley Additive exPlanations (SHAP) interpretability framework to visualize the impact of SDMs on the risk of developing SLE in females.MethodsStool samples were collected from 54 SLE patients and 55 Negative Controls (NC) for microbiota analysis using 16S rRNA sequencing. Feature selection was performed using Elastic Net and Boruta on species-level taxonomy. Subsequently, four ML algorithms, namely logistic regression (LR), Adaptive Boosting (AdaBoost), Random Forest (RF), and eXtreme gradient boosting (XGBoost), were used to achieve early identification of SLE with SDMs. Finally, the best-performing algorithm was combined with SHAP to explore how SDMs affect the risk of developing SLE in females.ResultsBoth alpha and beta diversity were found to be different in SLE group. Following feature selection, 68 and 21 microbiota were retained in Elastic Net and Boruta, respectively, with 16 microbiota overlapping between the two, i.e., SDMs for SLE. The four ML algorithms with SDMs could effectively identify SLE patients, with XGBoost performing the best, achieving Accuracy, Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, and AUC values of 0.844, 0.750, 0.938, 0.923, 0.790, and 0.930, respectively. The SHAP interpretability framework showed a complex non-linear relationship between the relative abundance of SDMs and the risk of SLE, with Escherichia_fergusonii having the largest SHAP value.ConclusionsThis study revealed dysbiosis in the gut microbiota of female SLE patients. ML classifiers combined with SDMs can facilitate early identification of female patients with SLE, particularly XGBoost. The SHAP interpretability framework provides insight into the impact of SDMs on the risk of SLE and may inform future scientific treatment for SLE.

show abstract

“…The authors in [10] presented a DDoS-detection analysis by using seven ML algorithms, four DL, and five unsupervised models. They evaluated the performance of 15 methods to select features according to the UNSW_NB-15 dataset.…”

Section: Comprehensive Overviewmentioning

confidence: 99%

Optimized MLP-CNN Model to Enhance Detecting DDoS Attacks in SDN Environment

Setitra,

Fan,

Agbley

et al. 2023

Network

View full text Add to dashboard Cite

In the contemporary landscape, Distributed Denial of Service (DDoS) attacks have emerged as an exceedingly pernicious threat, particularly in the context of network management centered around technologies like Software-Defined Networking (SDN). With the increasing intricacy and sophistication of DDoS attacks, the need for effective countermeasures has led to the adoption of Machine Learning (ML) techniques. Nevertheless, despite substantial advancements in this field, challenges persist, adversely affecting the accuracy of ML-based DDoS-detection systems. This article introduces a model designed to detect DDoS attacks. This model leverages a combination of Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) to enhance the performance of ML-based DDoS-detection systems within SDN environments. We propose utilizing the SHapley Additive exPlanations (SHAP) feature-selection technique and employing a Bayesian optimizer for hyperparameter tuning to optimize our model. To further solidify the relevance of our approach within SDN environments, we evaluate our model by using an open-source SDN dataset known as InSDN. Furthermore, we apply our model to the CICDDoS-2019 dataset. Our experimental results highlight a remarkable overall accuracy of 99.95% with CICDDoS-2019 and an impressive 99.98% accuracy with the InSDN dataset. These outcomes underscore the effectiveness of our proposed DDoS-detection model within SDN environments compared to existing techniques.

show abstract

Towards an Optimized Ensemble Feature Selection for DDoS Detection Using Both Supervised and Unsupervised Method

Cited by 9 publications

References 40 publications

Feature-Selection-Based DDoS Attack Detection Using AI Algorithms

Feature-Selection-Based DDoS Attack Detection Using AI Algorithms

Gut microbiota landscape and potential biomarker identification in female patients with systemic lupus erythematosus using machine learning

Optimized MLP-CNN Model to Enhance Detecting DDoS Attacks in SDN Environment

Contact Info

Product

Resources

About