Enhanced Network Defense: Optimized Multi-Layer Ensemble for DDoS Attack Detection
Journal Title: International Journal of Experimental Research and Review - Year 2024, Vol 46, Issue 10
Abstract
In today's digitally connected world, Distributed Denial of Service (DDoS) attacks remain a formidable challenge, undermining the stability of network infrastructures and demanding robust detection strategies. This research explores advanced methodologies for DDoS detection by conducting a comparative analysis of machine learning and deep learning approaches using the CICDDoS2019 dataset. Initially, a hybrid machine learning framework is implemented, integrating K-Means clustering for pre-labeling the dataset and employing supervised models such as Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), and Artificial Neural Network (ANN). This approach achieves an accuracy of 99.46%, showcasing its effectiveness while highlighting challenges like manual feature selection and limited scalability for complex datasets. A novel hybrid deep learning architecture is proposed to overcome these challenges, combining Convolutional Neural Networks (CNN) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal sequence learning. This automated feature extraction mechanism eliminates reliance on manual intervention, ensuring adaptability to evolving attack patterns. The proposed CNN-LSTM model demonstrates an impressive accuracy of 99.84%, significantly outperforming traditional machine learning models. Additionally, the model's adaptability and resilience against dynamic attack behaviours position it as a reliable solution for real-time DDoS mitigation. This study emphasizes the growing relevance of deep learning techniques in enhancing cyber security and underscores the potential of hybrid architectures in effectively detecting and mitigating modern cyber threats. The findings provide valuable insights into developing scalable, high-performance systems capable of addressing the ever-evolving nature of DDoS attacks.
Authors and Affiliations
Deepak Singh Rajput, Arvind Kumar Upadhyay
Keywords
Related Articles
- EP ID EP754343
- DOI 10.52756/ijerr.2024.v46.020
- Views 18
- Downloads 0
QbD-Driven Development and Validation of a Bioanalytical LC–MS Method for Quantification of Paliperidone in Human Plasma
This paper discusses how a Quality by Design (QbD) strategy was used to develop and test an HPLC-MS bioanalytical method for detecting plasma Paliperidone concentration. A C18 column and an isocratic mobile phase of orga...
Antioxidant Potential of Bioactive Peptides Derived from Fish Waste: A Focus on Catla catla Liver and Intestinal Tissue
Fish waste, if not managed properly, poses a significant environmental threat. Scientists worldwide have been exploring innovative ways to utilize this resource, finding applications in pharmaceuticals and nutraceuticals...
Performance Evaluation of YOLOv5-based Custom Object Detection Model for Campus-Specific Scenario
This study evaluates the performance of a custom object detection model based on the YOLOv5 architecture, specifically tailored for autonomous electric vehicles. The model undergoes pre-processing using the Roboflow comp...
Methodological Approaches to Optical Disc and Optical Cup Segmentation: A Critical Assessment
A progressive optic nerve condition called glaucoma causes irreversible eyesight loss. To diagnose retinal diseases, retinal fundus imaging has been used in recent years. Analyzing these images effectively requires pinpo...
Anthropometric characteristics of adult Bengalee slum dwellers of Midnapore town, Paschim Medinipore, West Bengal, India
A cross sectional study of 1000 Bengalee adults slum dwellers (male=494, female=506) aged 18-81 years of Midnapore town, Paschim Medinipore, West Bengal, India was undertaken to study anthropometric characteristics as we...