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 32
- Downloads 0
Diversity of Endophytic fungi in liana, Celastrus paniculatus collected from few sites of Jhargram and Paschim Medinipur districts, West Bengal, India
To determine the identity and diversity of endophytic fungi associated with the liana from five different forest localities of Jhargram and West Medinipur districts of West Bengal. On the basis of differentiation of weat...
A study on abundance and group diversity of soil microarthropods at four different soil habitats in North Dinajpur, West Bengal, India
Sampling was conducted at four different sites i.e., an agricultural field, a river basin, a brick field and a forest floor from the district of Uttar Dinajpur, West Bengal, India. Though abundance was higher at the fore...
Okara–by-product from soy processing: characteristic, properties, benefits, and potential perspectives for industry
A by-product from processing of soy into drinksand tofu is the insoluble portion of soybeans, a high-fiber product called okara. With the growing interest in plant substitutes for meat and milk, which are produced...
Water bird diversity in Winter and Summer season of Motijheel lake, Murshidabad, West Bengal, India
Wetlands are the most preferred habitats for water birds. They support the birds by way of feeding, roosting, nesting and rearing young ones. Birds are essential animal group of an ecosystem and maintain a trophic level....
Work participation rate and development status of Bongaon, Basirhat and Barasat subdivisions of North 24 Parganas District, West Bengal, India
The changing pattern of demography characterized with different types of economic characteristics i.e., size and growth of working population, changing pattern of occupational structure, shifting of worker from primary t...