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 4
- Downloads 0
Effective medical leaf identification using hybridization of GMM-CNN
Medical plants play a vital role in curing many diseases. These plants, along with their leaves, have medicinal values. If these leaves are identified appropriately, they can be chosen directly to have more significant r...
Phytochemical Investigation and Antiulcer Potential of Strychnos Nux vomica Seed Extract in Adult Wistar Rats
The current work sought to assess the possible anti-ulcer efficacy of Strychnos nux-vomica methanolic seed extracts in rats by conducting a thorough phytochemical analysis. This study aims to indicate and discover benefi...
Development of a Regression Model for Prediction of Chronic Kidney Disease Risk
In recent years, chronic kidney disease (CKD) has been widespread in public health. Therefore, the early prediction of these diseases can save many lives. Keeping this fact in mind, this study presents a new way to predi...
Exploring the Influence of Arbuscular Mycorrhizal Symbology on the Antioxidant Potential of Liverwort Asterella multiflora: A Comprehensive Study on Rhizoid and Thallus Anatomy
Arbuscular mycorrhizal (AM) symbiosis is a vital ecological interaction between plants and fungi that enhances nutrient uptake and plant resilience. While extensively studied in vascular plants, AM symbiosis in liverwort...
Alcoholic Extracts of Eleusine indica as Alternative Diuretic Regimens: A Computational Based Investigation
Diuretics are widely used in current clinical practice to increase urine production and excrete electrolytes, particularly sodium and chloride ions, without affecting the absorption of protein, vitamins, carbohydrates, o...