| Title | DDoS Attack Detection System using Apache Spark |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Kousar, Heena, Mulla, Mohammed Moin, Shettar, Pooja, D. G., Narayan |
| Conference Name | 2021 International Conference on Computer Communication and Informatics (ICCCI) |
| Keywords | apache spark, Classification of DDos attack, Cluster computing, DDoS Attack, DDoS attack detection, Decision trees, denial-of-service attack, distributed processing, HDFS, Human Behavior, machine learning, Metrics, pubcrawl, resilience, Resiliency, Scalability, telecommunication traffic, Training |
| Abstract | Distributed Denial of Service Attacks (DDoS) are most widely used cyber-attacks. Thus, design of DDoS detection mechanisms has attracted attention of researchers. Design of these mechanisms involves building statistical and machine learning models. Most of the work in design of mechanisms is focussed on improving the accuracy of the model. However, due to large volume of network traffic, scalability and performance of these techniques is an important research issue. In this work, we use Apache Spark framework for detection of DDoS attacks. We use NSL-KDD Cup as a benchmark dataset for experimental analysis. The results reveal that random forest performs better than decision trees and distributed processing improves the performance in terms of pre-processing and training time. |
| DOI | 10.1109/ICCCI50826.2021.9457012 |
| Citation Key | kousar_ddos_2021 |
DDoS Attack Detection System using Apache Spark