| Title | Multi-Core Parallel Processing Technique to Prepare the Time Series Data for the Early Detection of DDoS Flooding Attacks |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Kumar, S. Ratan, Kumari, V. Valli, Raju, K. V. S. V. N. |
| Conference Name | 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom) |
| Keywords | cloud computing, denial-of-service attack, Distributed databases, distributed denial of service attacks, Floods, Internet of Thing, Internet of Things, Metrics, multicore computing security, parallel processing, pubcrawl, resilience, Resiliency, Scalability, Time series analysis |
| Abstract | Distributed Denial of Service (DDoS) attacks pose a considerable threat to Cloud Computing, Internet of Things (IoT) and other services offered on the Internet. The victim server receives terabytes of data per second during the DDoS attack. It may take hours to examine them to detect a potential threat, leading to denial of service to legitimate users. Processing vast volumes of traffic to mitigate the attack is a challenging task for network administrators. High-performance techniques are more suited for processing DDoS attack traffic compared to Sequential Processing Techniques. This paper proposes a Multi-Core Parallel Processing Technique to prepare the time series data for the early detection of DDoS flooding attacks. Different time series analysis methods are suggested to detect the attack early on. Producing time series data using parallel processing saves time and further speeds up the detection of the attack. The proposed method is applied to the benchmark data set CICDDoS2019 for generating four different time series to detect TCP-based flooding attacks, namely TCP-SYN, TCP-SYN-ACK, TCP-ACK, and TCP-RST. The implementation results show that the proposed method can give a speedup of 2.3 times for processing attack traffic compared to sequential processing. |
| DOI | 10.1109/INDIACom51348.2021.00096 |
| Citation Key | kumar_multi-core_2021 |
Multi-Core Parallel Processing Technique to Prepare the Time Series Data for the Early Detection of DDoS Flooding Attacks