Adaptive Learning and Automatic Filtering of Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environment
B. S. Kiruthika Devi1, T. Subbulakshmi2
1B. S. Kiruthika Devi, Department of Computing Science and Engineering, Vellore Institute of Technology Chennai (Tamil Nadu), India.
2T. Subbulakshmi, Department of Computing Science and Engineering, Vellore Institute of Technology Chennai (Tamil Nadu), India.
Manuscript received on 14 December 2019 | Revised Manuscript received on 22 December 2019 | Manuscript Published on 31 December 2019 | PP: 200-205 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10391291S319/19©BEIESP | DOI: 10.35940/ijeat.A1039.1291S319
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Distributed Denial of Service (DDoS) attacks has become the most powerful cyber weapon to target the businesses that operate on the cloud computing environment. The sophisticated DDoS attack affects the functionalities of the cloud services and affects its core capabilities of cloud such as availability and reliability. The current intrusion detection system (IDS) must cope with the dynamicity and intensity of immense traffic at the cloud hosted applications and the security attack must be inspected based on the attack flow characteristics. Hence, the proposed Adaptive Learning and Automatic Filtering of Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environment is designed to adapt with varying kind of protocol attacks using misuse detection. The system is equipped with custom and threshold techniques that satisfies security requirements and can identify the different DDoS security attacks. The proposed system provides promising results in detecting the DDoS attacks in cloud environment with high detection accuracy and good alert reduction. Threshold method provides 98% detection accuracy with 99.91%, 99.92% and 99.94% alert reduction for ICMP, UDP and TCP SYN flood attack. The defense system filters the attack sources at the target virtual instance and protects the cloud applications from DDoS attacks.
Keywords: Ddos, Cloud Computing, IDS, Virtual Instance, Detection, Defense.
Scope of the Article: Cloud Computing