KS-DDoS: Kafka streams-based classification approach for DDoS attacks

• Published in: The Journal of supercomputing Vol. 78; no. 6; pp. 8946 - 8976
• Main Authors: Patil, Nilesh Vishwasrao, Krishna, C. Rama, Kumar, Krishan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2022; Springer Nature B.V
• Subjects: Algorithms; Classification; Clusters; Compilers; Computer Science; Denial of service attacks; Distributed processing; Internet; Interpreters; Machine learning; Processor Architectures; Programming Languages; Distributed H2O machine learning algorithms; CICDDoS2019 dataset; Apache hadoop; DDoS attacks; Kafka streams; Distributed processing frameworks; Distributed streaming platform; Big data
• ISSN: 0920-8542; 1573-0484; 1573-0484
• DOI: 10.1007/s11227-021-04241-1

A distributed denial of service (DDoS) attack is the most destructive threat for internet-based systems and their resources. It stops the execution of victims by transferring large numbers of network traces. Due to this, legitimate users experience a delay while accessing internet-based systems and their resources. Even a short delay in responses leads to a massive financial loss. Numerous techniques have been proposed to protect internet-based systems from various kinds of DDoS attacks. However, the frequency and strength of attacks are increasing year-after-year. This paper proposes a novel Apache Kafka Streams-based distributed classification approach named KS-DDoS. For this classification approach, firstly, we design distributed classification models on the Hadoop cluster using highly scalable machine learning algorithms by fetching data from Hadoop distributed files system (HDFS). Secondly, we deploy an efficient distributed classification model on the Kafka Stream cluster to classify incoming network traces into nine classes in real-time. Further, this distributed classification approach stores highly discriminative features with predicted outcomes into HDFS for creating/updating models using a new set of instances. We implemented a distributed processing framework-based experimental environment to design, deploy, and validate the proposed classification approach for DDoS attacks. The results show that the proposed distributed KS-DDoS classification approach efficiently classifies incoming network traces with at least 80% classification accuracy.