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SSK-DDoS: distributed stream processing framework based classification system for DDoS attacks.

Nilesh Vishwasrao Patil1, C Rama Krishna1, Krishan Kumar2

  • 1Computer Science & Engineering, National Institute of Technical Teachers Training & Research, Chandigarh, Panjab University, Chandigarh, India.

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Summary
This summary is machine-generated.

A new system, SSK-DDoS, uses Spark Streaming and Kafka for real-time detection of distributed denial of service (DDoS) attacks. This approach effectively classifies network flows, enhancing internet security against evolving threats.

Keywords:
Apache HadoopApache KafkaApache SparkBig dataDDoS attacksDistributed stream processing frameworksSpark MLlib machine learning

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Network Security

Background:

  • Distributed Denial of Service (DDoS) attacks pose a significant threat to internet-based applications, causing resource unavailability for legitimate users.
  • Existing DDoS detection systems often rely on offline batch processing, failing to provide real-time classification of network flows.
  • The increasing frequency and sophistication of DDoS attacks necessitate advanced, real-time detection mechanisms.

Purpose of the Study:

  • To propose and implement a novel distributed classification system, SSK-DDoS, for real-time identification of various DDoS attack types and benign network traffic.
  • To leverage Apache Spark Streaming and Apache Kafka for high-throughput, low-latency stream processing and feature extraction.
  • To enhance the adaptability of the detection system through the storage of classified features for future retraining.

Main Methods:

  • Developed a distributed system (SSK-DDoS) integrating Spark Streaming and Kafka on a Hadoop cluster.
  • Utilized Spark MLlib machine learning algorithms for distributed classification of network flows.
  • Implemented real-time stream processing, feature extraction, and classification into seven categories: Benign, DDoS-DNS, DDoS-LDAP, DDoS-MSSQL, DDoS-NetBIOS, DDoS-UDP, and DDoS-SYN.
  • Stored classified features and predictions in the Hadoop Distributed File System (HDFS) for model retraining.

Main Results:

  • The SSK-DDoS system demonstrated efficient real-time classification of network flows into the specified seven categories.
  • Formulated features and their predicted classes were successfully stored in HDFS, enabling continuous learning.
  • Validation using the CICDDoS2019 dataset confirmed the system's effectiveness in classifying diverse network traffic patterns.

Conclusions:

  • The proposed SSK-DDoS system offers an effective solution for real-time distributed denial of service attack detection.
  • The integration of Spark Streaming, Kafka, and machine learning provides a scalable and efficient framework for network security.
  • The system's ability to store data for retraining ensures its long-term relevance against evolving cyber threats.