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

Distributed denial of service (DDoS) is an immense threat for Internet based-applications and their resources. It immediately floods the victim system by transmitting a large number of network packets, and due to this, the victim system resources become unavailable for legitimate users. Therefore, this attack is claimed to be a dangerous attack for Internet-based applications and their resources. Several security approaches have been proposed in the literature to protect Internet-based applications from this type of threat. However, the frequency and strength of DDoS attacks are increasing day-by-day. Further, most of the traditional and distributed processing frameworks-based DDoS attack detection systems analyzed network flows in offline batch processing. Hence, they failed to classify network flows in real-time. This paper proposes a novel Spark Streaming and Kafka-based distributed classification system, named by SSK-DDoS, for classifying different types of DDoS attacks and legitimate network flows. This classification approach is implemented using a distributed Spark MLlib machine learning algorithms on a Hadoop cluster and deployed on the Spark streaming platform to classify streams in real-time. The incoming streams consume by Kafka's topic to perform preprocessing tasks such as extracting and formulating features for classifying them into seven groups: Benign, DDoS-DNS, DDoS-LDAP, DDoS-MSSQL, DDoS-NetBIOS, DDoS-UDP, and DDoS-SYN. Further, the SSK-DDoS classification system stores formulated features with their predicted class into the HDFS that will help to retrain the distributed classification approach using a new set of samples. The proposed SSK-DDoS classification system has been validated using the recent CICDDoS2019 dataset. The results show that the proposed SSK-DDoS efficiently classified network flows into seven classes and stored formulated features with the predicted value of each incoming network flow into HDFS.

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

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.