An approach to on-stream DDoS blitz detection using machine learning algorithms

Manjula, H.T.; Mangla, Neha

doi:10.1016/j.matpr.2021.07.280

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…Through this study, the author wants to provide an alternative method to detect DDoS attacks. Machine learning (ML)-based methods, which are part of AI technology, have proven to be quite relevant in detecting DDoS attacks, as evidenced by the results of several studies conducted by Thorat, Parekh, and Mangrulkar [9], Manjula and Neha Mangla [10]. Based on previous related studies, the authors propose the detection of DDoS attacks based on feature importance [11], and the Support Vector Machine (SVM) algorithm [12][13].…”

Section: Introductionmentioning

confidence: 99%

DDoS Attacks Detection Method Using Feature Importance and Support Vector Machine

Sanmorino

Gustriansyah

Alie

2022

JUITA

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In this study, the author wants to prove the combination of feature importance and support vector machine relevant to detecting distributed denial-of-service attacks. A distributed denial-of-service attack is a very dangerous type of attack because it causes enormous losses to the victim server. The study begins with determining network traffic features, followed by collecting datasets. The author uses 1000 randomly selected network traffic datasets for the purposes of feature selection and modeling. In the next stage, feature importance is used to select relevant features as modeling inputs based on support vector machine algorithms. The modeling results were evaluated using a confusion matrix table. Based on the evaluation using the confusion matrix, the score for the recall is 93 percent, precision is 95 percent, and accuracy is 92 percent. The author also compares the proposed method to several other methods. The comparison results show the performance of the proposed method is at a fairly good level in detecting distributed denial-of-service attacks. We realized this result was influenced by many factors, so further studies are needed in the future.

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Section: Introductionmentioning

confidence: 99%

DDoS Attacks Detection Method Using Feature Importance and Support Vector Machine

Sanmorino

Gustriansyah

Alie

2022

JUITA

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Kafka‐Shield: Kafka Streams‐based distributed detection scheme for IoT traffic‐based DDoS attacks

Shukla,

Krishna,

Patil

2024

Security and Privacy

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With the rapid proliferation of insecure Internet of Things (IoT) devices, the security of Internet‐based applications and networks has become a prominent concern. One of the most significant security threats encountered in IoT environments is a Distributed Denial of Service (DDoS) attack. This attack can severely disrupt critical services and prevent smart devices from functioning normally, leading to severe consequences for businesses and individuals. It aims to overwhelm victims' resources, websites, and other services by flooding them with massive attack packets, making them inaccessible to legitimate users. Researchers have developed multiple detection schemes to detect DDoS attacks. As technology advances and other facilitating factors have increased, it is a challenge to identify such powerful attacks in real‐time. In this paper, we propose a novel distributed detection scheme for IoT network traffic‐based DDoS attacks by deploying it in a Kafka Streams processing framework named Kafka‐Shield. The Kafka‐Shield comprises two stages: design and deployment. Firstly, the detection scheme is designed on the Hadoop cluster employing a highly scalable H2O.ai machine learning platform. Secondly, a portable, scalable, and distributed detection scheme is deployed on the Kafka Streams processing framework. To analyze the incoming traffic data and categorize it into nine target classes in real time. Additionally, Kafka‐Shield stores each network flow with significant input features and the predicted outcome in the Hadoop Distributed File System (HDFS). It enables the development of new models or updating current ones. To validate the effectiveness of the Kafka‐Shield, we performed critical analysis using various configured attack scenarios. The experimental results affirm Kafka‐Shield's remarkable efficiency in detecting DDoS attacks. It has a detection rate of over 99% and can process 0.928 million traces in nearly 3.027 s.

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SDDA-IoT: storm-based distributed detection approach for IoT network traffic-based DDoS attacks

Shukla,

Krishna,

Patil

2024

Cluster Comput

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An approach to on-stream DDoS blitz detection using machine learning algorithms

Cited by 5 publications

References 9 publications

DDoS Attacks Detection Method Using Feature Importance and Support Vector Machine

DDoS Attacks Detection Method Using Feature Importance and Support Vector Machine

Kafka‐Shield: Kafka Streams‐based distributed detection scheme for IoT traffic‐based DDoS attacks

SDDA-IoT: storm-based distributed detection approach for IoT network traffic-based DDoS attacks

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