EIoT-DDoS: embedded classification approach for IoT traffic-based DDoS attacks

• Published in: Cluster computing Vol. 27; no. 2; pp. 1471 - 1490
• Main Authors: Shukla, Praveen, Krishna, C. Rama, Patil, Nilesh Vishwasrao
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2024; Springer Nature B.V
• Subjects: Accuracy; Artificial intelligence; Classification; Communications traffic; Computer Communication Networks; Computer Science; Cybersecurity; Datasets; Decision trees; Deep learning; Denial of service attacks; Health care; Internet of Things; Machine learning; Neural networks; Operating Systems; Performance evaluation; Processor Architectures; United States > US; Embedded feature reduction; Machine learning techniques; IoT devices; Bot-IoT dataset; DDoS attacks; Internet of Things
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-023-04027-5

The Internet of Things (IoT) has shown incredible adaptability in recent years and has become an integral part of human life. The proliferation of IoT technology has made IoT devices more prone to severe security threats, such as Distributed Denial of Service (DDoS) attacks, which are dangerous threats to public systems and networks. Further, the frequency and complexity of IoT traffic-based DDoS attacks are increasing year-by-year. This article proposes an IoT traffic-based DDoS attack detection approach for classifying incoming IoT network traffic into 11 classes using multiclass machine learning techniques. The proposed approach comprises two phases: (i) designing and (ii) detection. In the designing phase, we employ the embedded feature reduction technique to create cost-effective and efficient classification models with a high feature reduction rate. Further, we evaluate these models using the K-fold cross-validation technique. While in the detection phase, we evaluate the performance of an efficient model by executing four different IoT traffic-based scenarios. A publicly available Bot-IoT dataset is employed to design and validate the proposed multiclass classification approach. The results show that the proposed approach provides an 84.4% feature reduction rate and approximately 5.19% higher classification accuracy than the existing approaches.