A Data Enhancement Algorithm for DDoS Attacks Using IoT

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 17; p. 7496
• Main Authors: Lv, Haibin, Du, Yanhui, Zhou, Xing, Ni, Wenkai, Ma, Xingbang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 29.08.2023; MDPI
• Subjects: Accuracy; Algorithms; Classification; Clustering; Cyberterrorism; Datasets; Denial of service attacks; imbalanced classification; Internet of Things; Intrusion detection systems; Methods; Normal distribution; oversampling
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23177496

With the rapid development of the Internet of Things (IoT), the frequency of attackers using botnets to control IoT devices in order to perform distributed denial-of-service attacks (DDoS) and other cyber attacks on the internet has significantly increased. In the actual attack process, the small percentage of attack packets in IoT leads to low accuracy of intrusion detection. Based on this problem, the paper proposes an oversampling algorithm, KG-SMOTE, based on Gaussian distribution and K-means clustering, which inserts synthetic samples through Gaussian probability distribution, extends the clustering nodes in minority class samples in the same proportion, increases the density of minority class samples, and improves the amount of minority class sample data in order to provide data support for IoT-based DDoS attack detection. Experiments show that the balanced dataset generated by this method effectively improves the intrusion detection accuracy in each category and effectively solves the data imbalance problem.