IoT attack prediction using big Bot-IoT data. (19th July 2022)

• Record Type: Journal Article
• Authors: Leevy, Joffrey L.; Khoshgoftaar, Taghi M.; Hancock, John
• Is Part Of: International journal of internet of things and cyber-assurance. Volume 2:Number 1(2022)
• Page Start: 45
• Page End: 61
• Publication Date: 2022-07-19
• Subjects: Bot-IoT -- decision tree -- easy-to-learn -- intrusion detection -- IoT -- machine learning -- denial-of-service -- DoS -- distributed denial-of-service -- DDoS -- information theft -- reconnaissance
• Languages: English
• ISSNs: 2059-7975

Abstract

Bot-IoT is a recent and publicly available dataset that depicts attack traffic launched by BotNets against internet of things (IoT) networks. Normal (non-attack) traffic is represented by over 9, 000 of the approximately 73, 000, 000 instances of big data that constitute this dataset. We present an easy-to-learn Bot-IoT approach, centred on the use of a minimum number of dataset features and a simple machine learning algorithm. Our contribution is defined by decision tree models built from derived Bot-IoT datasets with no more than three features. As per our definition of easy-to-learn, we require that predictive models have area under the receiver operating characteristic curve (AUC) mean scores greater than 0.99. According to our results, all the derived datasets produce easy-to-learn models. To the best of our knowledge, this work, in terms of its simplicity, interpretability, and performance, is an improvement over Bot-IoT classification approaches in existing literature.