Lightweight Botnet Detection for IoT: Autoencoder-Based Approach Using Packet Length

The growing prevalence of botnet attacks poses a significant threat, particularly as the demand for IoT devices continues to rise. This underscores the need for effective botnet detection techniques tailored for IoT environments. Machine Learning and Deep Learning are often used to tackle this problem with either one showing there are trade-offs between detection and computational performances. To address these challenges, this paper investigates the use of packet length as a key feature for botnet detection, leveraging an autoencoder model. This approach involves feature engineering of packet length and employs an autoencoder to identify anomalous network behavior, making it an efficient and effective solution for detecting Mirai botnet. This method ensures a lightweight detection framework, making it suitable for deployment on resource-constrained IoT devices while maintaining high detection with Precision at 99.64% and F1 Scores at 98,17% respectively tested on the MedBiot dataset.