Gradient Boosting Models for Cybersecurity Threat Detection with Aggregated Time Series Features

The rapid proliferation of Internet of Things (IoT) devices has revolutionized the way we interact with and manage our surroundings. However, this widespread adoption has also brought forth significant cybersecurity challenges. IoT devices, with their interconnectedness and varying functionalities, present a unique threat landscape that requires tailored detection techniques. Traditional approaches to cybersecurity, primarily focused on network monitoring and anomaly detection, often fall short in effectively identifying threats originating from IoT devices due to their dynamic and complex behaviors. This paper addresses our solution for FedCSIS 2023 Challenge: Cybersecurity Threat Detection in the behavior of IoT Devices. First, we aggregated time series features, and then at the feature selection stage, we filtered and combined different categorical and numerical features to generate four different feature sets. The Gradient boosting models, i.e. lightgbm, catboost and xgboost, are applied and trained individually with hyper-parameter tuning. The final three submissions are two best individual lightgbm models with the AUC scores of 0.9999 and 0.9998, respectively on the different feature sets, which secured the 4th place with a final score of 0.9993, and one ensemble result with a AUC score of 0.9998 from combination of xgboost, catboost and lightgbm, which has the final score of 0.9997 while unluckily was missing in the final three evaluation entries.