A Novel Web Attack Recognition System for IOT via Ensemble Classification

Machine learning is increasingly utilized in various fields such as biomedicine, information security, firewall protection, medical science, the Internet of Things (IoT), and mobile application security. Consequently, the demand for machine learning applications is growing. In this article, we apply machine learning to detect web or Internet attacks on IoT mobile devices. With the rapid growth in smartphone usage, protecting personal information and privacy has become a significant challenge. Impersonation is a major consequence of data leaks. Current preventive methods, like passcodes and fingerprinting, cannot continuously monitor usage to verify user authorization. Typically, once a user is authorized, they gain complete control of the device, and no further protection can prevent access to device data. To address this, we propose an ensemble machine learning model for detecting impersonation, based on a multi-view bagging strategy that collects sequential tapping information from the smartphone keyboard. This model continually authenticates the user while typing using sequential-tapping biometrics. By conducting multiple tests on our model, we evaluated it using the CLaMP (Classification of Malware with Portable headers) dataset from Kaggle. Our empirical and theoretical results demonstrate that our model outperforms other approaches, achieving a 6.42% equal error rate, 93.14% accuracy, and 95.41% H-mean using only the accelerometer and five keyboard taps.