Anomaly Detection in IoT Network by using Multi-class Adaptive Boosting Classifier

Özet

Detection of anomaly and attack identification is some of the major concerns in IoT domain in recent days. With the exponential use of IoT based infrastructure in every domain, threats and anomalies are amplifying adequately. Attacks such as malicious operations, spying, service denial, etc. are the main cause for failure in IoT system. To solve such an important problem, it is highly desirable to develop some intelligent computing-based approaches with better security conventions for protecting the system. With the combination of several models, ensemble learning helps to enhance the performance of machine learning methods. As compared to any single method, the ensemble learning-based models are highly predictable for large dimensional data. In this paper, an adaptive boosting based model has been proposed to identify the anomaly in IoT based environment. The performance of the proposed method is compared with several other competitive machine learning-based methods and found to be superior with all the considered metrics.