An Anomaly Detection Method for Wireless Sensor Networks Based on the Improved Isolation Forest

With the continuous development of technologies such as the Internet of Things (IoT) and cloud computing, sensors collect and store large amounts of sensory data, realizing real-time recording and perception of the environment. Due to the open characteristics of WSN, the security risks during information transmission are prominent, and network attack or intrusion is likely to occur. Therefore, effective anomaly detection is vital for IoT systems to keep the system safe. The original Isolation Forest algorithm is an anomaly detection algorithm with linear time complexity and has a better detection effect on perceptual data. However, there are also disadvantages such as strong randomness, low generalization performance, and insufficient stability. This paper proposes a data anomaly detection method named BS-iForest (box plot-sampled iForest) for wireless sensor networks based on a variant of Isolation Forest to address the problems. This method first uses the sub-dataset filtered by the box graph to train and construct trees. Then, isolation trees with higher accuracy are selected in the training set to form a base forest anomaly detector. Next, the base forest anomaly detector uses anomaly detection to judge data outliers during the next period. These experiments were performed on datasets collected from sensors deployed in a data center of a university, and the Breast Wisconsin (BreastW) dataset, showing the performance of the variant of the Isolation Forest algorithm. Compared with the traditional isolation forest, the area under the curve (AUC) increased by 1.5% and 7.7%, which verified that the proposed method outperforms the standard Isolation Forest algorithm with the two datasets we chose.