To satisfy the growing need of wireless sensor networks in areas of defence, Internet of Things, health care, environmental monitoring, and so on., IETF has proposed a new Routing Protocol for Low power and Lossy networks which works with IPv6. The nodes of these network are placed in vulnerable environments and critical,sensitive information is transferred between them based on the application. Hence, the security of such a network is very important. Intrusion Detection System plays an important role in providing security to such types of networks, which is computationally costly owing to the limited resources of sensor nodes. By considering the capabilities of wireless sensor networks, an intrusion detection model is designed using Logistic Regression, Gaussian Naive Bayes, Artificial Neural Networks, Support Vector Machine and Random Forest and analyzed on IEEE-IoT-IDS, WSN-DS, and simulated data. Based on the analysis, suitable machine learning algorithm is selected for rule generation. Later, multiple attacks are identified using Rule Based Approach (RBA). For the efficient utilization of the sensor node s energy, the rule based algorithm executes at the base station. Experimental results show that the proposed method gives good results in the identification of multiple intrusions.