The Internet of Things field has created many challenges for network architectures. Ensuring cyberspace security is the primary goal of intrusion detection systems (IDSs). Due to the increases in the number and types of attacks, researchers have sought to improve intrusion detection systems by efficiently protecting the data and devices connected in cyberspace. IDS performance is essentially tied to the amount of data, data dimensionality, and security features. This paper proposes a novel IDS model to improve computational complexity by providing accurate detection in less processing time than other related works. The Gini index method is used to compute the impurity of the security features and refine the selection process. A balanced communication-avoiding support vector machine decision tree method is performed to enhance intrusion detection accuracy. The evaluation is conducted using the UNSW-NB 15 dataset, which is a real dataset and is available publicly. The proposed model achieves high attack detection performance, with an accuracy of approximately 98.5%.