The advent of the smart grid promises to usher in an era that will bring intelligence, efficiency, and optimality to the power grid. Most of these changes will occur as an Internet-like communications network is superimposed on top of the current power grid using wireless mesh network technologies with the 802.15.4, 802.11, and WiMAX standards. Each of these will expose the power grid to cybersecurity threats. In order to address this issue, this work proposes a distributed intrusion detection system for smart grids (SGDIDS) by developing and deploying an intelligent module, the analyzing module (AM), in multiple layers of the smart grid. Multiple AMs will be embedded at each level of the smart grid-the home area networks (HANs), neighborhood area networks (NANs), and wide area networks (WANs)-where they will use the support vector machine (SVM) and artificial immune system (AIS) to detect and classify malicious data and possible cyberattacks. AMs at each level are trained using data that is relevant to their level and will also be able to communicate in order to improve detection. Simulation results demonstrate that this is a promising methodology for supporting the optimal communication routing and improving system security through the identification of malicious network traffic.