# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 5 Smart Grid Attacks
### Non-Cryptography Solutions
#### 7.2.2 DDoS Detection  
DDoS attacks can flood the smart grid's network with traffic, overwhelming its capacity and causing it to crash and cause either a partial or a total power outage and/or cascading blackout, burnout, or brownout [262]. However, by implementing DDoS detection and prevention mechanisms, smart grid incident responders can quickly react by identifying the type of attack, activating the necessary countermeasures, and isolating systems and devices if necessary. This will prevent and minimize power disruptions and ensure the smart grid's resiliency and reliability. As a result, several recent solutions were presented to mitigate this threat (see Table 26), such as in [263], where [PERSON] et al. described an anomaly detection method that improves the detection rate of DDoS attacks in smart grids. This was done by increasing the training classification and testing phases in a Convolutional Neural Network (CNN), using a full version of the Variance Fractal Dimension Trajectory (VFDTv2), and Support Vector Machine (SVM), and applying Discrete Wavelet Transform (DWT). The implementation managed to detect DDoS attacks with an accuracy of 87.35%. In [209], [PERSON] et al. presented a compromise propagation model alongside a behavioral DDoS model to explore how dependencies between the grid's networks might influence the scale and impact of DDoS attacks. The results showed how these models could be considered in the future as an alternative to graph-based modeling for predicting the interconnected network vulnerabilities within smart grid critical infrastructure. In [264], [PERSON] et al. presented iCAD as an information-centric architecture, extending the iCAAP architecture they had already presented before to show its effectiveness in mitigating DoS/DDoS attacks. The simulation results demonstrated that iCAD can successfully handle DDoS traffic and meet QoS expectations of applications. In [265], [PERSON] et al. developed a situational awareness tool to detect the system's compromise by monitoring the Indicators of Compromise (IOCs) of amplification DDoS attacks and exploring attack footprints to understand their nature and cyber behavior. The tool was tested against a real dataset of DDoS attack instances and successfully distinguished and detected different types of amplification DDoS attacks. In [210], [PERSON] et al. presented a hybrid deep learning algorithm that focuses on DDoS attacks on the smart grid's communication infrastructure, using the Convolutional Neural Network (CNN) and the Gated Recurrent Unit (GRU) algorithms. The simulation results showed that the algorithm outperforms the current intrusion detection algorithms with an accuracy of 99.7%. In [266], [PERSON] et al. presented a comprehensive and systematic analysis of DoS attacks in the smart grid along with their effect while discussing the detection and mitigation techniques against DoS attacks in the smart grid using reinforcement learning (RL) algorithms. DDoS detection techniques are crucial for preserving the smart grid's control systems and communication channels' availability, particularly in high-load, attack-prone conditions. One interesting use case involves using DDoS detection at a utility's data aggregation server gateway, where real-time traffic analysis can pinpoint flooding efforts before they interfere with meter-to-grid interactions.