Network Intrusion Detection Systems (NIDSes) are crucial for securing various networks from malicious attacks. Recent developments in Deep Neural Networks (DNNs) have encouraged researchers to incorporate DNNs as the underlying detection engine for NIDS. However, DNNs are susceptible to adversarial attacks, where subtle modifications to input data result in misclassification, posing a significant threat to security-sensitive domains such as NIDS. Existing efforts in adversarial defenses predominantly focus on supervised classification tasks in Computer Vision, differing substantially from the unsupervised outlier detection tasks in NIDS. To bridge this gap, we introduce a novel method of generalized adversarial robustness and present NIDS-Vis, an innovative black-box algorithm that traverses the decision boundary of DNN-based NIDSes near given inputs. Through NIDS-Vis, we can visualize the geometry of the decision boundaries and examine their impact on performance and adversarial robustness. Our experiment uncovers a tradeoff between performance and robustness, and we propose two novel training techniques, feature space partition and distributional loss function, to enhance the generalized adversarial robustness of DNN-based NIDSes without significantly compromising performance.

Autonomous and connected vehicles are rapidly evolving, integrating numerous technologies and software. This progress, however, has made them appealing targets for cybersecurity attacks. As the risk of cyber threats escalates with this advancement, the focus is shifting from solely preventing these attacks to also mitigating their impact. Current solutions rely on vehicle security operation centers, where attack information is analyzed before deciding on a response strategy. However, this process can be time-consuming and faces scalability challenges, along with other issues stemming from vehicle connectivity. This paper proposes a dynamic intrusion response system integrated within the vehicle. This system enables the vehicle to respond to a variety of incidents almost instantly, thereby reducing the need for interaction with the vehicle security operation center. The system offers a comprehensive list of potential responses, a methodology for response evaluation, and various response selection methods. The proposed solution was implemented on an embedded platform. Two distinct cyberattack use cases served as the basis for evaluating the system. The evaluation highlights the system’s adaptability, its ability to respond swiftly, its minimal memory footprint, and its capacity for dynamic system parameter adjustments. The proposed solution underscores the necessity and feasibility of incorporating dynamic response mechanisms in smart vehicles. This is a crucial factor in ensuring the safety and resilience of future smart mobility.

In recent years, smart grid-based Electric Vehicle (EV) charging systems have increasingly faced vulnerabilities to Distributed Denial of Service (DDoS) attacks, especially through malicious authentication failures. These attacks typically involve monopolizing the Grid Server (GS), thereby hindering the authentication process for legitimate EVs. Despite the severity of this issue, no research (to the best of our knowledge) has focused on detecting DDoS attacks exploiting weaknesses in EV authentication. This study introduces a DDoS attack detection model specifically designed for EV authentication. The approach involves developing a machine learning model involving unique feature selection and combination. The proposed approach has been evaluated using a new DDOS attack dataset. The model is engineered to optimize feature combination, aiming for high sampling resolution, minimal information loss, and robust performance under 16 distinct attack scenarios. The feature combination used in this study shows improved accuracy over traditional DDoS detection methods based on access time variation while minimizing information loss.