The data described herein pertains to the Robotic Systems Security domain. This data in brief presents the attributes of the ROSIDS23 dataset and its collection process in detail. This dataset comprises Robot Operating System (ROS)-based cyber-attacks to address the emerging need in high fidelity data for robotic system security research. The data was gathered from the IFARLab-DIH environment. IFARLab-DIH is a robotic and factory-level laboratory that includes a ROS-based network and is used to conduct studies up to TRL 5 on robotic systems. ROSIDS23 dataset contains benign and various attack traffic collected from the ROS middleware using the tcpdump network protocol analyser. Then the eighty-two traffic features were extracted from the captured pcap files and converted into CSV format using the CICFlowMeter tool. This dataset can serve as a valuable resource for developing and improving security countermeasures in robotic systems and can help the evolution of resilient robotics infrastructure.

Robotic systems are widely used in industry, agriculture, the inspection of infrastructure, and even in our daily lives. The safety and security of robotic systems have become a primary concern as their interaction with humans increases. In this context, attacks on robotic systems have increased for diversified field applications. It is necessary to accurately detect these abnormal events in these systems as soon as possible. However, these systems also need a runtime verification approach on whether they conform to the established specifications. In this study, runtime verification for anomaly detection methods is proposed for the security of the robot operating system (ROS). Firstly, an anomaly detection method is proposed to detect unexpected situations, such as the number of the received packages being decreased under DoS attacks. Then, a holistic runtime verification architecture is proposed for the anomaly detection method. This architecture consists of three major entities: a verification device, an attacker device, and a robotic platform without losing generality. In the verification device, ROSMonitoring and Oracle are used to implement runtime verification. The proposed architecture is verified through an experimental setup. It is shown that the architecture can be used for runtime verification of different anomaly detection algorithms. A discussion on the security of robotic systems is also presented.

The adoption of deep learning has exposed significant vulnerabilities, especially to adversarial attacks that cause misclassifications through subtle small perturbations. Such attacks challenge security-critical applications. This study addresses these vulnerabilities by proposing a novel adversarial attack detection method leveraging data reconstruction errors. We evaluate this approach against three well-known adversarial attacks—Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Basic Iterative Method (BIM)—on Intrusion Detection Systems. Our method combines reconstruction error alongside aleatoric, epistemic, and entropy metrics to distinguish between original and adversarial samples. Experimental results show that our approach achieves a detection success rate of 92% to 100%, outperforming existing methods, particularly at low perturbation levels. This research enhances the robustness and reliability of machine learning models against adversarial threats by using effective error metrics in adversarial detection.

The emergence of robotic technologies has made a significant contribution in industry. Robot Operating System (ROS) is becoming a standard framework for industrial systems uses as a middleware system with many versions. However, the initial design of ROS does not include cyber-security concepts. The intense interest in robot systems, the security concerns and vulnerabilities of these systems have started to attract the attention of attackers. One of these attacks is DoS attack that targeting system availability by slowing down or crashing a service rather than obtaining the information or system. In this study, the impact of DoS attack has been analyzed in various scenarios for both in application and transport layer of the ROS middleware.In the experiments four different volume of DoS attacks are performed in five different experiment scenarios on ROS. To understand the impact of DoS attack, network traffics are monitored using Tshark. The resulting effects measured with some Quality of Service parameters that are delay and packet loss.

The increasing connectivity of smart systems opens up new security vulnerabilities. Since smart systems are used in various sectors such as healthcare, smart cities, and the intelligent industries, security becomes a fundamental concern. For this reason, security measurements are often added to security systems, such as authorization, authentication, encryption, Intrusion Prevention System (IPS) and Intrusion Detection Systems (IDS). However, these systems are still vulnerable to attacks such as DoS, DDoS. Recently, Deep Learning methods have been used to detect these attacks as early as possible. This method, however, has its own vulnerabilities to adversarial attacks. This paper presents three types of adversarial effects on the deep learning models analysis. In this study we use the open dataset CICIDS2017. Initially, DDoS attacks are detected using deep learning methods. Then, adversarial examples are created using adversarial attack methods. The untargeted adversarial attacks are used in this study, these being; FGSM, PGD and BIM. The results show that all adversarial attacks are effective on Deep Learning models. However, the results show that attacks from PGD and BIM are more effective. In addition, the deep learning models training is evaluated with K-fold cross-validation. The results show that, deep learning models that occur during each fold could be get different accuracy results against adversarial attacks. Adversarial attacks may be used in a K-fold cross validation process as a parameter for best model selection.