Botnets are groups of compromised computers that botmasters (botherders) use to launch attacks over the Internet. To avoid detection, botnets use DNS fast flux to change the mapping between IP addresses and domain names periodically. Domain generation algorithms (DGAs) are employed to generate a large number of domain names. Detection techniques have been proposed to identify malicious domain names generated by DGAs. Three metrics, Kullback-Leibler (KL) distance, Edit distance (ED), and Jaccard index (JI), are used to detect botnet domains with up to 100% detection rate and 2.5% false-positive rate. In this paper, we propose two DGAs that use hidden Markov models (HMMs) and probabilistic context-free grammars (PCFGs), respectively. Experiment results show that DGA detection metrics (KL, JI, and ED) and detection systems (BotDigger and Pleiades) have difficulty detecting domain names generated using the proposed approaches. Game theory is used to optimize strategies for both botmasters and security personnel. Results show that, to optimize DGA detection, security personnel should use the ED detection technique with probability 0.78 and JI detection with probability 0.22, and botmasters should choose the HMM-based DGA with probability 0.67 and PCFG-based DGA with probability 0.33.

Distributed denial of service (DDoS) attacks remain a persistent nuisance on the Internet. They exploit the fact that the Internet lacks centralized access control. Since this vulnerability was a core design decision of the early Internet, DDoS attacks have persisted. This article presents the technologies and tools that are used in DDoS, followed by a timeline of the major DDoS incidents. This is followed by a discussion of the primary classes of DDoS incidents and how the computing ecosystem enables DDoS. Early attacks were related to hacker culture, but their focus quickly changed to commercial exploitation. There have also been a number of political uses of DDoS, including cyberwar, hacktivism, and terrorism.

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.

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 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.