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Abstract A classical theorem of Roth states that the maximum size of a solution‐free set of a homogeneous linear equation  in is if and only if the sum of the coefficients of is 0. In this paper, we prove a Ramsey–Turán variant of Roth's theorem, with respect to a natural notion of “structured” sets introduced by Erdős and Sárközy in the 1970s. Namely, we show that the following statements are equivalent:Every solution‐free set of in with has size .There exists a non‐emptysubsetof coefficients of with zero sum. 

Intrusion detection systems (IDSes) are critical building blocks for securing Internet-of-Things (IoT) devices and networks. Advances in AI techniques are contributing to enhancing the efficiency of IDSes, but their performance typically depends on high-quality training datasets. The scarcity of such datasets is a major concern for the effective use of machine learning for IDSes in IoT networks. To address such a need, we present IoTDSCreator - a tool for the automatic generation of labeled datasets able to support various devices, connectivity technologies, and attacks. IoTDSCreator provides a user with DC-API, an API by which the user can describe a target network and an attack scenario against it. Based on the description, the framework configures the network, leveraging virtualization techniques on user-provided physical machines, performs single or multi-step attacks, and finally returns labeled datasets. Thereby, IoTDSCreator dramatically reduces the manual effort for generating labeled and diverse datasets. We release the source code of IoTDSCreator and 16 generated datasets with 193 features based on 26 types of IoT devices, 2 types of communication links, and 15 types of IoT applications. 

Internet of Things (IoT) cyber threats, exemplified by jackware and crypto mining, underscore the vulnerability of IoT devices. Due to the multi-step nature of many attacks, early detection is vital for a swift response and preventing malware propagation. However, accurately detecting early-stage attacks is challenging, as attackers employ stealthy, zero-day, or adversarial machine learning to evade detection. To enhance security, we propose ARIoTEDef, an Adversarially Robust IoT Early Defense system, which identifies early-stage infections and evolves autonomously. It models multi-stage attacks based on a cyber kill chain and maintains stage-specific detectors. When anomalies in the later action stage emerge, the system retroactively analyzes event logs using an attention-based sequence-to-sequence model to identify early infections. Then, the infection detector is updated with information about the identified infections. We have evaluated ARIoTEDef against multi-stage attacks, such as the Mirai botnet. Results show that the infection detector’s average F1 score increases from 0.31 to 0.87 after one evolution round. We have also conducted an extensive analysis of ARIoTEDef against adversarial evasion attacks. Our results show that ARIoTEDef is robust and benefits from multiple rounds of evolution.