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A Deep Generative Model for Synthesizing Labeled Wireless Signals
Researchers propose IIns-GAN, a GAN that synthesizes realistic labeled ultra-wideband wireless signals, cutting dataset costs for wireless sensing training.
The paper introduces Inter-Instance Generative Adversarial Networks (IIns-GAN), a deep generative method that synthesizes realistic wireless signals with position-related labels to avoid costly real-world measurement and labeling. Unlike environment-model-based synthesis, the generated signals adapt to different environment scenarios and support training tasks such as distance estimation and environment identification. Experiments on public Ultra-Wideband (UWB) datasets show the synthetic signals closely mirror real measurements and improve model training performance.
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Home & Small Office Wireless Routers Exploited to Attack Gaming Servers
Unit 42 details an updated Gafgyt botnet variant exploiting Zyxel, Huawei, and Realtek router vulnerabilities to recruit devices for DoS attacks on gaming servers.
Unit 42 researchers identified an updated Gafgyt variant derived from the JenX botnet that combines three remote code execution exploits: CVE-2017-18368 (Zyxel P660HN-T1A), CVE-2017-17215 (Huawei HG532), and CVE-2014-8361 (Realtek RTL81XX chipset). Shodan scans show more than 32,000 wireless routers worldwide potentially vulnerable to these exploits. The exploits act as droppers, pulling architecture-specific binaries from a malicious server (185.172.110.224), and the botnet performs denial-of-service attacks against gaming servers, most notably Valve Source engine servers.
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