Participant-Quantity-Aware Online Task Allocation in Mobile Crowdsensing

Yang, Guisong; Guo, Dongsheng; Wang, Buye; He, Xingyu; Wang, Jiangtao; Wang, Gang

doi:10.1109/jiot.2023.3305034

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…It features a Web crawler for firmware discovery, emulation capabilities, and dynamic analytics [14]. ThingPot was initially implemented to simulate an IoT platform with all supported application-level protocols, including, e.g., a Philips Hue smart lighting system [15], and was later extended with supervised machine learning (ML) for adaptive capabilities and distributed denial of service (DDoS) detection [16]. IoTCandyJar combines low and high interaction capabilities, exploiting ML with a Markov decision process for the discovery of the most suitable IoT device behaviors to extend attack sessions [17].…”

Section: State Of the Artmentioning

confidence: 99%

Designing and Evaluating a Flexible and Scalable HTTP Honeypot Platform: Architecture, Implementation, and Applications

Rabzelj,

Južnič,

Volk

et al. 2023

Electronics

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Digitalization of our economy and society has ushered in notable productivity increases but has also exposed more of our infrastructures and systems to cyberattacks. This trend is exacerbated by the proliferation of poorly designed Internet of Things (IoT) devices and cloud services, which often lack appropriate security measures, either due to bugs or configuration mistakes. In this article, we propose, validate, and critically evaluate a flexible honeypot system based on the Hypertext Transfer Protocol (HTTP) that can mimic any HTTP-based service and application. This covers a large share of IoT devices, including black box devices with no software or firmware available for emulation, as well as cloud- and web-based services. We validate the system by implementing 14 services and by running a 4-month experiment, collecting data from attackers. We propose a novel data enrichment mechanism for identifying internet scanning services, as well as several other data collection and enrichment approaches. Finally, we present some results and visualizations of the data collection experiment, demonstrating possible applications and future use cases, as well as potential drawbacks of such systems.

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Section: State Of the Artmentioning

confidence: 99%