Pub Date : 2023-04-17DOI: 10.1109/SysCon53073.2023.10131249
S. Häger, Marcus Haferkamp, C. Wietfeld
The provisioning of wireless network-based sensing functionalities in the scope of 6G Joint Communication and Sensing (JCAS) is expected to be a driver for innovations and smart city-enabled services by future networks. Leveraging communication channels to acquire data on activities near network and sensing infrastructure constitutes the first step towards perceptive radio networks. Notably, the use of millimeter-wave (mmWave) frequencies is promising due to, e.g., inherent directionality and high time resolution boosting the performance of user positioning services constituting a prime example of channel-based sensing.In this work, we propose a novel 6G-aided networked sensing concept, mainly operating in mmWave beam space, and measuring along the inter-cell link, thereby enabling finer spatial sensory activity monitoring by the proposed radio sensing map (RSM) functions in the network. To evaluate the presented 6G-driven concept, we conduct vehicle detection and classification as a simulation-based case study underlining its high feasibility. Finally, we show the suitability of the enhancements offering new service potentials, such as recognizing a passing road user’s trajectory.
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Pub Date : 2023-04-17DOI: 10.1109/SysCon53073.2023.10131168
N. Sharmin, Shanto Roy, Aron Laszka, Jaime Acosta, Chris Kiekintveld
Cyber attackers often use passive reconnaissance to collect information about target networks. This technique can be used to identify systems and plan attacks, making it an increasingly challenging task for security analysts to detect. Adversaries can recover statistical information from the information collected from compromised nodes, revealing target identities such as operating systems, software and servers. A comprehensive analysis of the collected data can aid in understanding what information an adversary can deduce from this technique. With this analysis, the defender can examine the methods of inferring a target used by adversaries and model adversaries’ inference techniques and belief formation. For this purpose, we propose a model-driven decision support system (DSS) based on a Bayesian belief network (BBN) to depict adversary node-based inference techniques from passively collected data and belief formation. BBN provides a compact representation of probabilistic data and allows the formalization of adversary beliefs. We demonstrate this approach with a case study based on the passively observed operating system (OS) fingerprinting data, which is evaluated utilizing p-value significance level and compared against the model generated from local networks and predictive accuracy. We also show that our methods produce models with high predictive accuracy surpassing a sequential artificial neural network (ANN).
{"title":"Bayesian Models for Node-Based Inference Techniques","authors":"N. Sharmin, Shanto Roy, Aron Laszka, Jaime Acosta, Chris Kiekintveld","doi":"10.1109/SysCon53073.2023.10131168","DOIUrl":"https://doi.org/10.1109/SysCon53073.2023.10131168","url":null,"abstract":"Cyber attackers often use passive reconnaissance to collect information about target networks. This technique can be used to identify systems and plan attacks, making it an increasingly challenging task for security analysts to detect. Adversaries can recover statistical information from the information collected from compromised nodes, revealing target identities such as operating systems, software and servers. A comprehensive analysis of the collected data can aid in understanding what information an adversary can deduce from this technique. With this analysis, the defender can examine the methods of inferring a target used by adversaries and model adversaries’ inference techniques and belief formation. For this purpose, we propose a model-driven decision support system (DSS) based on a Bayesian belief network (BBN) to depict adversary node-based inference techniques from passively collected data and belief formation. BBN provides a compact representation of probabilistic data and allows the formalization of adversary beliefs. We demonstrate this approach with a case study based on the passively observed operating system (OS) fingerprinting data, which is evaluated utilizing p-value significance level and compared against the model generated from local networks and predictive accuracy. We also show that our methods produce models with high predictive accuracy surpassing a sequential artificial neural network (ANN).","PeriodicalId":169296,"journal":{"name":"2023 IEEE International Systems Conference (SysCon)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126803160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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