Poster: Towards Selecting Threat Appropriate Industrial Intrusion Detection Systems
为应对工业控制系统中动态变化的威胁，提出基于威胁情报共享机制选择合适的检测器，并通过评估不同入侵检测系统在攻击情景下的表现来验证其有效性。
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Representative Papers
Can Risk-Based Alerting Mitigate Cybersecurity Alert Fatigue?
本文通过系统评估风险基础警报(RBA)方法，旨在减少安全运营中心(SOCs)面临的大量虚假警报问题，从而缓解网络安全警报疲劳。
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A Systematic Sample Size Analysis of ML-Based Path Loss Prediction for LPWAN
This study addresses the limited accuracy of path loss prediction for low-power wide-area networks such as LoRa in urban environments by systematically quantifying the impact of training sample size on machine learning model performance. Leveraging real-world LoRa deployment data augmented with LiDAR-derived terrain features and geographic coordinates, the authors employ random forest and k-nearest neighbors algorithms to model path loss, evaluating both interpolation and cross-gateway extrapolation scenarios via leave-one-gateway-out cross-validation. Results demonstrate that, with the largest training set, the models achieve a root mean square error (RMSE) as low as 6.5 dB—significantly outperforming the best baseline (9.7 dB)—though performance degrades when extrapolating to unseen gateways. This work is the first to reveal the critical relationship between training data scale and generalization capability in practical LoRa deployments.
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Do you dare to try Test-Driven Forensics? Increasing Trust in Desktop Forensics with ADARE
This study addresses the challenges posed by rapid evolution in digital forensic systems and tools, which induces drift in evidentiary behaviors and tool outputs, thereby undermining result reproducibility and trustworthiness. To mitigate this, the authors propose a test-driven forensic methodology that introduces state-transition testing for causal attribution, encoding forensic expectations as executable specifications. The approach integrates virtual machine environments with computer vision–guided GUI automation to simulate authentic user interactions and verify system state changes. An open web platform is developed to facilitate sharing and replication of experiments. The method’s efficacy is demonstrated through five case studies, including a regression analysis across 25 versions of Autopsy, which uncovered numerous undocumented, substantial changes in its reporting output.
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Quantum Prediction of Transport Dynamics in Discretized State Spaces
This work addresses the challenge of efficiently solving high-dimensional Bayesian state estimation, where the evolution of probability densities is hindered by prohibitive computational complexity. By leveraging the Fokker–Planck equation, the proposed method encodes probability densities into the amplitudes of quantum states within a discrete position-velocity space and implements the prediction step in the spectral domain via quantum Fourier transforms and phase rotations. The key innovation lies in the first exact unitary implementation of the drift term in amplitude space, complemented by a Wick-rotation-based unitary surrogate model for the diffusion term, thereby establishing a fully unitary propagation framework that overcomes the longstanding barrier of representing nonlinear diffusion in quantum amplitudes. Numerical experiments demonstrate excellent agreement with exact solutions of the Fokker–Planck equation and reveal exponential scalability in state dimensionality, substantially outperforming classical tensor decomposition approaches.
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Poster: Towards Selecting Threat Appropriate Industrial Intrusion Detection Systems
为应对工业控制系统中动态变化的威胁，提出基于威胁情报共享机制选择合适的检测器，并通过评估不同入侵检测系统在攻击情景下的表现来验证其有效性。
0 citationsRead paper
Can Risk-Based Alerting Mitigate Cybersecurity Alert Fatigue?
本文通过系统评估风险基础警报(RBA)方法，旨在减少安全运营中心(SOCs)面临的大量虚假警报问题，从而缓解网络安全警报疲劳。
0 citationsRead paper
A Systematic Sample Size Analysis of ML-Based Path Loss Prediction for LPWAN
This study addresses the limited accuracy of path loss prediction for low-power wide-area networks such as LoRa in urban environments by systematically quantifying the impact of training sample size on machine learning model performance. Leveraging real-world LoRa deployment data augmented with LiDAR-derived terrain features and geographic coordinates, the authors employ random forest and k-nearest neighbors algorithms to model path loss, evaluating both interpolation and cross-gateway extrapolation scenarios via leave-one-gateway-out cross-validation. Results demonstrate that, with the largest training set, the models achieve a root mean square error (RMSE) as low as 6.5 dB—significantly outperforming the best baseline (9.7 dB)—though performance degrades when extrapolating to unseen gateways. This work is the first to reveal the critical relationship between training data scale and generalization capability in practical LoRa deployments.
0 citationsRead paper
Do you dare to try Test-Driven Forensics? Increasing Trust in Desktop Forensics with ADARE
This study addresses the challenges posed by rapid evolution in digital forensic systems and tools, which induces drift in evidentiary behaviors and tool outputs, thereby undermining result reproducibility and trustworthiness. To mitigate this, the authors propose a test-driven forensic methodology that introduces state-transition testing for causal attribution, encoding forensic expectations as executable specifications. The approach integrates virtual machine environments with computer vision–guided GUI automation to simulate authentic user interactions and verify system state changes. An open web platform is developed to facilitate sharing and replication of experiments. The method’s efficacy is demonstrated through five case studies, including a regression analysis across 25 versions of Autopsy, which uncovered numerous undocumented, substantial changes in its reporting output.
0 citationsRead paper
Quantum Prediction of Transport Dynamics in Discretized State Spaces
This work addresses the challenge of efficiently solving high-dimensional Bayesian state estimation, where the evolution of probability densities is hindered by prohibitive computational complexity. By leveraging the Fokker–Planck equation, the proposed method encodes probability densities into the amplitudes of quantum states within a discrete position-velocity space and implements the prediction step in the spectral domain via quantum Fourier transforms and phase rotations. The key innovation lies in the first exact unitary implementation of the drift term in amplitude space, complemented by a Wick-rotation-based unitary surrogate model for the diffusion term, thereby establishing a fully unitary propagation framework that overcomes the longstanding barrier of representing nonlinear diffusion in quantum amplitudes. Numerical experiments demonstrate excellent agreement with exact solutions of the Fokker–Planck equation and reveal exponential scalability in state dimensionality, substantially outperforming classical tensor decomposition approaches.
0 citationsRead paper