Driver Behavior Estimation at Signalized Intersections Using a Physics-Constrained Decision-Conditioned Autoregressive Transformer
本文通过分析驾驶员在交通信号灯转换时的行为，利用物理约束的决策条件自回归Transformer模型预测其决策及纵向轨迹，以减少闯红灯和急刹车导致的交通事故。
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Selected work
Representative Papers
Propagation Model for SSC attacks: Why SBOM (tools) don't tell the whole truth
研究针对SBOM工具在检测软件供应链攻击传播效果上的不足，提出一个四阶段传播模型，并通过实证评估展示了现有工具的局限性。
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An Explicit Solution to Black-Scholes Implied Volatility
This work addresses the long-standing challenge of explicitly solving for implied volatility in the Black–Scholes model by introducing the first closed-form analytical formula that requires neither iteration, approximation, nor series expansion. The approach reinterprets the option price as the survival probability of an inverse Gaussian distribution and leverages its quantile function to analytically invert the Black–Scholes framework, yielding an explicit expression for implied volatility that depends solely on observable market variables. Numerical experiments demonstrate that the method achieves machine precision and offers a computational speedup of approximately 3.4× compared to the current state-of-the-art benchmark.
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Can SOC Operators Explain their Decisions while Triaging Alarms? A Real-World Study
This study addresses a critical gap in explainability within Security Operations Center (SOC) workflows: while SOC analysts achieve high decision accuracy (83%) during alert triage, their post-hoc explanations align with the true root causes in only 39% of cases. Through a systematic literature review encompassing 257 papers and an empirical user study involving 12 SOC analysts, this work provides the first evidence of a significant disconnect between decision correctness and explanation fidelity. The findings underscore the urgent need for computational mechanisms that support accurate, causally grounded justifications for analyst decisions. By revealing this explanatory deficit, the research offers foundational insights for enhancing human–machine collaboration and bolstering trustworthiness in SOC decision-making processes.
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SoK: Reshaping Research on Network Intrusion Detection Systems
This study addresses the significant gap between academic research and real-world deployment in network intrusion detection systems (NIDS), which stems from a lack of consensus on the fundamental characteristics of NIDS and consequently leads to inconsistent evaluation benchmarks. Through a systematic survey (SoK), this work formally defines the intrinsic properties of NIDS, critically examines prevailing evaluation methodologies, and employs reproducible case studies to expose the disconnect between theoretical research and operational practice. Building on these insights, the paper proposes foundational principles and concrete recommendations for reframing NIDS research through the lens of security operations, aiming to align academic inquiry more closely with real-world requirements and provide actionable methodological guidance for future work.
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Recent publications
Latest Papers
Driver Behavior Estimation at Signalized Intersections Using a Physics-Constrained Decision-Conditioned Autoregressive Transformer
本文通过分析驾驶员在交通信号灯转换时的行为，利用物理约束的决策条件自回归Transformer模型预测其决策及纵向轨迹，以减少闯红灯和急刹车导致的交通事故。
0 citationsRead paper
Propagation Model for SSC attacks: Why SBOM (tools) don't tell the whole truth
研究针对SBOM工具在检测软件供应链攻击传播效果上的不足，提出一个四阶段传播模型，并通过实证评估展示了现有工具的局限性。
0 citationsRead paper
An Explicit Solution to Black-Scholes Implied Volatility
This work addresses the long-standing challenge of explicitly solving for implied volatility in the Black–Scholes model by introducing the first closed-form analytical formula that requires neither iteration, approximation, nor series expansion. The approach reinterprets the option price as the survival probability of an inverse Gaussian distribution and leverages its quantile function to analytically invert the Black–Scholes framework, yielding an explicit expression for implied volatility that depends solely on observable market variables. Numerical experiments demonstrate that the method achieves machine precision and offers a computational speedup of approximately 3.4× compared to the current state-of-the-art benchmark.
0 citationsRead paper
Can SOC Operators Explain their Decisions while Triaging Alarms? A Real-World Study
This study addresses a critical gap in explainability within Security Operations Center (SOC) workflows: while SOC analysts achieve high decision accuracy (83%) during alert triage, their post-hoc explanations align with the true root causes in only 39% of cases. Through a systematic literature review encompassing 257 papers and an empirical user study involving 12 SOC analysts, this work provides the first evidence of a significant disconnect between decision correctness and explanation fidelity. The findings underscore the urgent need for computational mechanisms that support accurate, causally grounded justifications for analyst decisions. By revealing this explanatory deficit, the research offers foundational insights for enhancing human–machine collaboration and bolstering trustworthiness in SOC decision-making processes.
0 citationsRead paper
SoK: Reshaping Research on Network Intrusion Detection Systems
This study addresses the significant gap between academic research and real-world deployment in network intrusion detection systems (NIDS), which stems from a lack of consensus on the fundamental characteristics of NIDS and consequently leads to inconsistent evaluation benchmarks. Through a systematic survey (SoK), this work formally defines the intrinsic properties of NIDS, critically examines prevailing evaluation methodologies, and employs reproducible case studies to expose the disconnect between theoretical research and operational practice. Building on these insights, the paper proposes foundational principles and concrete recommendations for reframing NIDS research through the lens of security operations, aiming to align academic inquiry more closely with real-world requirements and provide actionable methodological guidance for future work.
0 citationsRead paper