WAPP: Safe Learning of Positive Security WAF Policies from Live Traffic
本文提出WAPP框架，通过信任过滤、规则合成等方法从实时流量中安全地学习正向安全策略，以解决WAF对未知或变种攻击的防护不足问题。
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Representative Papers
Learning to Modulate, Not to Cycle: Soft Actor---Critic Recovers Inverter-Style Heat-Pump Control
This study addresses the issue of excessive compressor cycling in residential heat pumps, which accelerates equipment wear—a factor commonly overlooked by existing reinforcement learning controllers that focus primarily on energy consumption and thermal comfort. To bridge this gap, the work explicitly incorporates compressor wear into the reward function and evaluates Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) algorithms within the hydronic heat pump case of the BOPTEST platform. Results demonstrate that SAC autonomously learns a variable-speed continuous modulation strategy, achieving zero start-stop cycles while reducing thermal discomfort by 90.7% at the cost of only an 11.5% increase in operational expenditure, substantially outperforming conventional control approaches.
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Predicting Male Fertility Using Machine Learning: A Semen Parameters Based Analysis with the VISEM Dataset
Male infertility is often underdiagnosed due to the lack of objective assessment tools. This study systematically evaluates the performance of over forty machine learning models in classifying fertility status into three categories—fertile, subfertile, and infertile—using semen parameters (concentration, motility, and morphology) from the VISEM dataset comprising 85 subjects. Leveraging feature engineering, the LazyPredict automated modeling framework, five-fold cross-validation, and multiclass ROC-AUC analysis, the Nearest Centroid classifier emerged as the top-performing model, achieving an accuracy of 94.2%. Its performance significantly surpassed that of support vector machines and quadratic discriminant analysis, demonstrating strong potential as a clinical decision-support tool for male infertility diagnosis.
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Next-generation cyberattack detection with large language models: anomaly analysis across heterogeneous logs
This study addresses the limitations of traditional intrusion detection systems, which struggle to effectively identify cross-heterogeneous-log cyberattacks due to high false-positive rates, semantic blind spots, and log scarcity. To overcome these challenges, the authors propose a two-stage training paradigm based on large language models (LLMs): first fine-tuning the base model Base-AMAN 3B for foundational security understanding, then distilling this knowledge into a lightweight AMAN 0.5B model for real-time detection. The work introduces LogAtlas, a privacy-preserving, well-annotated log dataset series, and highlights the inadequacy of conventional evaluation metrics in security contexts, advocating instead for task-oriented assessment. The resulting system achieves per-session inference in 0.3–0.5 seconds with daily operational costs under $50, demonstrating the practical feasibility and efficiency of LLMs in real-world cybersecurity applications.
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Deadline-Aware, Energy-Efficient Control of Domestic Immersion Hot Water Heater
This study addresses the inefficiency of conventional residential immersion water heaters, which often operate continuously during winter due to neglecting predictable hot water usage windows and thermal losses. To mitigate this, the work proposes a deadline-aware control strategy that minimizes energy consumption while guaranteeing the target water temperature is reached by a specified time. It introduces, for the first time, deadline-aware reinforcement learning to water heater control, leveraging a Gymnasium-based simulation environment and a first-order thermal loss model. The approach is evaluated against Bang-Bang control, Monte Carlo Tree Search (MCTS), and Proximal Policy Optimization (PPO). Experimental results demonstrate that PPO achieves an average energy consumption of 3.23 kWh over a two-hour horizon, reducing energy use by 26%–69% compared to Bang-Bang control, and outperforming Bang-Bang and MCTS by 54% and 33%, respectively, in typical scenarios, with near-zero inference overhead.
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WAPP: Safe Learning of Positive Security WAF Policies from Live Traffic
本文提出WAPP框架，通过信任过滤、规则合成等方法从实时流量中安全地学习正向安全策略，以解决WAF对未知或变种攻击的防护不足问题。
0 citationsRead paper
Learning to Modulate, Not to Cycle: Soft Actor---Critic Recovers Inverter-Style Heat-Pump Control
This study addresses the issue of excessive compressor cycling in residential heat pumps, which accelerates equipment wear—a factor commonly overlooked by existing reinforcement learning controllers that focus primarily on energy consumption and thermal comfort. To bridge this gap, the work explicitly incorporates compressor wear into the reward function and evaluates Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) algorithms within the hydronic heat pump case of the BOPTEST platform. Results demonstrate that SAC autonomously learns a variable-speed continuous modulation strategy, achieving zero start-stop cycles while reducing thermal discomfort by 90.7% at the cost of only an 11.5% increase in operational expenditure, substantially outperforming conventional control approaches.
0 citationsRead paper
Predicting Male Fertility Using Machine Learning: A Semen Parameters Based Analysis with the VISEM Dataset
Male infertility is often underdiagnosed due to the lack of objective assessment tools. This study systematically evaluates the performance of over forty machine learning models in classifying fertility status into three categories—fertile, subfertile, and infertile—using semen parameters (concentration, motility, and morphology) from the VISEM dataset comprising 85 subjects. Leveraging feature engineering, the LazyPredict automated modeling framework, five-fold cross-validation, and multiclass ROC-AUC analysis, the Nearest Centroid classifier emerged as the top-performing model, achieving an accuracy of 94.2%. Its performance significantly surpassed that of support vector machines and quadratic discriminant analysis, demonstrating strong potential as a clinical decision-support tool for male infertility diagnosis.
0 citationsRead paper
Next-generation cyberattack detection with large language models: anomaly analysis across heterogeneous logs
This study addresses the limitations of traditional intrusion detection systems, which struggle to effectively identify cross-heterogeneous-log cyberattacks due to high false-positive rates, semantic blind spots, and log scarcity. To overcome these challenges, the authors propose a two-stage training paradigm based on large language models (LLMs): first fine-tuning the base model Base-AMAN 3B for foundational security understanding, then distilling this knowledge into a lightweight AMAN 0.5B model for real-time detection. The work introduces LogAtlas, a privacy-preserving, well-annotated log dataset series, and highlights the inadequacy of conventional evaluation metrics in security contexts, advocating instead for task-oriented assessment. The resulting system achieves per-session inference in 0.3–0.5 seconds with daily operational costs under $50, demonstrating the practical feasibility and efficiency of LLMs in real-world cybersecurity applications.
0 citationsRead paper
Deadline-Aware, Energy-Efficient Control of Domestic Immersion Hot Water Heater
This study addresses the inefficiency of conventional residential immersion water heaters, which often operate continuously during winter due to neglecting predictable hot water usage windows and thermal losses. To mitigate this, the work proposes a deadline-aware control strategy that minimizes energy consumption while guaranteeing the target water temperature is reached by a specified time. It introduces, for the first time, deadline-aware reinforcement learning to water heater control, leveraging a Gymnasium-based simulation environment and a first-order thermal loss model. The approach is evaluated against Bang-Bang control, Monte Carlo Tree Search (MCTS), and Proximal Policy Optimization (PPO). Experimental results demonstrate that PPO achieves an average energy consumption of 3.23 kWh over a two-hour horizon, reducing energy use by 26%–69% compared to Bang-Bang control, and outperforming Bang-Bang and MCTS by 54% and 33%, respectively, in typical scenarios, with near-zero inference overhead.
0 citationsRead paper