Selected work

Representative Papers

Semantic-Aware Predictive Mapping for Exploration and Navigation

Oct 07, 2026

This study addresses the ambiguity in exploratory navigation caused by purely geometric predictive maps, which struggle to distinguish semantically similar structures such as doors and walls. To overcome this limitation, this work proposes the first integration of an independent door channel into a predictive model, introducing semantic door cues to optimize predictive occupancy mapping. A dual-channel neural network is constructed based on the CogniPlan dataset and evaluated using a multidimensional assessment framework comprising L1 loss, F1 score, and IoU metrics. The proposed approach effectively enhances map completion in ambiguous regions and significantly improves local geometric inference accuracy. Experimental results demonstrate that the L1 error in door regions is reduced to 0.000025, with both F1 score and IoU reaching 1.0, substantially outperforming baseline models.

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Explaining Intrusion Alert Decisions of Deep Learning-based Network Intrusion Detection Systems for Security Analysts

Jul 13, 2026

This work addresses the limited interpretability of alerts generated by existing deep learning–based network intrusion detection systems (NIDS), which hinders effective analyst-driven triage in practice. To bridge this gap, the authors propose EXP-SEC, a novel framework that incorporates a forensic module to pinpoint suspicious traffic and introduces a fine-grained explanation mechanism capable of handling feature overlap and group-wise dependencies. By leveraging a multi-stage mapping strategy, EXP-SEC translates model predictions into semantically meaningful alerts aligned with the domain knowledge of security operations centers. This framework is the first to deliver domain-aligned explanations tailored for security analysts, significantly outperforming xNIDS in group-level and overlap-aware explanatory utility while maintaining comparable performance in accuracy, sparsity, and stability. The resulting explanations are more intuitive and actionable for human analysts.

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HilEnT: Hilbert, Entropy Transformed Image Based Malware Detection

Jul 06, 2026

This study addresses the growing threat of malware by proposing HilEnT, a novel binary-to-image conversion method that maps malware binaries into three-channel color images through Hilbert curve mapping integrated with local entropy features. By combining deep learning with few-shot learning strategies, the approach significantly enhances malware detection and classification performance across four public datasets. Experimental results demonstrate that HilEnT achieves high accuracy and robustness in both binary and multi-class classification tasks, particularly excelling in low-data regimes. The method effectively supports few-shot malware recognition, offering a promising solution for identifying malicious software under data-scarce conditions.

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TIER: Trajectory-Invariant Explanation Regularization for Membership Privacy

Jul 02, 2026

This work addresses the privacy risks posed by explanation interfaces, which can leak membership information and are inadequately mitigated by existing defenses against membership inference attacks based on confidence descent trajectories. To counter such explanation-driven attacks, the authors propose a trajectory-invariance regularization mechanism that, during training, leverages model gradients to generate perturbations mimicking confidence descent trajectories. The method enforces explanation consistency via KL divergence constraints and aligns the explanatory behaviors of members and non-members through a variance penalty. This approach significantly enhances robustness against trajectory-based membership inference attacks while preserving both model utility and explanation fidelity, thereby strengthening privacy guarantees.

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Impact of Task Phrasing on Presumptions in Large Language Models

May 01, 2026

Large language models (LLMs) are susceptible to task phrasing in real-world applications, often adopting irrational prior assumptions that compromise their reliability and safety. This study addresses this issue by systematically investigating, for the first time, how variations in task wording influence LLMs’ decision-making priors, using the iterated prisoner’s dilemma as a case study. Through a controlled experimental design combined with behavioral analysis and logical reasoning evaluation, the research demonstrates that neutral phrasing significantly reduces models’ reliance on prior assumptions, thereby encouraging more logically consistent reasoning. These findings underscore the critical role of deliberate task wording in enhancing the controllability and safety of LLM behavior.

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Recent publications

Latest Papers

Semantic-Aware Predictive Mapping for Exploration and Navigation

Oct 07, 2026

This study addresses the ambiguity in exploratory navigation caused by purely geometric predictive maps, which struggle to distinguish semantically similar structures such as doors and walls. To overcome this limitation, this work proposes the first integration of an independent door channel into a predictive model, introducing semantic door cues to optimize predictive occupancy mapping. A dual-channel neural network is constructed based on the CogniPlan dataset and evaluated using a multidimensional assessment framework comprising L1 loss, F1 score, and IoU metrics. The proposed approach effectively enhances map completion in ambiguous regions and significantly improves local geometric inference accuracy. Experimental results demonstrate that the L1 error in door regions is reduced to 0.000025, with both F1 score and IoU reaching 1.0, substantially outperforming baseline models.

0 citationsRead paper

Explaining Intrusion Alert Decisions of Deep Learning-based Network Intrusion Detection Systems for Security Analysts

Jul 13, 2026

This work addresses the limited interpretability of alerts generated by existing deep learning–based network intrusion detection systems (NIDS), which hinders effective analyst-driven triage in practice. To bridge this gap, the authors propose EXP-SEC, a novel framework that incorporates a forensic module to pinpoint suspicious traffic and introduces a fine-grained explanation mechanism capable of handling feature overlap and group-wise dependencies. By leveraging a multi-stage mapping strategy, EXP-SEC translates model predictions into semantically meaningful alerts aligned with the domain knowledge of security operations centers. This framework is the first to deliver domain-aligned explanations tailored for security analysts, significantly outperforming xNIDS in group-level and overlap-aware explanatory utility while maintaining comparable performance in accuracy, sparsity, and stability. The resulting explanations are more intuitive and actionable for human analysts.

0 citationsRead paper

HilEnT: Hilbert, Entropy Transformed Image Based Malware Detection

Jul 06, 2026

This study addresses the growing threat of malware by proposing HilEnT, a novel binary-to-image conversion method that maps malware binaries into three-channel color images through Hilbert curve mapping integrated with local entropy features. By combining deep learning with few-shot learning strategies, the approach significantly enhances malware detection and classification performance across four public datasets. Experimental results demonstrate that HilEnT achieves high accuracy and robustness in both binary and multi-class classification tasks, particularly excelling in low-data regimes. The method effectively supports few-shot malware recognition, offering a promising solution for identifying malicious software under data-scarce conditions.

0 citationsRead paper

TIER: Trajectory-Invariant Explanation Regularization for Membership Privacy

Jul 02, 2026

This work addresses the privacy risks posed by explanation interfaces, which can leak membership information and are inadequately mitigated by existing defenses against membership inference attacks based on confidence descent trajectories. To counter such explanation-driven attacks, the authors propose a trajectory-invariance regularization mechanism that, during training, leverages model gradients to generate perturbations mimicking confidence descent trajectories. The method enforces explanation consistency via KL divergence constraints and aligns the explanatory behaviors of members and non-members through a variance penalty. This approach significantly enhances robustness against trajectory-based membership inference attacks while preserving both model utility and explanation fidelity, thereby strengthening privacy guarantees.

0 citationsRead paper

Impact of Task Phrasing on Presumptions in Large Language Models

May 01, 2026

Large language models (LLMs) are susceptible to task phrasing in real-world applications, often adopting irrational prior assumptions that compromise their reliability and safety. This study addresses this issue by systematically investigating, for the first time, how variations in task wording influence LLMs’ decision-making priors, using the iterated prisoner’s dilemma as a case study. Through a controlled experimental design combined with behavioral analysis and logical reasoning evaluation, the research demonstrates that neutral phrasing significantly reduces models’ reliance on prior assumptions, thereby encouraging more logically consistent reasoning. These findings underscore the critical role of deliberate task wording in enhancing the controllability and safety of LLM behavior.

0 citationsRead paper