Author name cluster

Wei Qiao

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

AAAI Conference 2026 Conference Paper

Sentient: Detecting APTs via Capturing Indirect Dependencies and Behavioral Logic

Wenhao Yan
Ning An
Wei Qiao
Weiheng Wu
Zhigang Lu
Bo Jiang
Baoxu Liu
Junrong Liu

Advanced Persistent Threats (APTs) are difficult to detect due to their complexity and stealthiness. To mitigate such attacks, many approaches model entities and their relationship using provenance graphs to detect the stealthy and persistent characteristics of APTs. However, existing detection methods suffer from the flaws of missing indirect dependencies, noisy complex scenarios, and missing behavioral logical associations, which make it difficult to detect complex scenarios and effectively identify stealthy threats. In this paper, we propose Sentient, an APT detection method that combines pre-training and intent analysis. It employs a graph transformer to learn structural and semantic information from provenance graphs to avoid missing indirect dependencies. We mitigate scenario noise by combining global and local information. Additionally, we design an Intent Analysis Module (IAM) to associate logical relationships between behaviors. Sentient is trained solely on easily obtainable benign data to detect malicious behaviors that deviate from benign behavioral patterns. We evaluated Sentient on three widely-used datasets covering real-world attacks and simulated attacks. Notably, compared to six state-of-the-art methods, Sentient achieved an average reduction of 44% in false positive rate(FPR) for detection.

PDF Details DOI

NeurIPS Conference 2023 Conference Paper

Benchmarking Robustness to Adversarial Image Obfuscations

Florian Stimberg
Ayan Chakrabarti
Chun-Ta Lu
Hussein Hazimeh
Otilia Stretcu
Wei Qiao
Yintao Liu
Merve Kaya

Automated content filtering and moderation is an important tool that allows online platforms to build striving user communities that facilitate cooperation and prevent abuse. Unfortunately, resourceful actors try to bypass automated filters in a bid to post content that violate platform policies and codes of conduct. To reach this goal, these malicious actors may obfuscate policy violating images (e. g. , overlay harmful images by carefully selected benign images or visual patterns) to prevent machine learning models from reaching the correct decision. In this paper, we invite researchers to tackle this specific issue and present a new image benchmark. This benchmark, based on ImageNet, simulates the type of obfuscations created by malicious actors. It goes beyond Image-Net-C and ImageNet-C-bar by proposing general, drastic, adversarial modifications that preserve the original content intent. It aims to tackle a more common adversarial threat than the one considered by lp-norm bounded adversaries. We evaluate 33 pretrained models on the benchmark and train models with different augmentations, architectures and training methods on subsets of the obfuscations to measure generalization. Our hope is that this benchmark will encourage researchers to test their models and methods and try to find new approaches that are more robust to these obfuscations.

PDF Details

EAAI Journal 2008 Journal Article

Robust neuro-identification of nonlinear plants in electric power systems with missing sensor measurements

Wei Qiao
Zhi Gao
Ronald G. Harley
Ganesh K. Venayagamoorthy

Fault tolerant measurements are an essential requirement for system identification, control and protection. Measurements can be corrupted or interrupted due to sensor failure, broken or bad connections, bad communication, or malfunction of some hardware or software. This paper proposes a novel robust artificial neural network identifier (RANNI) by combining a sensor evaluation and (missing sensor) restoration scheme (SERS) and an ANN identifier (ANNI) in a cascading structure. This RANNI is able to provide continuous on-line identification of nonlinear plants when some crucial sensor measurements are unavailable. A static synchronous series compensator (SSSC) connected to a power system is used as a test system to examine the validity of the proposed model. Simulation studies are carried out with single and multiple phase current sensors missing; results show that the proposed RANNI continuously tracks the plant dynamics with good precision during the steady state, the small disturbance, the transient state after a large disturbance and the unbalanced three-phase operations. The proposed RANNI is readily applicable to other plant models in power systems.

Details DOI