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Found 16 papers, 11 papers with code
Face It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logs
Based on the insights gained from the preliminary study, we propose an LLM-based two-stage strategy for end-users to localize the root-cause configuration properties based on logs.
Can Language Models Pretend Solvers? Logic Code Simulation with LLMs
This study delves into a novel aspect, namely logic code simulation, which forces LLMs to emulate logical solvers in predicting the results of logical programs.
MTAD: Tools and Benchmarks for Multivariate Time Series Anomaly Detection
These methods are evaluated with five multivariate KPI datasets that are publicly available.
Practical Anomaly Detection over Multivariate Monitoring Metrics for Online Services
As modern software systems continue to grow in terms of complexity and volume, anomaly detection on multivariate monitoring metrics, which profile systems' health status, becomes more and more critical and challenging.
Performance Issue Identification in Cloud Systems with Relational-Temporal Anomaly Detection
Performance issues permeate large-scale cloud service systems, which can lead to huge revenue losses.
Improving the Transferability of Adversarial Samples by Path-Augmented Method
However, such methods selected the image augmentation path heuristically and may augment images that are semantics-inconsistent with the target images, which harms the transferability of the generated adversarial samples.
Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention
Our study demonstrates that logs and metrics can manifest system anomalies collaboratively and complementarily, and neither of them only is sufficient.
eBPF-based Working Set Size Estimation in Memory Management
Working set size estimation (WSS) is of great significance to improve the efficiency of program executing and memory arrangement in modern operating systems.
Learning Concordant Attention via Target-aware Alignment for Visible-Infrared Person Re-identification
Owing to the large distribution gap between the heterogeneous data in Visible-Infrared Person Re-identification (VI Re-ID), we point out that existing paradigms often suffer from the inter-modal semantic misalignment issue and thus fail to align and compare local details properly.
AEON: A Method for Automatic Evaluation of NLP Test Cases
However, in practice, many of the generated test cases fail to preserve similar semantic meaning and are unnatural (e. g., grammar errors), which leads to a high false alarm rate and unnatural test cases.
Improving Adversarial Transferability via Neuron Attribution-Based Attacks
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples.
Graph-based Incident Aggregation for Large-Scale Online Service Systems
The proposed framework is evaluated with real-world incident data collected from a large-scale online service system of Huawei Cloud.
Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection
To better understand the characteristics of different anomaly detectors, in this paper, we provide a comprehensive review and evaluation of five popular neural networks used by six state-of-the-art methods.
Improving the Transferability of Adversarial Samples With Adversarial Transformations
Although deep neural networks (DNNs) have achieved tremendous performance in diverse vision challenges, they are surprisingly susceptible to adversarial examples, which are born of intentionally perturbing benign samples in a human-imperceptible fashion.
DeepObfuscation: Securing the Structure of Convolutional Neural Networks via Knowledge Distillation
Our obfuscation approach is very effective to protect the critical structure of a deep learning model from being exposed to attackers.
Cryptography and Security
Learning to Rank Using Localized Geometric Mean Metrics
First, we design a concept called \textit{ideal candidate document} to introduce metric learning algorithm to query-independent model.
