paper-with-me

홈 › Papers

Outlier detection on network flow analysis

2018-08-06 · Quang-Vinh Dang

It is important to be able to detect and classify malicious network traffic flows such as DDoS attacks from benign flows. Normally the task is performed by using supervised classification algorithms. In this paper we analyze the usage of outlier detection algorithms for the network traffic classification problem.

📄 PDF Abstract BibTeX arXiv:1808.02024

Code (0)

등록된 구현이 없습니다.

Tasks

ClassificationGeneral ClassificationOutlier DetectionTraffic Classification

Similar Papers 제목 키워드 기반

Robust Outlier Detection and Low-Latency Concept Drift Adaptation for Data Stream Regression: A Dual-Channel Architecture

2025-12-13 · Bingbing Wang, Shengyan Sun, Jiaqi Wang, Yu Tang arxiv

Outlier detection and concept drift detection represent two challenges in data analysis. Most studies address these issues separately. However, joint detection mechanisms in regression remain underexplored, where the con…

Outlier Detection

Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection

2023-02-01 · CVPR 2023 1 · Nishant Kumar, Siniša Šegvić, Abouzar Eslami, Stefan Gumhold

Real-world deployment of reliable object detectors is crucial for applications such as autonomous driving. However, general-purpose object detectors like Faster R-CNN are prone to providing overconfident predictions for …

Autonomous DrivingObjectobject-detectionObject Detection

PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection

2024-12-11 · Sihan Chen, Zhuangzhuang Qian, Wingchun Siu, Xingcan Hu 외

Outlier detection (OD), also known as anomaly detection, is a critical machine learning (ML) task with applications in fraud detection, network intrusion detection, clickstream analysis, recommendation systems, and socia…

Anomaly DetectionFraud DetectionIntrusion DetectionLarge Language Model+4

InFlow: Robust outlier detection utilizing Normalizing Flows

2021-06-10 · Nishant Kumar, Pia Hanfeld, Michael Hecht, Michael Bussmann 외

Normalizing flows are prominent deep generative models that provide tractable probability distributions and efficient density estimation. However, they are well known to fail while detecting Out-of-Distribution (OOD) inp…

Density EstimationOutlier DetectionOut of Distribution (OOD) Detection

Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data

2022-08-30 · Robert Schmier, Ullrich Köthe, Christoph-Nikolas Straehle

Detecting test data deviating from training data is a central problem for safe and robust machine learning. Likelihoods learned by a generative model, e.g., a normalizing flow via standard log-likelihood training, perfor…

Anomaly DetectionOutlier Detection