Network Anomaly Detection

Network anomaly detection aims to identify unusual patterns in network traffic indicative of security breaches or system failures. Current research heavily utilizes deep learning, particularly autoencoders, generative adversarial networks (GANs), and convolutional neural networks (CNNs), often combined with other techniques like Isolation Forests and transformers to improve accuracy and reduce false positives. These advancements address challenges posed by high-dimensional data, evolving attack methods, and the need for efficient, scalable solutions, impacting network security and reliability across various applications, including IoT and 5G networks. Furthermore, incorporating explainable AI (XAI) methods is gaining traction to enhance model transparency and improve performance.

Papers

November 18, 2024

SADDE: Semi-supervised Anomaly Detection with Dependable Explanations
Yachao Yuan, Yu Huang, Yali Yuan, Jin Wang
Anomaly Detection Faithful Explanation Network Anomaly Detection

July 11, 2024

Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation
D'Jeff K. Nkashama, Jordan Masakuna Félicien, Arian Soltani, Jean-Charles Verdier, Pierre-Martin Tardif, Marc Frappier, Froduald Kabanza
Deep Learning Native Robustness Anomaly Detection Data Contamination State of the Art Anomaly Advanced Persistent Threat Network Anomaly Detection

July 8, 2024

Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks
Shuzhan Wang, Ruxue Jiang, Zhaoqi Wang, Yan Zhou
Anomaly Detection Computer Network Log Analysis Network Anomaly Detection Deep Learning Based Anomaly Detection

July 1, 2024

PCAPVision: PCAP-Based High-Velocity and Large-Volume Network Failure Detection
Lukasz Tulczyjew, Ihor Biruk, Murat Bilgic, Charles Abondo, Nathanael Weill
High Speed Failure Detection Network Anomaly Detection Deep Packet Inspection

October 10, 2023

Assessing the Impact of a Supervised Classification Filter on Flow-based Hybrid Network Anomaly Detection
Dominik Macko, Patrik Goldschmidt, Peter Pištek, Daniela Chudá
Global Impact Supervised Machine Learning Based Anomaly Detection Anomaly Detector Network Anomaly Detection

August 17, 2023

Forensic Data Analytics for Anomaly Detection in Evolving Networks
Li Yang, Abdallah Moubayed, Abdallah Shami, Amine Boukhtouta, Parisa Heidari, Stere Preda, Richard Brunner, Daniel Migault, Adel Larabi
Anomaly Detection Prediction Framework Network Anomaly Detection Network Evolution

March 13, 2023

Network Anomaly Detection Using Federated Learning
William Marfo, Deepak K. Tosh, Shirley V. Moore
Centralized Training Centralized Machine Learning Network Anomaly Detection Efficient Anomaly Detection

February 2, 2023

Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detection
Fernando Pérez-Bueno, Luz García, Gabriel Maciá-Fernández, Rafael Molina
Anomaly Detection Network Anomaly Detection Probabilistic Principal Component Analysis Multivariate Statistical Network Monitoring

December 23, 2022

Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks
Tung-Anh Nguyen, Jiayu He, Long Tan Le, Wei Bao, Nguyen H. Tran
Anomaly Detection Principal Component Analysis Internet of Thing Network Grassmann Manifold Network Anomaly Detection

December 15, 2022

DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection
Mohanad Sarhan, Gayan Kulatilleke, Wai Weng Lo, Siamak Layeghy, Marius Portmann
Network Intrusion Detection Deeper Network Hybrid Deep Learning One Class Classification Multi Class Anomaly Detection Network Anomaly Detection One Class Classifier

November 28, 2022

ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness
Jingcan Duan, Bin Xiao, Siwei Wang, Haifang Zhou, Xinwang Liu
Graph Anomaly Detection Attributed Network Network Anomaly Detection Anomaly Node ApeRIodic SEmi Parametric Topology Attack

November 18, 2022

Rare Yet Popular: Evidence and Implications from Labeled Datasets for Network Anomaly Detection
Jose Manuel Navarro, Alexis Huet, Dario Rossi
Anomaly Detection Evidence Piece Unlabeled Anomaly Future Implication Global Outlier Popularity Score Network Anomaly Detection

July 8, 2022

ENCODE: Encoding NetFlows for Network Anomaly Detection
Clinton Cao, Annibale Panichella, Sicco Verwer, Agathe Blaise, Filippo Rebecchi
Network Anomaly Detection Network Attack Deep Packet Inspection

May 3, 2022

ARCADE: Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detection
Willian T. Lunardi, Martin Andreoni Lopez, Jean-Pierre Giacalone
Anomaly Detection Convolutional Autoencoder Network Anomaly Detection Network Attack Based Intrusion Detection

December 23, 2021

A Multi-View Framework for BGP Anomaly Detection via Graph Attention Network
Songtao Peng, Jiaqi Nie, Xincheng Shu, Zhongyuan Ruan, Lei Wang, Yunxuan Sheng, Qi Xuan
Multi View Graph Attention Network Software Defined Network Anomaly Detection

December 14, 2021

Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)
Khushnaseeb Roshan, Aasim Zafar
Machine Learning Supervised Autoencoder Explainable Artificial Intelligence XAI Method Shapley Value Shapley Additive Explanation Network Anomaly Detection