DeepAID: Interpreting and Improving Deep Learning-based Anomaly
Detection in Security Applications

DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications

23 September 2021

Dongqi Han

Papers citing "DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications"

14 / 14 papers shown

Title
UniNet: A Unified Multi-granular Traffic Modeling Framework for Network Security Binghui Wu D. Divakaran M. Gurusamy 57 0 0 06 Mar 2025
A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network Security Osvaldo Arreche Mustafa Abdallah AAML 41 1 0 14 Jan 2025
Visually Analyze SHAP Plots to Diagnose Misclassifications in ML-based Intrusion Detection Maraz Mia Mir Mehedi Ahsan Pritom Tariqul Islam Kamrul Hasan AAML 35 0 0 04 Nov 2024
XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language Model Yasir Ali Farrukh S. Wali I. Khan Nathaniel D. Bastian 147 2 0 27 Aug 2024
AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection Aditya Singh Pavan Reddy 32 1 0 05 May 2024
AI for DevSecOps: A Landscape and Future Opportunities Michael Fu Jirat Pasuksmit C. Tantithamthavorn 35 6 0 07 Apr 2024
Genos: General In-Network Unsupervised Intrusion Detection by Rule Extraction Ruoyu Li Qing Li Yu Zhang Dan Zhao Xi Xiao Yong-jia Jiang 37 3 0 28 Mar 2024
HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs) Tarek Ali Panos Kostakos 30 39 0 27 Sep 2023
FINER: Enhancing State-of-the-art Classifiers with Feature Attribution to Facilitate Security Analysis Yiling He Jian Lou Zhan Qin Kui Ren FAtt AAML 23 7 0 10 Aug 2023
Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenance Zijun Cheng Qiujian Lv Jinyuan Liang Yan Wang Degang Sun Thomas Pasquier Xueyuan Han 19 33 0 09 Aug 2023
SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection Giovanni Apruzzese Pavel Laskov J. Schneider 44 24 0 30 Apr 2023
SoK: Modeling Explainability in Security Analytics for Interpretability, Trustworthiness, and Usability Dipkamal Bhusal Rosalyn Shin Ajay Ashok Shewale M. K. Veerabhadran Michael Clifford Sara Rampazzi Nidhi Rastogi FAtt AAML 34 5 0 31 Oct 2022
SoK: Explainable Machine Learning for Computer Security Applications A. Nadeem D. Vos Clinton Cao Luca Pajola Simon Dieck Robert Baumgartner S. Verwer 34 40 0 22 Aug 2022
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017