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TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion
  Attacks against Network Intrusion Detection Systems

TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems

27 October 2022
Islam Debicha
Richard Bauwens
Thibault Debatty
Jean-Michel Dricot
Tayeb Kenaza
Wim Mees
    AAML
ArXiv (abs)PDFHTML

Papers citing "TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems"

13 / 13 papers shown
Title
Detect & Reject for Transferability of Black-box Adversarial Attacks
  Against Network Intrusion Detection Systems
Detect & Reject for Transferability of Black-box Adversarial Attacks Against Network Intrusion Detection Systems
Islam Debicha
Thibault Debatty
Jean-Michel Dricot
Wim Mees
Tayeb Kenaza
AAML
12
9
0
22 Dec 2021
Modeling Realistic Adversarial Attacks against Network Intrusion
  Detection Systems
Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems
Giovanni Apruzzese
M. Andreolini
Luca Ferretti
Mirco Marchetti
M. Colajanni
AAML
78
108
0
17 Jun 2021
Adversarial Feature Selection against Evasion Attacks
Adversarial Feature Selection against Evasion Attacks
Fei Zhang
P. Chan
Battista Biggio
D. Yeung
Fabio Roli
AAML
54
226
0
25 May 2020
Rallying Adversarial Techniques against Deep Learning for Network
  Security
Rallying Adversarial Techniques against Deep Learning for Network Security
Joseph Clements
Yuzhe Yang
Ankur A Sharma
Hongxin Hu
Yingjie Lao
AAML
65
52
0
27 Mar 2019
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion
  Detection
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
Yisroel Mirsky
Tomer Doitshman
Yuval Elovici
A. Shabtai
81
814
0
25 Feb 2018
Towards Deep Learning Models Resistant to Adversarial Attacks
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry
Aleksandar Makelov
Ludwig Schmidt
Dimitris Tsipras
Adrian Vladu
SILMOOD
315
12,131
0
19 Jun 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection
  Methods
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Nicholas Carlini
D. Wagner
AAML
131
1,864
0
20 May 2017
On Detecting Adversarial Perturbations
On Detecting Adversarial Perturbations
J. H. Metzen
Tim Genewein
Volker Fischer
Bastian Bischoff
AAML
63
950
0
14 Feb 2017
Towards Evaluating the Robustness of Neural Networks
Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini
D. Wagner
OODAAML
268
8,583
0
16 Aug 2016
Learning without Forgetting
Learning without Forgetting
Zhizhong Li
Derek Hoiem
CLLOODSSL
306
4,428
0
29 Jun 2016
DeepFool: a simple and accurate method to fool deep neural networks
DeepFool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
P. Frossard
AAML
154
4,905
0
14 Nov 2015
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAMLGAN
282
19,121
0
20 Dec 2014
Intriguing properties of neural networks
Intriguing properties of neural networks
Christian Szegedy
Wojciech Zaremba
Ilya Sutskever
Joan Bruna
D. Erhan
Ian Goodfellow
Rob Fergus
AAML
282
14,963
1
21 Dec 2013
1