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IDSGAN: Generative Adversarial Networks for Attack Generation against
  Intrusion Detection

IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection

6 September 2018
Zilong Lin
Yong-yu Shi
Zhi Xue
    AAML
ArXivPDFHTML

Papers citing "IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection"

14 / 14 papers shown
Title
Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability
Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability
Anass Grini
Oumaima Taheri
Btissam El Khamlichi
Amal El Fallah-Seghrouchni
AAML
49
0
0
02 May 2025
Deep generative models as an adversarial attack strategy for tabular
  machine learning
Deep generative models as an adversarial attack strategy for tabular machine learning
Salijona Dyrmishi
Mihaela C. Stoian
Eleonora Giunchiglia
Maxime Cordy
AAML
LMTD
36
0
0
19 Sep 2024
Detecting unknown HTTP-based malicious communication behavior via
  generated adversarial flows and hierarchical traffic features
Detecting unknown HTTP-based malicious communication behavior via generated adversarial flows and hierarchical traffic features
Xiao-chun Yun
Jiang Xie
Shuhao Li
Yongzheng Zhang
Peishuai Sun
19
9
0
07 Sep 2023
Statistical Detection of Adversarial examples in Blockchain-based
  Federated Forest In-vehicle Network Intrusion Detection Systems
Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems
I. Aliyu
Sélinde Van Engelenburg
Muhammed Muazu
Jinsul Kim
C. Lim
AAML
48
14
0
11 Jul 2022
Using EBGAN for Anomaly Intrusion Detection
Using EBGAN for Anomaly Intrusion Detection
Yinxue Cui
Wenfeng Shen
Jian Zhang
Weijia Lu
Chuang Liu
Lingge Sun
Sisi Chen
21
3
0
21 Jun 2022
Generating Practical Adversarial Network Traffic Flows Using NIDSGAN
Generating Practical Adversarial Network Traffic Flows Using NIDSGAN
B. Zolbayar
Ryan Sheatsley
Patrick McDaniel
Mike Weisman
Sencun Zhu
Shitong Zhu
S. Krishnamurthy
GAN
AAML
25
14
0
13 Mar 2022
EVAGAN: Evasion Generative Adversarial Network for Low Data Regimes
EVAGAN: Evasion Generative Adversarial Network for Low Data Regimes
Rizwan Hamid Randhawa
N. Aslam
Mohammad Alauthman
Husnain Rafiq
AAML
GAN
38
11
0
14 Sep 2021
Synthetic flow-based cryptomining attack generation through Generative
  Adversarial Networks
Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks
Alberto Mozo
Ángel González-Prieto
Antonio Agustin Pastor Perales
Sandra Gómez Canaval
Edgar Talavera
32
24
0
30 Jul 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
39
105
0
17 Jun 2021
On the Robustness of Domain Constraints
On the Robustness of Domain Constraints
Ryan Sheatsley
Blaine Hoak
Eric Pauley
Yohan Beugin
Mike Weisman
Patrick McDaniel
AAML
OOD
38
25
0
18 May 2021
Generative Adversarial Networks (GANs) in Networking: A Comprehensive
  Survey & Evaluation
Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation
Hojjat Navidan
P. Moshiri
M. Nabati
Reza Shahbazian
S. Ghorashi
V. Shah-Mansouri
David Windridge
15
84
0
10 May 2021
PacketCGAN: Exploratory Study of Class Imbalance for Encrypted Traffic
  Classification Using CGAN
PacketCGAN: Exploratory Study of Class Imbalance for Encrypted Traffic Classification Using CGAN
Pan Wang
Shuhang Li
Feng Ye
Zixuan Wang
Moxuan Zhang
26
65
0
27 Nov 2019
The Threat of Adversarial Attacks on Machine Learning in Network
  Security -- A Survey
The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey
Olakunle Ibitoye
Rana Abou-Khamis
Mohamed el Shehaby
Ashraf Matrawy
M. O. Shafiq
AAML
44
68
0
06 Nov 2019
Securing Connected & Autonomous Vehicles: Challenges Posed by
  Adversarial Machine Learning and The Way Forward
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward
A. Qayyum
Muhammad Usama
Junaid Qadir
Ala I. Al-Fuqaha
AAML
27
187
0
29 May 2019
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