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Machine Learning for Detecting Data Exfiltration: A Review

Machine Learning for Detecting Data Exfiltration: A Review

17 December 2020
Bushra Sabir
Faheem Ullah
M. Babar
R. Gaire
    AAML
ArXivPDFHTML

Papers citing "Machine Learning for Detecting Data Exfiltration: A Review"

18 / 18 papers shown
Title
Introduction to Machine Learning
Introduction to Machine Learning
Laurent Younes
25
2,496
0
04 Sep 2024
A Multi-Vocal Review of Security Orchestration
A Multi-Vocal Review of Security Orchestration
Chadni Islam
M. Babar
Surya Nepal
23
69
0
21 Feb 2020
Is Attention Interpretable?
Is Attention Interpretable?
Sofia Serrano
Noah A. Smith
75
679
0
09 Jun 2019
A Survey of Network-based Intrusion Detection Data Sets
A Survey of Network-based Intrusion Detection Data Sets
Markus Ring
Sarah Wunderlich
Deniz Scheuring
Dieter Landes
Andreas Hotho
37
558
0
06 Mar 2019
Attention is not Explanation
Attention is not Explanation
Sarthak Jain
Byron C. Wallace
FAtt
87
1,307
0
26 Feb 2019
Adversarial Attacks on Deep Learning Models in Natural Language
  Processing: A Survey
Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey
W. Zhang
Quan Z. Sheng
A. Alhazmi
Chenliang Li
AAML
49
57
0
21 Jan 2019
An Analysis of Attention Mechanisms: The Case of Word Sense
  Disambiguation in Neural Machine Translation
An Analysis of Attention Mechanisms: The Case of Word Sense Disambiguation in Neural Machine Translation
Gongbo Tang
Rico Sennrich
Joakim Nivre
43
84
0
17 Oct 2018
Insight into Insiders and IT: A Survey of Insider Threat Taxonomies,
  Analysis, Modeling, and Countermeasures
Insight into Insiders and IT: A Survey of Insider Threat Taxonomies, Analysis, Modeling, and Countermeasures
I. Homoliak
Flavio Toffalini
J. Guarnizo
Yuval Elovici
Martín Ochoa
75
116
0
04 May 2018
Architectural Tactics for Big Data Cybersecurity Analytic Systems: A
  Review
Architectural Tactics for Big Data Cybersecurity Analytic Systems: A Review
Faheem Ullah
M. Babar
26
40
0
09 Feb 2018
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Battista Biggio
Fabio Roli
AAML
85
1,401
0
08 Dec 2017
Exposure: A White-Box Photo Post-Processing Framework
Exposure: A White-Box Photo Post-Processing Framework
Yuanming Hu
Hao He
Chenxi Xu
Baoyuan Wang
Stephen Lin
40
304
0
27 Sep 2017
Machine Learning Models that Remember Too Much
Machine Learning Models that Remember Too Much
Congzheng Song
Thomas Ristenpart
Vitaly Shmatikov
VLM
52
511
0
22 Sep 2017
Recent Trends in Deep Learning Based Natural Language Processing
Recent Trends in Deep Learning Based Natural Language Processing
Tom Young
Devamanyu Hazarika
Soujanya Poria
Min Zhang
65
2,833
0
09 Aug 2017
Automated Poisoning Attacks and Defenses in Malware Detection Systems:
  An Adversarial Machine Learning Approach
Automated Poisoning Attacks and Defenses in Malware Detection Systems: An Adversarial Machine Learning Approach
Sen Chen
Minhui Xue
Lingling Fan
S. Hao
Lihua Xu
Haojin Zhu
Yue Liu
AAML
62
220
0
13 Jun 2017
Attention Is All You Need
Attention Is All You Need
Ashish Vaswani
Noam M. Shazeer
Niki Parmar
Jakob Uszkoreit
Llion Jones
Aidan Gomez
Lukasz Kaiser
Illia Polosukhin
3DV
453
129,831
0
12 Jun 2017
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence
  Modeling
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Junyoung Chung
Çağlar Gülçehre
Kyunghyun Cho
Yoshua Bengio
293
12,662
0
11 Dec 2014
Command & Control: Understanding, Denying and Detecting - A review of
  malware C2 techniques, detection and defences
Command & Control: Understanding, Denying and Detecting - A review of malware C2 techniques, detection and defences
Joseph Gardiner
M. Cova
Shishir Nagaraja
26
34
0
05 Aug 2014
word2vec Explained: deriving Mikolov et al.'s negative-sampling
  word-embedding method
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
Yoav Goldberg
Omer Levy
SSL
54
1,608
0
15 Feb 2014
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