Adversary-Aware Learning Techniques and Trends in Cybersecurity
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(FIVE YEARS 2)

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Published By Springer International Publishing

9783030556914, 9783030556921

Latest Documents Most Cited Documents Contributed Authors Related Sources Related Keywords

Cyber-(in)Security, Revisited: Proactive Cyber-Defenses, Interdependence and Autonomous Human-Machine Teams (A-HMTs)

Adversary-Aware Learning Techniques and Trends in Cybersecurity ◽

10.1007/978-3-030-55692-1_10 ◽

2021 ◽

pp. 197-224

Author(s):

William F. Lawless ◽

Ranjeev Mittu ◽

Ira S. Moskowitz ◽

Donald A. Sofge ◽

Stephen Russell

Automating the Investigation of Sophisticated Cyber Threats with Cognitive Agents

Adversary-Aware Learning Techniques and Trends in Cybersecurity ◽

10.1007/978-3-030-55692-1_7 ◽

2021 ◽

pp. 125-146

Author(s):

Steven Meckl ◽

Gheorghe Tecuci ◽

Dorin Marcu ◽

Mihai Boicu

Keyword(s):

Cognitive Agents ◽

Cyber Threats

Integrating Human Reasoning and Machine Learning to Classify Cyber Attacks

Adversary-Aware Learning Techniques and Trends in Cybersecurity ◽

10.1007/978-3-030-55692-1_8 ◽

2020 ◽

pp. 147-165

Author(s):

Ying Zhao ◽

Lauren Jones

Keyword(s):

Machine Learning ◽

Cyber Attacks ◽

Human Reasoning

Overview of GANs for Image Synthesis and Detection Methods

Adversary-Aware Learning Techniques and Trends in Cybersecurity ◽

10.1007/978-3-030-55692-1_5 ◽

2020 ◽

pp. 85-101

Author(s):

Eric Tjon ◽

Melody Moh ◽

Teng-Sheng Moh

Keyword(s):

Image Synthesis ◽

Detection Methods

Robust Machine Learning Using Diversity and Blockchain

Adversary-Aware Learning Techniques and Trends in Cybersecurity ◽

10.1007/978-3-030-55692-1_6 ◽

2020 ◽

pp. 103-121

Author(s):

Raj Mani Shukla ◽

Shahriar Badsha ◽

Deepak Tosh ◽

Shamik Sengupta

Keyword(s):

Machine Learning

Security of Distributed Machine Learning

Adversary-Aware Learning Techniques and Trends in Cybersecurity ◽

10.1007/978-3-030-55692-1_2 ◽

2020 ◽

pp. 17-36

Author(s):

Rui Zhang ◽

Quanyan Zhu

Keyword(s):

Machine Learning ◽

Distributed Machine Learning

Adversarial Machine Learning in Text: A Case Study of Phishing Email Detection with RCNN Model

Adversary-Aware Learning Techniques and Trends in Cybersecurity ◽

10.1007/978-3-030-55692-1_4 ◽

2020 ◽

pp. 61-83

Author(s):

Daniel Lee ◽

Rakesh M. Verma

Keyword(s):

Machine Learning

Homology as an Adversarial Attack Indicator

Adversary-Aware Learning Techniques and Trends in Cybersecurity ◽

10.1007/978-3-030-55692-1_9 ◽

2020 ◽

pp. 167-196

Author(s):

Ira S. Moskowitz ◽

Nolan Bay ◽

Brian Jalaian ◽

Arnold Tunick

Keyword(s):

Adversarial Attack

Be Careful When Learning Against Adversaries: Imitative Attacker Deception in Stackelberg Security Games

Adversary-Aware Learning Techniques and Trends in Cybersecurity ◽

10.1007/978-3-030-55692-1_3 ◽

2020 ◽

pp. 37-57

Author(s):

Haifeng Xu ◽

Thanh H. Nguyen

Keyword(s):

Security Games

Rethinking Intelligent Behavior as Competitive Games for Handling Adversarial Challenges to Machine Learning

Adversary-Aware Learning Techniques and Trends in Cybersecurity ◽

10.1007/978-3-030-55692-1_1 ◽

2020 ◽

pp. 3-16

Author(s):

Joseph B. Collins ◽

Prithviraj Dasgupta

Keyword(s):

Machine Learning ◽

Intelligent Behavior ◽

Competitive Games

Adversary-Aware Learning Techniques and Trends in CybersecurityLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer International Publishing

Cyber-(in)Security, Revisited: Proactive Cyber-Defenses, Interdependence and Autonomous Human-Machine Teams (A-HMTs)

Automating the Investigation of Sophisticated Cyber Threats with Cognitive Agents

Integrating Human Reasoning and Machine Learning to Classify Cyber Attacks

Overview of GANs for Image Synthesis and Detection Methods

Robust Machine Learning Using Diversity and Blockchain

Security of Distributed Machine Learning

Adversarial Machine Learning in Text: A Case Study of Phishing Email Detection with RCNN Model

Homology as an Adversarial Attack Indicator

Be Careful When Learning Against Adversaries: Imitative Attacker Deception in Stackelberg Security Games

Rethinking Intelligent Behavior as Competitive Games for Handling Adversarial Challenges to Machine Learning

Adversary-Aware Learning Techniques and Trends in Cybersecurity
Latest Publications