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Detection of illicit cryptomining using network metadata

EURASIP Journal on Information Security, 2021-12, Vol.2021 (1), p.1-20, Article 11

2021

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Learning and Classification of Malware Behavior

Detection of Intrusions and Malware, and Vulnerability Assessment, 2008, Vol.5137, p.108-125

2008

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Practical Evasion of a Learning-Based Classifier: A Case Study

2014 IEEE Symposium on Security and Privacy, 2014, p.197-211

2014

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Learning Intrusion Detection: Supervised or Unsupervised?

Image Analysis and Processing – ICIAP 2005, 2005, p.50-57

2005

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Machine learning in adversarial environments

Machine learning, 2010-11, Vol.81 (2), p.115-119

2010

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Hidost: a static machine-learning-based detector of malicious files

EURASIP Journal on Information Security, 2016-09, Vol.2016 (1), Article 22

2016

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A factorization method for the classification of infrared spectra

BMC bioinformatics, 2010-11, Vol.11 (1), p.561-561, Article 561

2010

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Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples

IEEE eTransactions on network and service management, 2022-12, Vol.19 (4), p.5312-5332

2022

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Evasion Attacks against Machine Learning at Test Time

Machine Learning and Knowledge Discovery in Databases, 2013, p.387-402

2013

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Detecting Self-mutating Malware Using Control-Flow Graph Matching

Detection of Intrusions and Malware & Vulnerability Assessment, 2006, p.129-143

2006

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The Role of Machine Learning in Cybersecurity

Digital threats (Print), 2023-03, Vol.4 (1), p.1-38, Article 8

2023

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Detection of Intrusions and Malware, and Vulnerability Assessment: Third International Conference, DIMVA 2006, Berlin, Germany, July 13-14, 2006, Proceedings

1, 2006

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Static detection of malicious JavaScript-bearing PDF documents

Proceedings of the 27th Annual Computer Security Applications Conference, 2011, p.373-382

2011

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Detecting Unknown Network Attacks Using Language Models

Detection of Intrusions and Malware & Vulnerability Assessment, 2006, p.74-90

2006

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Language models for detection of unknown attacks in network traffic

Journal in computer virology, 2007-02, Vol.2 (4), p.243-256

2007

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Automatic feature selection for anomaly detection

Proceedings of the 1st ACM workshop on Workshop on AISec, 2008, p.71-76

2008

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Towards Understanding the Skill Gap in Cybersecurity

Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 1, 2022, p.477-483

2022

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A framework for quantitative security analysis of machine learning

Proceedings of the 2nd ACM workshop on Security and artificial intelligence, 2009, p.1-4

2009

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Feasible Direction Decomposition Algorithms for Training Support Vector Machines

Machine learning, 2002-01, Vol.46 (1-3), p.315-349

Laskov, Pavel

2002

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Network–Level Polymorphic Shellcode Detection Using Emulation

Detection of Intrusions and Malware & Vulnerability Assessment, 2006, p.54-73

2006

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SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection

2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P), 2023, p.592-614

2023

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Using Labeling to Prevent Cross-Service Attacks Against Smart Phones

Detection of Intrusions and Malware & Vulnerability Assessment, 2006, p.91-108

2006

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Anomaly Detection at “supersonic” Speed

Information technology (Munich, Germany), 2012-04, Vol.54 (2), p.82-89

2012

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SoK: The Impact of Unlabelled Data in Cyberthreat Detection

2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P), 2022, p.20-42

2022

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Understanding the risk factors of learning in adversarial environments

Proceedings of the 4th ACM workshop on Security and artificial intelligence, 2011, p.87-92

2011

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Detection of illicit cryptomining using network metadata

Learning and Classification of Malware Behavior

Practical Evasion of a Learning-Based Classifier: A Case Study

Learning Intrusion Detection: Supervised or Unsupervised?

Machine learning in adversarial environments

Hidost: a static machine-learning-based detector of malicious files

A factorization method for the classification of infrared spectra

Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples

Evasion Attacks against Machine Learning at Test Time

Detecting Self-mutating Malware Using Control-Flow Graph Matching

The Role of Machine Learning in Cybersecurity

Detection of Intrusions and Malware, and Vulnerability Assessment: Third International Conference, DIMVA 2006, Berlin, Germany, July 13-14, 2006, Proceedings

Static detection of malicious JavaScript-bearing PDF documents

Detecting Unknown Network Attacks Using Language Models

Language models for detection of unknown attacks in network traffic

Automatic feature selection for anomaly detection

Towards Understanding the Skill Gap in Cybersecurity

A framework for quantitative security analysis of machine learning

Feasible Direction Decomposition Algorithms for Training Support Vector Machines

Network–Level Polymorphic Shellcode Detection Using Emulation

SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection

Using Labeling to Prevent Cross-Service Attacks Against Smart Phones

Anomaly Detection at “supersonic” Speed

SoK: The Impact of Unlabelled Data in Cyberthreat Detection

Understanding the risk factors of learning in adversarial environments

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