相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Applicability of machine learning in spam and phishing email filtering: review and approaches
Tushaar Gangavarapu et al.
ARTIFICIAL INTELLIGENCE REVIEW (2020)
On defending against label flipping attacks on malware detection systems (Mar, 10.1007/s00521-020-04831-9, 2020)
Rahim Taheri et al.
NEURAL COMPUTING & APPLICATIONS (2020)
Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics
Yuxin Ma et al.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS (2020)
Adversarial attacks on medical machine learning
Samuel G. Finlayson et al.
SCIENCE (2019)
A taxonomy on impact of label noise and feature noise using machine learning techniques
A. Shanthini et al.
SOFT COMPUTING (2019)
Adversarial classification: An adversarial risk analysis approach
Roi Naveiro et al.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING (2019)
A Survey of Attacks Against Twitter Spam Detectors in an Adversarial Environment
Niddal H. Imam et al.
ROBOTICS (2019)
Machine learning for email spam filtering: review, approaches and open research problems
Emmanuel Gbenga Dada et al.
HELIYON (2019)
Privacy-preserving Naive Bayes classifiers secure against the substitution-then-comparison attack
Chong-zhi Gao et al.
INFORMATION SCIENCES (2018)
A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View
Qiang Liu et al.
IEEE ACCESS (2018)
Evading Classifiers by Morphing in the Dark
Hung Dang et al.
CCS'17: PROCEEDINGS OF THE 2017 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (2017)
Evaluation of random forest classifier in security domain
Zeinab Khorshidpour et al.
APPLIED INTELLIGENCE (2017)
Evaluating the classifier behavior with noisy data considering performance and robustness: The Equalized Loss of Accuracy measure
Jose A. Saez et al.
NEUROCOMPUTING (2016)
Support vector machines under adversarial label contamination
Huang Xiao et al.
NEUROCOMPUTING (2015)
Behaviour reflects personality: detecting co-residence attacks on Xen-based cloud environments
Nikolaos Pitropakis et al.
INTERNATIONAL JOURNAL OF INFORMATION SECURITY (2015)
Security Evaluation of Pattern Classifiers under Attack
Battista Biggio et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2014)
An empirical study of the classification performance of learners on imbalanted and noisy software quality data
Chris Seiffert et al.
INFORMATION SCIENCES (2014)
Classification in the Presence of Label Noise: a Survey
Benoit Frenay et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2014)
The security of machine learning
Marco Barreno et al.
MACHINE LEARNING (2010)
Robustness of multimodal biometric fusion methods against spoof attacks
Ricardo N. Rodrigues et al.
JOURNAL OF VISUAL LANGUAGES AND COMPUTING (2009)
Exploring conditions for the optimality of Naive bayes
H Zhang
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE (2005)
Bayesian face recognition
B Moghaddam et al.
PATTERN RECOGNITION (2000)