Machine learning has yield significant advances in decision-making for complex systems, but are they robust against adversarial attacks? We generalize the evasion attack problem to the multi-class linear classifiers, and present an efficient algorithm for approximating the optimal disguised instance. Experiments on real-world data demonstrate the effectiveness of our method
Pattern classifiers have been widely used in adversarial settings like spam and malware detection, ...
In recent years, machine learning algorithms have been applied widely in various fields such as heal...
We investigate a problem at the intersection of machine learning and security: training-set attacks ...
Despite the widespread use of machine learning in adversarial settings such as computer security, re...
In security-sensitive applications, the success of machine learning depends on a thorough vetting of...
Machine-learning techniques are widely used in securityrelated applications, like spam and malware d...
Machine learning is widely used in security-sensitive settings like spam and malware detection, alth...
In adversarial classification tasks like spam filtering, intrusion detection in computer networks an...
Machine learning models are vulnerable to evasion attacks, where the attacker starts from a correctl...
Machine learning based classification techniques are being used in a growing number of security appl...
This thesis presents and evaluates three mitigation techniques for evasion attacks against machine l...
Abstract—Learning-based classifiers are increasingly used for detection of various forms of maliciou...
In recent years, machine learning (ML) has become an important part to yield security and privacy in...
Abstract—Learning-based classifiers are increasingly used for detection of various forms of maliciou...
Over the last decade, machine learning (ML) and artificial intelligence (AI) solutions have been wid...
Pattern classifiers have been widely used in adversarial settings like spam and malware detection, ...
In recent years, machine learning algorithms have been applied widely in various fields such as heal...
We investigate a problem at the intersection of machine learning and security: training-set attacks ...
Despite the widespread use of machine learning in adversarial settings such as computer security, re...
In security-sensitive applications, the success of machine learning depends on a thorough vetting of...
Machine-learning techniques are widely used in securityrelated applications, like spam and malware d...
Machine learning is widely used in security-sensitive settings like spam and malware detection, alth...
In adversarial classification tasks like spam filtering, intrusion detection in computer networks an...
Machine learning models are vulnerable to evasion attacks, where the attacker starts from a correctl...
Machine learning based classification techniques are being used in a growing number of security appl...
This thesis presents and evaluates three mitigation techniques for evasion attacks against machine l...
Abstract—Learning-based classifiers are increasingly used for detection of various forms of maliciou...
In recent years, machine learning (ML) has become an important part to yield security and privacy in...
Abstract—Learning-based classifiers are increasingly used for detection of various forms of maliciou...
Over the last decade, machine learning (ML) and artificial intelligence (AI) solutions have been wid...
Pattern classifiers have been widely used in adversarial settings like spam and malware detection, ...
In recent years, machine learning algorithms have been applied widely in various fields such as heal...
We investigate a problem at the intersection of machine learning and security: training-set attacks ...
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