Pattern recognition systems are increasingly being used in adversarial environments like
network intrusion detection, spam filtering and biometric authentication and verification …

Pattern classification systems are commonly used in adversarial applications, like biometric
authentication, network intrusion detection, and spam filtering, in which data can be …

In many security applications a pattern recognition system faces an adversarial classification
problem, in which an intelligent, adaptive adversary modifies patterns to evade the classifier …

Pattern classification systems are currently used in security applications like intrusion
detection in computer networks, spam filtering and biometric identity recognition. These are …

Pattern recognition systems have been widely used in adversarial classification tasks like
spam filtering and intrusion detection in computer networks. In these applications a …

Most existing machine learning classifiers are highly vulnerable to adversarial examples. An
adversarial example is a sample of input data which has been modified very slightly in a way …

Pattern recognition and machine learning techniques have been increasingly adopted in
adversarial settings such as spam, intrusion, and malware detection, although their security …

Learning-based classifiers are found to be vulnerable to attacks by adversarial samples.
Some works suggested that ensemble classifiers tend to be more robust than single …

In adversarial classification tasks like spam filtering and intrusion detection, malicious
adversaries may manipulate data to thwart the outcome of an automatic analysis. Thus …

Classification techniques are widely used in security settings in which data can be
deliberately manipulated by an adversary trying to evade detection and achieve some …