Evaluation of classifiers: Practical considerations for security applications

36 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Security Evaluation of Pattern Classifiers under Attack (2014, background): “three main open issues can be identified: (i) analyzing the vulnerabilities of classification algorithms, and the corresponding attacks [11], [14], [15]; (ii) developing novel methods to assess classifier security against these attacks, which is not possible using classical performance evaluation me”
Multiple classifier systems for robust classifier design in adversarial environments (2010, background): “Evaluating classifier robustness in adversarial classification tasks is a complex issue, which is outside the scope of this paper, and is the subject of an ongoing work [13,37], also by the authors.”
Malware Detection in Adversarial Settings: Exploiting Feature Evolutions and Confusions in Android Apps (2017, methodology): “To enable the proactive design of malware detectors, existing work [16, 11, 19, 13] has been proposed to evaluate malware detectors in adversarial seings.”
Identifying Security Evaluation of PatternClassifiers Under attack (2015, unknown): “An intrinsic limitation of our work is that security evaluation is carried out empirically, and it is thus data dependent; on the other hand, model-driven analyses [12], [17], [38] require a full analytical model of the problem and of the adversary’s behavior, that may be very difficult to develop f”