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Friday, 23 February 2018

Security Evaluation of Pattern Classifiers under Attack(2015)

Security Evaluation of Pattern Classifiers

under Attack(2015)

ABSTRACT: Pattern classification systems are commonly used in adversarial applications, like biometric authentication, network intrusion detection, and spam filtering, in which data can be purposely manipulated by humans to undermine their operation. As this adversarial scenario is not taken into account by classical design methods, pattern classification systems may exhibit vulnerabilities, whose exploitation may severely affect their performance, and consequently limit their practical utility. Extending pattern classification theory and design methods to adversarial settings is thus a novel and very relevant research direction, which has not yet been pursued in a systematic way. In this paper, we address one of the main open issues: evaluating at design phase the security of pattern classifiers, namely, the performance degradation under potential attacks they may incur during operation. We propose a framework for empirical evaluation of classifier security that formalizes and generalizes the main ideas proposed in the literature, and give examples of its use in three real applications. Reported results show that security evaluation can provide a more complete understanding of the classifier’s behavior in adversarial environments, and lead to better design choices
Pattern classification systems based on classical theory and design methods do not take into account adversarial settings; they exhibit vulnerabilities to several potential attacks, allowing adversaries to undermine their effectiveness. A systematic and unified treatment of this issue is thus needed to allow the trusted adoption of pattern classifiers in adversarial environments, starting from the theoretical foundations up to novel design methods, extending the classical design cycle of. In particular, three main open issues can be identified: (i) analyze the vulnerabilities of classification algorithms, and the corresponding attacks. (ii) Developing novel methods to assess classifier security against these attacks, which are not possible using classical performance evaluation methods. (iii) Developing novel design methods to guarantee classifier security in adversarial environments.   
1. Poor analyzing the vulnerabilities of classification algorithms, and the corresponding attacks.
2. A malicious webmaster may manipulate search engine rankings to artificially promote website.
In this work we address issues above by developing a framework for the empirical evaluation of classifier security at design phase that extends the model selection and performance evaluation steps of the classical design cycle .We summarize previous work, and point out three main ideas that emerge from it. We then formalize and generalize them in our framework. First, to pursue security in the context of an arms race it is not sufficient to react to observed attacks, but it is also necessary to proactively anticipate the adversary by predicting the most relevant, potential attacks through a what-if analysis; this allows one to develop suitable countermeasures before the attack actually occurs, according to the principle of security by design. Second, to provide practical guidelines for simulating realistic attack scenarios, we define a general model of the adversary, in terms of her goal, knowledge, and capability, which encompass and generalize models proposed in previous work. Third, since the presence of carefully targeted attacks may affect the distribution of training and testing data separately, we propose a model of the data distribution that can formally characterize this behaviour, and that allows us to take into account a large number of potential attacks; we also propose an algorithm for the generation of training and testing sets to be used for security evaluation, which can naturally accommodate application-specific and heuristic techniques for simulating attacks.
1. Proposed system prevents developing novel methods to assess classifier security against these attacks.
2. The presence of an intelligent and adaptive adversary makes the classification problem highly non-stationary.
1.    Attack Scenario and Model of the Adversary
2.    Pattern Classification
3.    Adversarial classification:
4.    Security modules
Attack Scenario and Model of the Adversary:
Although the definition of attack scenarios is ultimately an application-specific issue, it is possible to give general guidelines that can help the designer of a pattern recognition system. Here we propose to specify the attack scenario in terms of a conceptual model of the adversary that encompasses, unifies, and extends different ideas from previous work. Our model is based on the assumption that the adversary acts rationally to attain a given goal, according to her knowledge of the classifier, and her capability of manipulating data. This allows one to derive the corresponding optimal attack strategy.
Pattern Classification:
Multimodal biometric systems for personal identity recognition have received great interest in the past few years. It has been shown that combining information coming from different biometric traits can overcome the limits and the weaknesses inherent in every individual biometric, resulting in a higher accuracy. Moreover, it is commonly believed that multimodal systems also improve security against Spoofing attacks, which consist of claiming a false identity and submitting at least one fake biometric trait to the system (e.g., a “gummy” fingerprint or a photograph of a user’s face). The reason is that, to evade multimodal system, one expects that the adversary should spoof all the corresponding biometric traits. In this application example, we show how the designer of a multimodal system can verify if this hypothesis holds, before deploying the system, by simulating spoofing attacks against each of the matchers.
Adversarial classification:
Assume that a classifier has to discriminate between legitimate and spam emails on the basis of their textual content, and that the bag-of-words feature representation has been chosen, with binary features denoting the occurrence of a given set of words
Security modules:
Intrusion detection systems analyze network traffic to prevent and detect malicious activities like intrusion attempts, ROC curves of the considered multimodal biometric system under a simulated spoof attack against the fingerprint or the face matcher. Port scans, and denial-of-service attacks. When suspected malicious traffic is detected, an alarm is raised by the IDS and subsequently handled by the system administrator. Two main kinds of IDSs exist: misuse detectors and anomaly-based ones. Misuse detectors match the analyzed network traffic against a database of signatures of known malicious activities. The main drawback is that they are not able to detect never-before-seen malicious activities, or even variants of known ones. To overcome this issue, anomaly-based detectors have been proposed. They build a statistical model of the normal traffic using machine learning techniques, usually one-class classifiers, and raise an alarm when anomalous traffic is detected. Their training set is constructed, and periodically updated to follow the changes of normal traffic, by collecting unsupervised network traffic during operation, assuming that it is normal (it can be filtered by a misuse detector, and should)
 System                          :         Pentium IV 2.4 GHz.
 Hard Disk                      :         40 GB.
 Floppy Drive                 :         1.44 Mb.
 Monitor                         :         15 VGA Colour.
 Mouse                            :         Logitech.
 Ram                               :         512 Mb.
 Operating system           :         Windows XP/7.
 Coding Language :         JAVA/J2EE
 IDE                      :         Netbeans 7.4
 Database              :         MYSQL

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