Interactive Access Rule Learning: Generating Adapted Access Rule Sets
نویسندگان
چکیده
This paper tackles the problem of usability and security in access control mechanisms. A theoretical solution for this problem is presented using the combination of automatic rule learning and user interaction. The result is the interactive rule learning approach. Interactive rule learning is designed to complete attribute-based access control to generate concise rule sets even by non-expert end-users. The resulting approach leads to adaptive access control rule sets that can be used for smart products. Keywords-adaptivity; usability; access control; rule learning.
منابع مشابه
Interactive Rule Learning for Access Control: Concepts and Design
Nowadays the majority of users are unable to properly configure security mechanisms mostly because they are not usable for them. To reach the goal of having usable security mechanisms, the best solution is to minimize the amount of user interactions and simplify configuration tasks. Automation is a proper solution for minimizing the amount of user interaction. Fully automated security systems a...
متن کاملMMDT: Multi-Objective Memetic Rule Learning from Decision Tree
In this article, a Multi-Objective Memetic Algorithm (MA) for rule learning is proposed. Prediction accuracy and interpretation are two measures that conflict with each other. In this approach, we consider accuracy and interpretation of rules sets. Additionally, individual classifiers face other problems such as huge sizes, high dimensionality and imbalance classes’ distribution data sets. This...
متن کاملNEW CRITERIA FOR RULE SELECTION IN FUZZY LEARNING CLASSIFIER SYSTEMS
Designing an effective criterion for selecting the best rule is a major problem in theprocess of implementing Fuzzy Learning Classifier (FLC) systems. Conventionally confidenceand support or combined measures of these are used as criteria for fuzzy rule evaluation. In thispaper new entities namely precision and recall from the field of Information Retrieval (IR)systems is adapted as alternative...
متن کاملAn Adaptive Intrusion Detection System using a Data Mining Approach
Weak data dependencies in large databases coupled with poorly written web based applications are a major cause for malicious transactions. The problem of security becomes especially acute when access roles are changed among users. Also the poorly maintained data base caches are a cause for added security leaks. We propose an adaptive Intrusion detection system to keep track of the varying data ...
متن کاملOn Mining Fuzzy Classification Rules for Imbalanced Data
Fuzzy rule-based classification system (FRBCS) is a popular machine learning technique for classification purposes. One of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. However many cases the minority classes are more important than the majority ones. In this paper, we have extended ...
متن کامل