Kamran Shafi

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Rule-based intrusion detection systems generally rely on hand crafted signatures developed by domain experts. This could lead to a delay in updating the signature bases and potentially compromising the security of protected systems. In this paper, we present a biologically-inspired computational approach to dynamically and adaptively learn signatures for(More)
Evolutionary Learning Classifier Systems (LCSs) combine reinforcement learning or supervised learning with effective genetics-based search techniques. Together these two mechanisms enable LCSs to evolve solutions to decision problems in the form of easy to interpret rules called classifiers. Although LCSs have shown excellent performance on some data mining(More)
Machine learning techniques are frequently applied to intrusion detection problems in various ways such as to classify normal and intrusive activities or to mine interesting intrusion patterns. Self-learning rule-based systems can relieve domain experts from the difficult task of hand crafting signatures, in addition to providing intrusion classification(More)
The pervasiveness of the computing power has made it an inevitable commodity of the modern time. The inexorable technological advances clearly predict the continually increasing reliance of human life on the computing systems in the future. Intelligent portable devices are commonplace these days and information accessibility is ubiquitous. There is a(More)
Evolutionary Learning Classifier Systems (LCSs) are rule based systems that have been used effectively in concept learning. XCS is a prominent LCS that uses genetic algorithms and reinforcement learning techniques. In traditional machine learning, early stopping have been investigated extensively to an extent that it is now a default mechanism in many(More)