Kamran Shafi

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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)
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) 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)
— Recently some algorithms have been proposed to clean post-training rule populations evolved by XCS, a state of the art Learning Classifier System (LCS). We present an algorithm to extract optimal rules, which we refer to as signatures, during the operation of UCS, a recent variant of XCS. In a benchmark binary valued dataset our method seconds the(More)
An emerging body of research is focusing on understanding and building artificial systems that can achieve open-ended development influenced by intrinsic motivations. In particular, research in robotics and machine learning is yielding systems and algorithms with increasing capacity for self-directed learning and autonomy. Traditional software architectures(More)
Adversarial learning is a recently introduced term which refers to the machine learning process in the presence of an adversary whose main goal is to cause dysfunction to the learning machine. The key problem in adversarial learning is to determine when and how an adversary will launch its attacks. It is important to equip the deployed machine learning(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)