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Users and network administrators need ways to filter email messages based primarily on the reputation of the sender. Unfortunately, conventional mechanisms for sender reputation—notably, IP blacklists—are cumbersome to maintain and evadable. This paper investigates ways to infer the reputation of an email sender based solely on network-level features,(More)
With the increase in the size of real-world databases, there is an ever-increasing need to scale up inductive learning algorithms. Incremental learning techniques are one possible solution to the scalability problem. In this paper, we propose three ctiteria to evaluate the robustness and reliability of incremental learning methods, and use them to study the(More)
Limited fidelity of software-based wireless network simulations has prompted many researchers to build testbeds for developing and evaluating their wireless protocols and mobile applications. Since most testbeds are tailored to the needs of specific research projects, they cannot be easily reused for other research projects that may have different(More)
As databases for real-world problems increase in size, there is a need in many situations to select and keep relevant training data for efficient storage and processing reasons. Support vector machines (SVMs) reportedly exhibit certain desirable properties in selecting and preserving useful training data as support vectors. This paper attempts to quantify(More)
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