Network packet classification is the central building block for important services such as QoS routing and firewalling. Accordingly, a wide range of classification schemes has been proposed, each with its own specific...
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ISBN:
(纸本)9781467397414
Network packet classification is the central building block for important services such as QoS routing and firewalling. Accordingly, a wide range of classification schemes has been proposed, each with its own specific set of characteristics. But while novel algorithms keep being developed at a high pace, there barely exists tool support for proper benchmarking, which makes it hard for researchers and engineers to evaluate and compare those algorithms in changing scenarios. In this paper, we present the Classification algorithm Testing Environment (CATE). CATE consistently and reproducibly extracts the key performance characteristics, such as memory footprint and matching speed, for a predefined set of classification algorithms from a highly customizable set of benchmarks. In addition, we demonstrate that CATE can be used to gain new insights on both the input parameter sensitivity and the scalability of even well-studied algorithms.
In this paper, we consider the multi-criteria inventory classification problem. We propose a new classification algorithm referred to as Constructive_Order_Classification_algorithm (COCA). This algorithm is based on s...
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ISBN:
(纸本)9781467358125
In this paper, we consider the multi-criteria inventory classification problem. We propose a new classification algorithm referred to as Constructive_Order_Classification_algorithm (COCA). This algorithm is based on some simple priority rules and aims to standardize the classification and provide relative stability in the classification through a consensus process. An illustrative example is used to explain the different steps of the proposed algorithm.
With the rapid development of the Internet, Internet security is becoming an important problem recently. Therefore, many techniques for intrusion detection have been proposed to protect networks effectively. In this p...
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ISBN:
(纸本)9781467322591
With the rapid development of the Internet, Internet security is becoming an important problem recently. Therefore, many techniques for intrusion detection have been proposed to protect networks effectively. In this paper, a new classification model, named classification with average matching degree and gaussian function, is proposed and combined with the class association rule mining of Genetic Network Programming (GNP). The proposed classification algorithm can efficiently classify a new access data into a class of normal, misuse or anomaly. The simulations are based on NSL-KDD data set.
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