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Intrusion detection systems (IDSs) must maximize the realization of security goals while minimizing costs. In this paper, we study the problem of building cost-sensitive intrusion detection models. We examine the major cost factors associated with an IDS, which include development cost, operational cost, damage cost due to successful intrusions, and the cost of manual and automated response to intrusions.These cost factors can be qualified according to a defined attack taxonomy and site-specific security policies and priorities. We define cost models to formulate the total expected cost of an IDS, and present cost-sensitive machine learning techniques that can produce detection models that are optimized for user-defined cost metrics. Empirical experiments show that our cost-sensitive modeling and deployment techniques are effective in reducing the overall cost of intrusion detection.

Highlights d We describe a new early Cambrian fossil with a polypoid body plan from Chengjiang d It is related to the supposed cnidarian Xianguangia and the iconic Dinomischus d The fossils possess very large ciliary structures otherwise seen only in ctenophores d We show that these fossils form a grade of stem-group ctenophores

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