text: Intrusion Detection Systems (IDSs) are becoming essential to protecting modern information infrastructures. The effectiveness of an IDS is directly related to the computational resources at its disposal. However, it is difficult to guarantee especially with an increasing demand of network capacity and rapid proliferation of attacks. On the other hand, modern intrusions often come as sequences of attacks to reach some predefined goals. It is therefore critical to identify the best default IDS configuration to attain the highest possible overall protection within a given resource budget. This paper proposes a game theory based solution to the problem of optimal signature-based IDS configuration under resource constraints. We apply the concepts of indices of power, namely, Shapley value and Banzhaf-Coleman index, from cooperative game theory to quantify the influence or contribution of libraries in an IDS with respect to given attack graphs. Such valuations take into consideration the knowledge on common attack graphs and experienced system attacks and are used to configure an IDS optimally at its default state by solving a knapsack optimization problem.
  @cite_0: When installing network intrusion detection systems (NIDSs), operators are faced with a large number of parameters and analysis options for tuning trade-offs between detection accuracy versus resource requirements. In this work we set out to assist this process by understanding and predicting the CPU and memory consumption of such systems.
summary: In @cite_0 , the authors investigate the prediction of resource consumption based on traffic profile. An interesting result, which we assume to be available in this paper, is that both CPU and memory usage can be predicted with a model linear in the number of connections. Equally important is the confirmation that the factoring of IDS resource usage with per-analyzer and per-connection scaling is a reasonable assumption. The authors use this finding to build an analyzer selection and configuration tool that estimates resource consumption per analyzer to determine whether a given configuration is feasible or not. The constraint used is a target CPU load below which the load should remain for a predefined percentage of time. The actual selection of a feasible analyzer set is however left as a manual task for the IDS operator. In our work, we propose a more informed and automated way of IDS configuration decision that takes into account the resource utilization per IDS library as well as the expected intrusion context based on experienced attack sequences or graphs.