Internet attacks pose a severe threat to most of the online resources and are a prime concern of security administrators these days. In spite of many efforts, the security techniques are unable to detect the intrusions accurately. Most of the methods suffer from the limitations of a high false positive rate, low detection rate and provide one solution which lacks the classification trade-offs. In this work, an effective two-stage method is proposed to produce a pool of non-dominating solutions or Pareto optimal solutions as base models and their ensembles for detecting the intrusions accurately. It generates Pareto optimal solutions to a chromosome structure in stage 1 formulating Pareto front. Whereas, another approximation to the Pareto front of optimal solutions is made to obtain non-dominating ensembles in the second stage. The final prediction ensemble solutions are computed from individual predictions using majority voting approach. Applicability of the suggested method is validated using benchmark dataset NSL-KDD dataset. The experimental results show that the recommended method provides better results than conventional ensemble techniques. The recommended method is also adequate to generate Pareto optimal solutions that address the issue of improving detection accuracy for minority as well as majority attack classes along with handling classification tradeoff problem. The proposed method resulted detection accuracy of 97% with FPR of 2% for KDD dataset respectively. The most attractive feature of the proposed method is that both generation of base classifier and their ensemble thereof are multi-objective in nature addressing the issue of low detection accuracy and classification tradeoffs.

Internet攻击对大多数在线资源都构成了严重威胁，并且是当今安全管理员最关心的问题。尽管进行了许多努力，安全技术仍无法准确检测到入侵。大多数方法受假阳性率高，检测率低的局限，并且提供了一种缺乏分类权衡的解决方案。在这项工作中，提出了一种有效的两阶段方法，以生成一组非支配解或Pareto最优解作为基础模型及其集合，以准确地检测入侵。它在阶段1的帕累托前沿中生成染色体结构的帕累托最优解。而对最优解的Pareto前沿进行了另一种近似，以在第二阶段获得非主导的合奏。使用多数表决方法根据单个预测来计算最终预测集合解。使用基准数据集NSL-KDD数据集验证了所建议方法的适用性。实验结果表明，推荐方法比常规集成技术提供了更好的结果。推荐的方法也足以生成帕累托最优解，以解决提高少数族裔和多数攻击类的检测准确性以及处理分类权衡问题的问题。该方法对KDD数据集的检测精度分别为97％和FPR为2％。