论文标题
使用高级学习者合奏的自主入侵检测系统
An Autonomous Intrusion Detection System Using an Ensemble of Advanced Learners
论文作者
论文摘要
入侵检测系统（IDS）是现代计算机网络的重要安全组成部分。随着使用基于计算机网络的基础架构的敏感服务量增加，IDS需要更加智能和自主。除了自治之外，IDS的另一个重要功能是其检测零日攻击的能力。为了解决这些问题，在本文中，我们提出了一个ID，以减少手动互动的数量和需要的专家知识，并能够在零日攻击下产生可接受的性能。我们的方法是并行使用三种学习技术：封闭式复发单元（GRU），卷积神经网络作为深度技术，而随机森林作为合奏技术。这些系统是并行培训的，结果是在两个逻辑下合并的：多数投票和“或”逻辑。我们使用NSL-KDD数据集来验证我们提出的系统的熟练程度。仿真结果表明，该系统有可能在零日攻击下以非常低的技术人员相互作用进行操作。我们在NSL-KDD的“ KDDTEST+”数据集上获得了87：28％的精度，并且在较低的培训时间和较低所需的计算资源的情况下，具有挑战性的“ KDDTest-21”的精度为76：61％。
An intrusion detection system (IDS) is a vital security component of modern computer networks. With the increasing amount of sensitive services that use computer network-based infrastructures, IDSs need to be more intelligent and autonomous. Aside from autonomy, another important feature for an IDS is its ability to detect zero-day attacks. To address these issues, in this paper, we propose an IDS which reduces the amount of manual interaction and needed expert knowledge and is able to yield acceptable performance under zero-day attacks. Our approach is to use three learning techniques in parallel: gated recurrent unit (GRU), convolutional neural network as deep techniques and random forest as an ensemble technique. These systems are trained in parallel and the results are combined under two logics: majority vote and "OR" logic. We use the NSL-KDD dataset to verify the proficiency of our proposed system. Simulation results show that the system has the potential to operate with a very low technician interaction under the zero-day attacks. We achieved 87:28% accuracy on the NSL-KDD's "KDDTest+" dataset and 76:61% accuracy on the challenging "KDDTest-21" with lower training time and lower needed computational resources.
