论文标题
深度学习的应用以增强现代计算机网络中入侵检测的准确性
Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks
论文作者
论文摘要
在本文中研究了深度学习以增强现代计算机网络中入侵检测准确性的应用。根据学习阶段使用的信息，将计算机网络中攻击的识别分为两类入侵检测和异常检测。入侵检测同时使用常规流量和攻击流量。异常检测方法试图建模系统的正常行为，任何违反该模型的事件都被认为是一种可疑行为。例如，如果通常是被动的Web服务器，则试图有许多地址可能被蠕虫感染。异常的诊断方法是统计模型，安全系统方法，审核协议，检查文件，创建白色列表，神经网络，遗传算法，矢量机，决策树。我们的结果表明，我们的方法提供了高度的准确性，精确度和召回时间，并减少了训练时间。在我们未来的工作中，改进的探索途径将是评估和扩展模型处理零日攻击的能力。
Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks were studied in this paper. The identification of attacks in computer networks is divided in to two categories of intrusion detection and anomaly detection in terms of the information used in the learning phase. Intrusion detection uses both routine traffic and attack traffic. Abnormal detection methods attempt to model the normal behavior of the system, and any incident that violates this model is considered to be a suspicious behavior. For example, if the web server, which is usually passive, tries to There are many addresses that are likely to be infected with the worm. The abnormal diagnostic methods are Statistical models, Secure system approach, Review protocol, Check files, Create White list, Neural Networks, Genetic Algorithm, Vector Machines, decision tree. Our results have demonstrated that our approach offers high levels of accuracy, precision and recall together with reduced training time. In our future work, the first avenue of exploration for improvement will be to assess and extend the capability of our model to handle zero-day attacks.
