随着网络技术的飞速发展,各种各样的应用以及网络中的异常流量对网络安全和QoS不断带来巨大的威胁;因此,通过有效的技术手段,管理和控制网络中的各种业务流量,是当前网络运营中面临的主要挑战之一;传统的流量分类以及入侵检测技术依赖...随着网络技术的飞速发展,各种各样的应用以及网络中的异常流量对网络安全和QoS不断带来巨大的威胁;因此,通过有效的技术手段,管理和控制网络中的各种业务流量,是当前网络运营中面临的主要挑战之一;传统的流量分类以及入侵检测技术依赖于复杂的特征提取甚至用户的隐私信息;由于互联网网络带宽的不断提高以及应用层协议越来越复杂,加密技术的不断发展,以及用户隐私问题越来越受重视等,现有的技术已经很难适应当今网络技术和应用的发展需求;近年来深度学习的广泛应用为流量分类领域提供了新的思路,在此基础上,我们利用卷积神经网络(Convolutional Neural Networks,CNN)、长短时记忆(Long Short Term Memory,LSTM)和堆栈自编码(Stacked Auto Encoder,SAE)三种深度学习算法构建了一个能够对网络特征进行自主选择的流量分类架构,并且无需依赖用户的隐私信息;实验结果表明,该流量分类架构与现有基于传统机器学习的流量分类方法相比,其分类精度和F1_Score分别有13.8%和14.3%的改善,而且对存储资源的需求也大大降低。展开更多
In communication networks with policy-based Transport Control on-Demand (TCoD) function,the transport control policies play a great impact on the network effectiveness. To evaluate and optimize the transport policies ...In communication networks with policy-based Transport Control on-Demand (TCoD) function,the transport control policies play a great impact on the network effectiveness. To evaluate and optimize the transport policies in communication network,a policy-based TCoD network model is given and a comprehensive evaluation index system of the network effectiveness is put forward from both network application and handling mechanism perspectives. A TCoD network prototype system based on Asynchronous Transfer Mode/Multi-Protocol Label Switching (ATM/MPLS) is introduced and some experiments are performed on it. The prototype system is evaluated and analyzed with the comprehensive evaluation index system. The results show that the index system can be used to judge whether the communication network can meet the application requirements or not,and can provide references for the optimization of the transport policies so as to improve the communication network effectiveness.展开更多
文摘随着网络技术的飞速发展,各种各样的应用以及网络中的异常流量对网络安全和QoS不断带来巨大的威胁;因此,通过有效的技术手段,管理和控制网络中的各种业务流量,是当前网络运营中面临的主要挑战之一;传统的流量分类以及入侵检测技术依赖于复杂的特征提取甚至用户的隐私信息;由于互联网网络带宽的不断提高以及应用层协议越来越复杂,加密技术的不断发展,以及用户隐私问题越来越受重视等,现有的技术已经很难适应当今网络技术和应用的发展需求;近年来深度学习的广泛应用为流量分类领域提供了新的思路,在此基础上,我们利用卷积神经网络(Convolutional Neural Networks,CNN)、长短时记忆(Long Short Term Memory,LSTM)和堆栈自编码(Stacked Auto Encoder,SAE)三种深度学习算法构建了一个能够对网络特征进行自主选择的流量分类架构,并且无需依赖用户的隐私信息;实验结果表明,该流量分类架构与现有基于传统机器学习的流量分类方法相比,其分类精度和F1_Score分别有13.8%和14.3%的改善,而且对存储资源的需求也大大降低。
基金Supported by the National 863 Program (No.2007AA-701210)
文摘In communication networks with policy-based Transport Control on-Demand (TCoD) function,the transport control policies play a great impact on the network effectiveness. To evaluate and optimize the transport policies in communication network,a policy-based TCoD network model is given and a comprehensive evaluation index system of the network effectiveness is put forward from both network application and handling mechanism perspectives. A TCoD network prototype system based on Asynchronous Transfer Mode/Multi-Protocol Label Switching (ATM/MPLS) is introduced and some experiments are performed on it. The prototype system is evaluated and analyzed with the comprehensive evaluation index system. The results show that the index system can be used to judge whether the communication network can meet the application requirements or not,and can provide references for the optimization of the transport policies so as to improve the communication network effectiveness.
