移动视频业务应用广泛,流量占比高且持续增长.针对有限的移动网络带宽,如何合理地规划网络服务、提供优质的移动视频体验,需要客观的视频体验评估反馈网络服务提供商和视频服务运营商以改善网络利用率及传输方案.当前大多数视频服务质量评估方法都基于DPI(Deep Packet Inspection)方法获取视频播放信息以计算视频QoE(Quality of Experience).然而,为了保护用户隐私和网络安全,越来越多的视频采用HTTPS加密传输,使得传统的DPI方法无法获取码率和清晰度等QoE评估参数.因此,文中提出一种基于视频块统计特征的加密视频QoE参数识别方法(以代表性网络视频YouTube为例).首先,根据SSL/TLS协议握手过程中未加密部分识别HTTPS加密的YouTube流量.然后,根据视频流前若干个包的4种特征识别出HLS、DASH和HPD传输模式,再根据视频块统计特征建立机器学习模式识别视频块的码率和清晰度.实验结果表明该方法传输模式、码率和清晰度识别平均准确率分别达到98%、99%和98%,可以有效用于加密YouTube的QoE评估.
In order to detect web shells that hackers inject into web servers by exploiting system vulnerabilities or web page open sources, a novel web shell detection system based on the scoring scheme is proposed, named Evil-hunter. First, a large set of malicious function samples normally used in web shells are collected from various sources on the Internet and security forums. Secondly, according to the danger level and the frequency of using these malicious functions in the web shells as well as in legal web applications, an assigning score strategy for each malicious sample is devised. Then, the appropriate score threshold value for each sample is obtained from the results of a statistical analysis. Finally, based on the threshold value, a simple algorithm is presented to identify files that contain web shells in web applications. The experimental results show that compared with other approaches, Evil-hunter can identify web shells more efficiently and accurately.
