Content Leakage Detection by Using Traffic Pattern for Trusted Content Delivery Networks by Comparing Videos of Different Lengths

Multimedia streaming applications and services are becoming popular in recent a year, thats why issue of trusted video delivery to prevent the undesirable content leakage become critical. The conventional Systems addressed this issue by proposing methods based on observation of streamed traffic throughout the network. The job of maintaining high detection accuracy while coping with traffic variation in the network is done by conventional system. But detection performance of conventional system degrades to the significant variation of length of the video. To overcoming this issue we are proposing a novel leakage detection of content scheme that is robust to the variation of the length of the video. In this, we focus on overcoming this issue by proposing a novel content-leakage detection scheme that is robust to the variation of length of the video. By comparing the length of different videos, we determine a relation between video length to be compared and the similarity between the videos which are compared. Thus, we enhance the detection performance of the proposed scheme even in an environment subjected to variation in length of video. Through a experiment, the effectiveness of our proposed scheme is evaluated in terms of variation of video length, variation in delays, packet and data loss.