Over the last few years, the dramatic growth in video demand has inspired the service providers (e.g., Netflix and YouTube) to swing towards HTTP based Dynamic Adaptive Streaming (DASH). However, sustaining the adequate bandwidth claims over this rapid growth in multimedia content becomes a significant challenge for network operators. Considering the effectiveness of the next generation future Internet architecture, i.e., Name Data Networking (NDN), recently DASH over NDN is implemented. The fundamental characteristics of NDN, such as efficient content distribution and low bandwidth requirements, significantly increase the bandwidth utilization, which ensures the smooth delivery of multimedia content. However, we discovered that the above characteristics of NDN also opens the door for new vulnerabilities.
In this paper, first we propose a new attack termed as “Bitrate Oscillation Attack” (BOA), which disrupt the functionality of DASH protocol over NDN by exploiting its two key features called in-network caching and interest aggregation. In particular, BOA forces the DASH streaming system running at the honest client to oscillate in various video resolutions with high frequency and amplitude, within a single video session. Second, to mitigate the BOA, we design and implement a proactive countermeasure called “NC based DAS-NDN”. Our solution efficiently enables the network coding to DAS multimedia content and within NDN architecture. Thus, without any coordination between the network nodes reduces bitrate oscillations in the presence of BOA and NDN’s inherent content source variations. The performance evaluation performed on different target scenarios proves the effectiveness of our proposed attack, and the results also show the correctness of our proposed corresponding countermeasure. In particular, the result analysis shows that BOA increases the annoyance factor in spatial dimension of end-user, and our countermeasure greatly reduces the adverse effects of BOA and also make DAS friendly to NDN’s inherent features.
Journal: Computer Networks.
Date of Publication: 19 June, 2020.