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Among the various strategies proposed for reducing or eliminating bias against small flows (in the presence of large flows), most require to identify and distinguish between small and large flows, besides having to track the ongoing sizes of all flows. Though these solutions do improve the response times of small flows (with negligible affect on the response times of large flows), they are not scalable with increasing traffic. In this context, we propose a new spike-detecting AQM that exploits TCP property in detecting large `spikes', and hence large flows, from which packets are dropped, and importantly, only at times of congestion. We discuss two such AQM policies using spike-detection for improving the performance of small flows: one that drops packets deterministically, and other that drops packets randomly. We show, using simulations, by comparing a number of metrics, that these new policies, in particular the one that drops packets randomly, out-performs not only the traditional drop-tail buffer with FCFS server, but also the RED policy as well as a size-based scheduler (proposed specifically for improving the response time of small flows). The improvement in performance becomes more revealing in scenario where the router buffer is small (less than one-tenth of the bandwidth-delay-product).