Dynamic latency sensitivity recognition: An application to energy saving

In the world of connected everything, network attacks and cyber-security breaches may cause huge monetary damages and even endanger lives; hence, full sanitization of the Internet traffic is a real necessity. In this paper we will apply a dynamic statistical analysis to separate latency sensitive traffic from the latency insensitive one at the source. Then, we will calculate the energy savings that can be achieved by identifying and dropping all the unwanted portion of the latency insensitive traffic directly at the source. This value represents an upper-bound to the actual amount of energy that can be saved by applying our adaptive aggressive intrusion detection technique to latency insensitive traffic, in fact the actual value depends on the actual load of the network and its capability to spread the hunt for malicious packet among all the network nodes. The main contribution of this paper is to show that energy savings through aggressive intrusion detection may be achieved without burdening latency sensitive traffic with delays that may render it unusable, nonetheless, as a side effect of early removal of unwanted traffic from the network flows is to reduce the network load, the traffic reduction so obtained allows sanitizing even the latency sensitive traffic with a reduced risk of excessive delays due to resources allocation and traffic forecasting errors.