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Network traffic classification gains continuous interesting while many applications emerge on the different kinds of networks with obfuscation techniques. Decision tree is a supervised machine learning method used widely to identify and classify network traffic. In this paper, we introduce a comparative study focusing on two common decision tree methods namely: C4.5 and Random forest. The study offers comparative results in two different factors are accuracy of classification and processing time. C4.5 achieved high percentage of classification accuracy reach to 99.67 for 24000 instances while Random Forest was faster than C4.5 in term of processing time.

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Device power consumption is a serious design consideration especially for embedded systems. By reducing the power consumption of a particular system, we could effectively prolong the runtime of the system, allowing for longer operational condition of a particular system. Previous studies have suggested that the power characteristics of a modern embedded processor have since been improved with manufacturer's implementation of better energy-focused designs. Implementation of hardware optimization such as better clock and power gating have been shown to produce better energy usage during on-load and off-load processing.In this paper we benchmarked the energy use of a modern embedded processor and study the effects of idling time to the processor and system energy usage. We have found that the processor energy use is significantly reduced in the instant that the processor goes idle during the execution process. The idling time during a processing timeslice allows the processor to use significantly less energy without explicitly depending on a frequency scaling algorithm to reduce energy consumption. This power saving feature directly implemented inside the processor hardware have the possibility to render software based frequency scaling algorithm and DVFS method to be less effective in reducing energy usage.