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In recent times, there has been a dramatic shift from bulk spam emails to targeted email phishing campaigns. Such attacks have started to cause huge brand, financial and operational damage to organisations globally. Phishing attacks involve simple, straightforward, masquerading methodology. 1 The aim is to lure and trick an unsuspecting victim in order to elicit as much information as possible, using SMS, email, WhatsApp and other messaging services, or phone calls that have been spoofed to appear is if they are from known, reliable friends or colleagues. 2

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Recent technological advancements have enabled mobile devices to provide mobile users with substantial capability and accessibility. Energy is evidently one of the most critical resources for such devices; in spite of the substantial gain in popularity of mobile devices, such as smartphones, their utility is severely constrained by battery life. Mobile users are very interested in accessing the Internet while it is one of the most expensive operations in terms of energy and cost. HTTP/2 has been proposed and accepted as the new standard for supporting the World Wide Web. HTTP/2 is expected to offer better performance, such as reduced page load time. Consequently, from the mobile users point of view, question arises: Does HTTP/2 offer improved energy consumption performance achieving longer battery life? In this paper, we compare the energy consumption of HTTP/2 with its predecessor (i.e., HTTP/1.1) using a variety of real world and synthetic test scenarios. We also investigate how Transport Layer Security (TLS) impacts the energy consumption of the mobile devices. Our study suggests that Round Trip Time (RTT) is one of the biggest factors in deciding how advantageous is HTTP/2 compared to HTTP/1.1. We conclude that for networks with higher RTTs, HTTP/2 has better energy consumption performance than HTTP/1.1.

The first challenge to develop an energy efficient application is to measure the application's energy consumption, which requires sophisticated hardware infrastructure and sig nificant amounts of developers' time. Models and tools that estimate software energy consumption can save developers time, as application profiling is much easier and more widely available than hardware instrumentation for measuring software energy consumption. Our work focuses on modelling software energy consumption by using system calls and machine learning tech niques. This system call based model is validated against actual energy measurements from five different Android applications.These results demonstrate that system call counts can successfully model software energy consumption if the idle energy consump tion of an application is estimated or known. In the absence of any knowledge of an application's idle energy consumption, our system call based approach is still useful to compare the energy consumption among different versions of the same application.

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