Department of IT, Faculty of Computing and IT, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Department of IS, Faculty of Computing and IT, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Sensors (Basel). 2020 Sep 7;20(18):5075. doi: 10.3390/s20185075.
Monitoring what application or type of applications running on a computer or a cluster without violating the privacy of the users can be challenging, especially when we may not have operator access to these devices, or specialized software. Smart grids and Internet of things (IoT) devices can provide power consumption data of connected individual devices or groups. This research will attempt to provide insides on what applications are running based on the power consumption of the machines and clusters. It is therefore assumed that there is a correlation between electric power and what software application is running. Additionally, it is believed that it is possible to create power consumption profiles for various software applications and even normal and abnormal behavior (e.g., a virus). In order to achieve this, an experiment was organized for the purpose of collecting 48 h of continuous real power consumption data from two PCs that were part of a university computer lab. That included collecting data with a one-second sample period, during class as well as idle time from each machine and their cluster. During the second half of the recording period, one of the machines was infected with a custom-made virus, allowing comparison between power consumption data before and after infection. The data were analyzed using different approaches: descriptive analysis, F-Test of two samples of variance, two-way analysis of variance (ANOVA) and autoregressive integrated moving average (ARIMA). The results show that it is possible to detect what type of application is running and if an individual machine or its cluster are infected. Additionally, we can conclude if the lab is used or not, making this research an ideal management tool for administrators.
在不侵犯用户隐私的情况下，监控计算机或集群上运行的应用程序或应用程序类型可能具有挑战性，特别是当我们可能无法对这些设备或专用软件进行操作时。智能电网和物联网 (IoT) 设备可以提供连接的单个设备或设备组的功耗数据。本研究将尝试根据机器和集群的功耗提供有关正在运行的应用程序的信息。因此，假设电功率与正在运行的软件应用程序之间存在相关性。此外，人们相信可以为各种软件应用程序甚至正常和异常行为（例如病毒）创建功耗配置文件。为了实现这一目标，组织了一项实验，目的是从两台属于大学计算机实验室的 PC 中收集 48 小时的连续实时功耗数据。这包括在每台机器及其集群的上课时间和空闲时间以一秒为采样周期收集数据。在记录期的后半段，其中一台机器感染了一个自定义病毒，允许在感染前后比较功耗数据。使用不同的方法对数据进行了分析：描述性分析、两个方差样本的 F 检验、双向方差分析 (ANOVA) 和自回归积分移动平均 (ARIMA)。结果表明，有可能检测到正在运行的应用程序类型，以及单个机器或其集群是否受到感染。此外，我们可以确定实验室是否正在使用，使这项研究成为管理员的理想管理工具。
