Front-end Design Framework for Analytics of Risky Behaviour in Corporate Smartphones

随着智能手机等移动设备变得越来越普遍和普及，这样的设备 受到安全威胁和恶意入侵。高风险用户行为、移动设备丢失或 第三方对设备的攻击可能会导致敏感信息泄露并损坏关键设备 基础设施，特别是当这种设备被公司员工用于与工作相关的和 个人目的。移动设备行为分析可以成为捕获、分析和预测的强大工具 威胁，以便检测和防止对这类设备的入侵。以前的许多研究都集中在 从蜂窝网络提供商的角度分析和提取业务模式的用户行为， 应用程序开发人员和操作系统供应商。这项提案解决的正是这一研究缺口。 这项建议的目的，是研究为新机场建立设计架构的第一步。 分析企业智能手机中的高风险用户行为。这项提案涉及的研究问题包括 以下内容： 1.Android和iOS设备可以收集哪些移动设备行为分析数据？ 2.如何在轻量级应用程序中以最佳方式捕获数据以发送到分析引擎？ 该项目将对智能手机用户可用的当前技术和技术进行详细分析 分析，调查可以从智能手机用户行为分析中提取哪些类型的数据，确定 可用作高风险预测因素的数据入围列表，并对以下机制提出建议 可以作为前端分析引擎设计的可行解决方案。该项目针对的是 对于行业合作伙伴，即Nova Communications，朝着部署和 移动设备行为分析引擎的商业化。

As mobile devices such as smartphones become increasingly ubiquitous and pervasive, such devices are subject to security threats and malicious intrusions. High risk user behaviour, loss of the mobile device, or an attack on the device by a third party can lead to leakage of sensitive information and damage to critical infrastructure, especially when such devices are used by corporate employees for both work-related and personal purposes. Mobile device behaviour analytics can be a powerful tool to capture, analyze and predict threats in order to detect and prevent intrusions on such devices. Much of the previous research has focused on user behavior analysis and extraction of traffic patterns from the perspective of cellular network providers, application developers and operating system vendors. It is this research gap that is addressed by this proposal. The objective of this proposal is to conduct a study on the first step in building a design framework for the analytics of risky user behavior in corporate smartphones. The research questions addressed in this proposal are the following: 1.What mobile device behavior analytics data can be collected from Android and iOS devices? 2.How can data be optimally captured in a lightweight application to send to an analytics engine? The project will make a detailed analysis of current techniques and technologies available for smartphone user analytics, investigate what types of data can be extracted from smartphone user behavior analytics, identify a shortlist of data that can be used as predictors for high risk, and make recommendations of mechanisms that could be used as viable solutions for the design of the front end analytics engine. This project addresses an important first step for the industry partner, namely, Nova Communications, towards the deployment and commercialization of a mobile device behaviour analytics engine.