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

企业智能手机风险行为分析的前端设计框架

• 批准号: 485068-2015
• 负责人: Sampalli, Srinivas
• 金额: $ 1.82万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=584701
• 关键词: Front; end; Design; Framework; Analytics

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.

随着智能手机等移动设备变得越来越普遍和普及，此类设备 遭受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导致敏感信息泄露和关键设备损坏 基础设施，特别是当企业员工将此类设备用于工作相关和 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，以检测和防止对此类设备的入侵。之前的大部分研究都集中在 从蜂窝网络提供商的角度分析用户行为并提取流量模式， 应用程序开发商和操作系统供应商。该提案正是解决了这一研究空白。 本提案的目的是对构建设计框架的第一步进行研究 分析企业智能手机中的危险用户行为。本提案解决的研究问题是 下列： 1.可以从Android和iOS设备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并将其发送到分析引擎？ 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 可用作高风险预测指标的数据候选清单，并提出机制建议 可以用作前端分析引擎设计的可行解决方案。该项目解决了 对于行业合作伙伴 Nova Communications 而言，迈向部署和部署的重要第一步 移动设备行为分析引擎的商业化。