Market Assessment of an intelligent framework for social engineering cyber security training

社会工程网络安全培训智能框架的市场评估

• 批准号: 556923-2020
• 负责人: Rueda, Luis
• 金额: $ 1.09万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Idea to Innovation
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=706222
• 关键词: Market; Assessment; intelligent; framework; social

Human beings are the weakest link in cybersecurity, and are, in fact, more vulnerable than computers. In recent years, countless major security incidents have succeeded because of human errors and the lack of appropriate security training and awareness. In this regard, although there is a wide range of cybersecurity products in the market to protect computers and networks, almost none or a few immature products exist for developing and training users in cybersecurity awareness. Organizations are still relying on hands-on workshops, online courses, and traditional training methods for cybersecurity awareness, which are ineffective and unscalable. We have developed a new multilayer technology for cybersecurity awareness training to help organizations understand and mitigate potential security risks associated with social engineering threats. Our technology utilizes the principles of artificial intelligence, gamification theory and cybersecurity strategies for enabling organizations to implement a social engineering firewall. Using deep reinforcement learning algorithms, the proposed approach provides an estimation of potential social engineering threats for a given organization by measuring the digital footprint of the organization assets on public sources, including the Internet, social networks and media, among others. Based on the estimated social engineering threat, our technology constructs and executes an interactive anti-social engineering training program that combines both passive and active training. Our technology then creates and recommends a social engineering firewall security strategy that is used within the organization network to reduce and mitigate the risk of any potential social engineering threats. The main goal of this project is to conduct a market study for the proposed technology, which will be used to assess the market potential of the technology in anticipation of transferring it to existing cybersecurity companies.

人类是网络安全中最薄弱的环节，实际上比计算机更脆弱。 近年来，由于人为错误以及缺乏适当的安全培训和意识，无数重大安全事件得以成功。在这方面，虽然市场上有各种各样的网络安全产品来保护计算机和网络，但几乎没有或只有少数不成熟的产品用于培养和培训用户的网络安全意识。组织仍然依赖于实践研讨会，在线课程和传统的网络安全意识培训方法，这些方法效率低下且不可扩展。我们开发了一种用于网络安全意识培训的新多层技术，以帮助组织了解和减轻与社会工程威胁相关的潜在安全风险。我们的技术利用人工智能、游戏化理论和网络安全策略的原理，使组织能够实施社会工程防火墙。 使用深度强化学习算法，所提出的方法通过测量组织资产在公共资源（包括互联网、社交网络和媒体等）上的数字足迹，为给定组织提供了潜在社会工程威胁的估计。 根据估计的社会工程威胁，我们的技术构建并执行一个互动的反社会工程训练计划，结合了被动和主动训练。然后，我们的技术会创建并推荐一个社会工程防火墙安全策略，该策略在组织网络中使用，以减少和缓解任何潜在的社会工程威胁的风险。 该项目的主要目标是对拟议技术进行市场研究，以评估该技术的市场潜力，并将其转让给现有的网络安全公司。