Towards Actionable Security Guidelines for IoT Compliance Auditing and Integration with Trustworthiness

在过去几年中，物联网(IoT)设备的部署显著增加。然而，安全已经落后，针对物联网设备的攻击(例如，使用智能烤箱的纵火、通过智能锁入室盗窃)的攻击数量不断增加就是明证，这可能会导致与针对传统设备的攻击一样严重的后果，更不用说有时甚至更严重了。如今，尽管计算能力相对较弱，但物联网设备往往更受信任，可以处理更多个人和安全关键数据。这些攻击的常见原因包括但不限于软件漏洞和错误配置。另一个重要因素是物联网设备生命周期的多方模型，例如，供应商/开发人员留下的默认设置可能与不了解安全的用户的情况不匹配。尽管已有努力，但缓解此类安全威胁仍然具有挑战性，主要原因如下：1)当前的物联网安全建议(如NIST、ENISA)级别太高，无法产生可执行/审计的项目；2)这些建议没有使用系统级数据表达，以便物联网设备的开发者和制造商可以轻松地将其集成到设备实施中；3)大多数物联网设备缺乏足够的存储和计算能力来进行合规性审计(例如，使用正式方法)；4)从那些频繁生成/收集敏感信息的设备收集数据会带来新的隐私问题。这个拟议的计划旨在克服这些挑战，并为物联网设备开发一个人工智能增强的安全框架，以便以自动方式受益于安全建议/标准。该计划的目标是：(I)通过弥合高级建议和低级别系统技术之间的差距，得出可操作的安全指南；(Ii)克服资源限制和对数据共享的隐私担忧；(Iii)为在开发/制造阶段整合这些指南或对其进行用户选择的合规性审计提供可信度保证。为了实现这些目标，拟议的研究将通过几个主要研究项目进行。首先，我们将构建一个自动化解决方案，通过应用自然语言处理(NLP)技术，将高级建议转换为更具体、可操作的安全指南，以适应异构物联网平台。其次，我们将设计一种合规性审计方法，该方法使用非敏感数据(例如设计规范)学习类似设备的群集，然后在本地利用敏感数据(例如传感器数据)以联合方式执行实际审计，以保护隐私。第三，我们将开发一种自动化技术来生成具体的集成输入，例如脚本、模板和策略，以便开发人员/制造商可以轻松地应用这些可操作的指导方针，并主动确保合规性(如果他们选择)。第二次和第三次推进都将尝试利用当前基于硬件的技术(例如，可信计算)。建议的研究结果将有助于确保物联网环境的安全，并可用于为即将到来的物联网时代设计可行的采用计划(例如，物联网安全教育、定义安全策略)。

There has been a significant increase in the deployments of the Internet of Things (IoT) devices over the last few years. Security, however, has lagged behind, as evidenced by the increasing number of attacks that target IoT devices (e.g., an arson that uses a smart oven, burglary via a smart lock), which may lead to as severe consequences as, not to mention sometimes more than, those targeting conventional devices. Nowadays, IoT devices tend to be trusted with more personal and security-critical data, although with relatively less computing power. Common causes of those attacks include but are not limited to software vulnerabilities and misconfigurations. Another important contributor is the multiparty model of the IoT device lifecycle, e.g., what the vendor/developer left as "defaults" might not match a security-unaware user's situation. Despite existing efforts, mitigating such security threats still remains challenging mainly for the following reasons: 1) current IoT security recommendations (e.g., NIST, ENISA) are too high-level to produce actionable items for enforcement/auditing; 2) those recommendations are not also expressed using system-level data so that the developers and manufacturers of IoT devices can easily integrate them into the device implementation; 3) most IoT devices lack sufficient storage and computational capabilities to conduct compliance auditing (e.g., using formal methods); and 4) collecting data from those devices, which frequently generate/collect sensitive information, introduces new privacy concerns.This proposed program seeks to overcome those challenges and develop an AI-enhanced security framework for IoT devices to benefit from the security recommendations/standards in an automated manner. The objectives of this program are to: (i) derive actionable security guidelines by bridging the gap between the high-level recommendations and low-level system technicality; (ii) overcome the resource constraint and privacy concerns about data sharing; (iii) provide trustworthiness assurance for either the integration of those guidelines during the development/manufacturing phase, or the compliance auditing thereof, of the user's choice. In pursuit of these objectives, the proposed research will be conducted through several major research thrusts. First, we will build an automated solution to translate high-level recommendations into more concrete, actionable security guidelines that adapts to heterogeneous IoT platforms, by applying natural language processing (NLP) techniques. Second, we will devise a compliance auditing approach that learns the clusters of similar devices using nonsensitive data (e.g., design specifications) and then locally utilizes the sensitive data (e.g., sensor data) to perform the actual auditing in a federated manner to preserve privacy. Third and orthogonally, we will develop an automated technique to generate concrete integration inputs, e.g., scripts, templates and policies, so that the developers/manufacturers can easily apply these actionable guidelines and proactively ensure compliance if they choose to. Both the second and third thrusts will try to make use of current hardware-based techniques (e.g., trusted computing). The outcome of the proposed research will help secure IoT environments and can be used to design a viable adoption plan (e.g., IoT security education, defining security policies) for the upcoming IoT era.