arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2206.03079cs.CRcs.IRcs.LGcs.SE
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开发者文本讨论中句子级 IoT 安全方面的实证研究

An Empirical Study of IoT Security Aspects at Sentence-Level in Developer Textual Discussions

University of Calgary(卡尔加里大学)

Nibir Chandra Mandal, Gias Uddin

AI总结:

本文基于 SO 的 53K 篇 IoT 帖子构建句子级标注基准,训练 RoBERTa 模型 SecBot 识别安全讨论,并通过主题建模归纳开发者面临的九类软件、硬件与网络安全挑战。

摘要:

IoT 是一种快速兴起的范式,如今已涵盖现代生活的几乎每个方面。因此,确保 IoT 设备的安全至关重要。IoT 设备可能不同于传统计算设备,因而在 IoT 设备中设计和实施适当的安全措施可能具有挑战性。我们观察到,IoT 开发者会在 Stack Overflow(SO)等开发者论坛中讨论其与安全相关的挑战。然而,我们发现 IoT 安全讨论也可能被埋没在 SO 的非安全讨论之中。本文旨在理解 IoT 开发者在将安全实践和技术应用于 IoT 设备时所面临的挑战。我们有两个目标:(1)开发一个能够自动发现 SO 中与安全相关的 IoT 讨论的模型;(2)研究模型输出,以了解 IoT 开发者与安全相关的挑战。首先,我们从 SO 下载 53K 篇包含 IoT 讨论的帖子。其次,我们从 53K 篇帖子中手动标注 5,919 个句子,将其标记为 1 或 0。第三,我们使用该基准研究一组深度学习 transformer 模型。表现最佳的模型称为 SecBot。第四,我们将 SecBot 应用于全部帖子,发现约 30K 个与安全相关的句子。第五,我们对与安全相关的句子应用主题建模,然后对主题进行标注和分类。第六,我们分析这些主题在 SO 中的演变。我们发现:(1)SecBot 基于对深度学习模型 RoBERTa 的重新训练,SecBot 取得 0.935 的最佳 F1-Score;(2)SecBot 的错误分类样本中存在六类错误,当关键词/语境存在歧义时,SecBot 最容易出错(例如,gateway 可以指安全网关,也可以指普通网关);(3)共有 9 个安全主题,归为软件、硬件和网络三大类;(4)主题数量最多的是软件安全,其次是网络安全。

英文摘要:

IoT is a rapidly emerging paradigm that now encompasses almost every aspect of our modern life. As such, ensuring the security of IoT devices is crucial. IoT devices can differ from traditional computing, thereby the design and implementation of proper security measures can be challenging in IoT devices. We observed that IoT developers discuss their security-related challenges in developer forums like Stack Overflow(SO). However, we find that IoT security discussions can also be buried inside non-security discussions in SO. In this paper, we aim to understand the challenges IoT developers face while applying security practices and techniques to IoT devices. We have two goals: (1) Develop a model that can automatically find security-related IoT discussions in SO, and (2) Study the model output to learn about IoT developer security-related challenges. First, we download 53K posts from SO that contain discussions about IoT. Second, we manually labeled 5,919 sentences from 53K posts as 1 or 0. Third, we use this benchmark to investigate a suite of deep learning transformer models. The best performing model is called SecBot. Fourth, we apply SecBot on the entire posts and find around 30K security related sentences. Fifth, we apply topic modeling to the security-related sentences. Then we label and categorize the topics. Sixth, we analyze the evolution of the topics in SO. We found that (1) SecBot is based on the retraining of the deep learning model RoBERTa. SecBot offers the best F1-Score of 0.935, (2) there are six error categories in misclassified samples by SecBot. SecBot was mostly wrong when the keywords/contexts were ambiguous (e.g., gateway can be a security gateway or a simple gateway), (3) there are 9 security topics grouped into three categories: Software, Hardware, and Network, and (4) the highest number of topics belongs to software security, followed by network security.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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