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首页> 外文期刊>IEEE transactions on industrial informatics >Cyber Vulnerability Intelligence for Internet of Things Binary
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Cyber Vulnerability Intelligence for Internet of Things Binary

机译：关于事物互联网的网络漏洞情报

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摘要

Internet of Things (IoT) integrates a variety of software (e.g., autonomous vehicles and military systems) in order to enable the advanced and intelligent services. These software increase the potential of cyber-attacks because an adversary can launch an attack using system vulnerabilities. Existing software vulnerability analysis methods used to be relying on human experts crafted features, which usually miss many vulnerabilities. It is important to develop an automatic vulnerability analysis system to improve the countermeasures. However, source code is not always available (e.g., most IoT related industry software are closed source). Therefore, vulnerability detection on binary code is a demanding task. This article addresses the automatic binary-level software vulnerability detection problem by proposing a deep learning-based approach. The proposed approach consists of two phases: binary function extraction, and model building. First, we extract binary functions from the cleaned binary instructions obtained by using IDA Pro. Then, we employ the attention mechanism on top of a bidirectional long short-term memory for building the predictive model. To show the effectiveness of the proposed approach, we have collected datasets from several different sources. We have compared our proposed approach with a series of baselines including source code-based techniques and binary code-based techniques. We have also applied the proposed approach to real-world IoT related software such as VLC media player and LibTIFF project that used on Autonomous Vehicles. Experimental results show that our proposed approach betters the baselines and is able to detect more vulnerabilities.
机译：事物互联网（物联网）集成了各种软件（例如，自主车辆和军事系统），以便实现先进和智能的服务。这些软件增加了网络攻击的潜力，因为对手可以使用系统漏洞发射攻击。现有的软件漏洞分析方法用于依赖于人类专家制作的功能，通常会错过许多漏洞。开发自动漏洞分析系统非常重要，以提高对策。但是，源代码并不总是可用（例如，大多数IoT相关的行业软件是封闭的来源）。因此，二进制代码的漏洞检测是一个苛刻的任务。本文通过提出基于深度学习的方法来解决自动二进制软件漏洞检测问题。所提出的方法包括两个阶段：二元函数提取和模型建设。首先，我们从使用IDA Pro获得的清洁二进制指令中提取二进制函数。然后，我们在双向短期内存顶部采用注意机制，以构建预测模型。为了展示所提出的方法的有效性，我们收集了来自几个不同来源的数据集。我们将所提出的方法与一系列基线进行了比较，包括基于源代码的技术和基于二进制代码的技术。我们还将提议的方法应用于现实世界的IOT相关软件，如VLC媒体播放器和用于自动车辆的Libtiff项目。实验结果表明，我们所提出的方法将基线粘接，能够检测更多的漏洞。

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