An increasing number of embedded devices are connecting to the Internet, ranging from cameras, routers to printers, while an adversary can exploit security flaws already known to compromise those devices. Security patches are usually associated with the device firmware, which relies on the device vendors and products. Due to compatibility and release-time issues, many embedded devices are still using outdated firmware with known vulnerabilities or flaws. In this article, we conduct a systematic study on device vulnerabilities by leveraging firmware fingerprints. Specifically, we use a web crawler to gather 9,716 firmware images from official websites of device vendors, and 347,685 security reports scattered across data archives, blogs, and forums. We propose to generate fine-grained fingerprints based on the subtle differences between the filesystems of various firmware images. Furthermore, machine learning algorithms and regex are used to identify device vulnerabilities and corresponding device firmware fingerprints. We perform real-world experiments to validate the performance of the firmware fingerprint, which yields high accuracy of 91% precision and 90% recall. We reveal that 6,898 reports have the firmware and related vulnerability information, and there are more than 10% of firmware vulnerabilities without any patches or solutions for mitigating underlying security risks.

The reuse of libraries in software development has become prevalent for improving development efficiency and software quality. However, security vulnerabilities of reused libraries propagated through software project dependency pose a severe security threat, but they have not yet been well studied. In this paper, we present the first large-scale empirical study of project dependencies with respect to security vulnerabilities. We developed PDGraph, an innovative approach for analyzing publicly known security vulnerabilities among numerous project dependencies, which provides a new perspective for assessing security risks in the wild. As a large-scale software collection in dependency, we find 337,415 projects and 1,385,338 dependency relations. In particular, PDGraph generates a project dependency graph, where each node is a project, and each edge indicates a dependency relationship. We conducted experiments to validate the efficacy of PDGraph and characterized its features for security analysis. We revealed that 1,014 projects have publicly disclosed vulnerabilities, and more than 67,806 projects are directly dependent on them. Among these, 42,441 projects still manifest 67,581 insecure dependency relationships, indicating that they are built on vulnerable versions of reused libraries even though their vulnerabilities are publicly known. During our eight-month observation period, only 1,266 insecure edges were fixed, and corresponding vulnerable libraries were updated to secure versions. Furthermore, we uncovered four underlying dependency risks that can significantly reduce the difficulty of compromising systems. We conducted a quantitative analysis of dependency risks on the PDGraph.

IP-based geolocation is essential for various location-aware Internet applications, such as online advertisement, content delivery, and online fraud prevention. Achieving accurate geolocation enormously relies on the number of high-quality (i.e., the fine-grained and stable over time) landmarks. However, the previous efforts of garnering landmarks have been impeded by the limited visible landmarks on the Internet and manual time cost. In this paper, we leverage the availability of numerous online webcams used to monitor physical surroundings as a rich source of promising high-quality landmarks for serving IP-based geolocation. In particular, we present a new framework called GeoCAM, which is designed to automatically generate qualified landmarks from online webcams, providing an IP-based geolocation service with high accuracy and wide coverage. GeoCAM periodically monitors websites hosting live webcams and uses the natural language processing technique to extract the IP addresses and latitude/longitude of webcams for generating landmarks at a large-scale. Given latency and topology constraints among webcam landmarks, GeoCAM uses the maximum likelihood estimation to approximately pinpoint the geolocation of a target host. We develop a prototype of GeoCAM and conduct real-world experiments for validating its efficacy. Our results show that GeoCam can detect 282,902 live webcams hosted in webpages with 94.2% precision and 90.4% recall, and then generate 16,863 stable and fine-grained landmarks, which are two orders of magnitude more than the landmarks used in prior works. To demonstrate the superiority of using large-scale webcams as landmarks, we implement four different geolocation algorithms and compare their performance between webcam landmarks and open-source landmarks. The evaluation results show that all the algorithms can significantly improve geolocation accuracy by using webcam landmarks.