Security of Internet of Things (IoT) devices is a well known concern as these devices come in increasing use in homes and commercial environments. To better understand the extent to which companies take security of the IoT devices seriously and the methods they use to secure them, this paper presents findings from a security analysis of 96 top-selling WiFi IoT devices on Amazon. We found that we could carry out a significant portion of the analysis by first analyzing the code of Android companion apps responsible for controlling the devices. An interesting finding was that these devices used only 32 unique companion apps; we found instances of devices from same as well as different brands sharing the same app, significantly reducing our work. We analyzed the code of these companion apps to understand how they communicated with the devices and the security of that communication. We found security problems to be widespread: 50% of the apps corresponding to 38% of the devices did not use proper encryption techniques; some even used well-known weak ciphers such as Caesar cipher. We also purchased 5 devices and confirmed the vulnerabilities found with exploits. In some cases, we were able to bypass the pairing process and still control the device. Finally, we comment on technical and non-technical lessons learned from the study that have security implications 
                        more » 
                        « less   
                    
                            
                            A Study of Vulnerability Analysis of Popular Smart Devices Through Their Companion Apps
                        
                    
    
            Security of Internet of Things (IoT) devices is a well-known concern as these devices come in increasing use in homes and commercial environments. To better understand the extent to which companies take security of the IoT devices seriously and the methods they use to secure them, this paper presents findings from a security analysis of 96 top-selling WiFi IoT devices on Amazon. We found that we could carry out a significant portion of the analysis by first analyzing the code of Android companion apps responsible for controlling the devices. An interesting finding was that these devices used only 32 unique companion apps; we found instances of devices from same as well as different brands sharing the same app, significantly reducing our work. We analyzed the code of these companion apps to understand how they communicated with the devices and the security of that communication. We found security problems to be widespread: 50% of the apps corresponding to 38% of the devices did not use proper encryption techniques; some even used well-known weak ciphers such as Caesar cipher. We also purchased 5 devices and confirmed the vulnerabilities found with exploits. In some cases, we were able to bypass the pairing process and still control the device. Finally, we comment on technical and non-technical lessons learned from the study that have security implications. 
        more » 
        « less   
        
    
    
                            - PAR ID:
- 10128313
- Date Published:
- Journal Name:
- 2019 IEEE Security and Privacy Workshops (SPW) -- SafeThings Workshop
- Page Range / eLocation ID:
- 181 to 186
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
- 
            
- 
            Recent years have witnessed the rise of Internet-of-Things (IoT) based cyber attacks. These attacks, as expected, are launched from compromised IoT devices by exploiting security flaws already known. Less clear, however, are the fundamental causes of the pervasiveness of IoT device vulnerabilities and their security implications, particularly in how they affect ongoing cybercrimes. To better understand the problems and seek effective means to suppress the wave of IoT-based attacks, we conduct a comprehensive study based on a large number of real-world attack traces collected from our honeypots, attack tools purchased from the underground, and information collected from high-profile IoT attacks. This study sheds new light on the device vulnerabilities of today's IoT systems and their security implications: ongoing cyber attacks heavily rely on these known vulnerabilities and the attack code released through their reports; on the other hand, such a reliance on known vulnerabilities can actually be used against adversaries. The same bug reports that enable the development of an attack at an exceedingly low cost can also be leveraged to extract vulnerability-specific features that help stop the attack. In particular, we leverage Natural Language Processing (NLP) to automatically collect and analyze more than 7,500 security reports (with 12,286 security critical IoT flaws in total) scattered across bug-reporting blogs, forums, and mailing lists on the Internet. We show that signatures can be automatically generated through an NLP-based report analysis, and be used by intrusion detection or firewall systems to effectively mitigate the threats from today's IoT-based attacks.more » « less
- 
            Recent years have witnessed the rise of Internet-of-Things (IoT) based cyber attacks. These attacks, as expected, are launched from compromised IoT devices by exploiting security flaws already known. Less clear, however, are the fundamental causes of the pervasiveness of IoT device vulnerabilities and their security implications, particularly in how they affect ongoing cybercrimes. To better understand the problems and seek effective means to suppress the wave of IoT-based attacks, we conduct a comprehensive study based on a large number of real-world attack traces collected from our honeypots, attack tools purchased from the underground, and information collected from high-profile IoT attacks. This study sheds new light on the device vulnerabilities of today’s IoT systems and their security implications: ongoing cyber attacks heavily rely on these known vulnerabilities and the attack code released through their reports; on the other hand, such a reliance on known vulnerabilities can actually be used against adversaries. The same bug reports that enable the development of an attack at an exceedingly low cost can also be leveraged to extract vulnerability-specific features that help stop the attack. In particular, we leverage Natural Language Processing (NLP) to automatically collect and analyze more than 7,500 security reports (with 12,286 security critical IoT flaws in total) scattered across bug-reporting blogs, forums, and mailing lists on the Internet. We show that signatures can be automatically generated through an NLP-based report analysis, and be used by intrusion detection or firewall systems to effectively mitigate the threats from today’s IoT-based attacks.more » « less
- 
            To study the security properties of the Internet of Things (IoT), firmware analysis is crucial. In the past, many works have been focused on analyzing Linux-based firmware. Less known is the security landscape of MCU-based IoT devices, an essential portion of the IoT ecosystem. Existing works on MCU firmware analysis either leverage the companion mobile apps to infer the security properties of the firmware (thus unable to collect low-level properties) or rely on small-scale firmware datasets collected in ad-hoc ways (thus cannot be generalized). To fill this gap, we create a large dataset of MCU firmware for real IoT devices. Our approach statically analyzes how MCU firmware is distributed and then captures the firmware. To reliably recognize the firmware, we develop a firmware signature database, which can match the footprints left in the firmware compilation and packing process. In total, we obtained 8,432 confirmed firmware images (3,692 unique) covering at least 11 chip vendors across 7 known architectures and 2 proprietary architectures. We also conducted a series of static analyses to assess the security properties of this dataset. The result reveals three disconcerting facts: 1) the lack of firmware protection, 2) the existence of N-day vulnerabilities, and 3) the rare adoption of security mitigation.more » « less
- 
            There has been a proliferation of mobile apps in the Medical, as well as Health&Fitness categories. These apps have a wide audience, from medical providers, to patients, to end users who want to track their fitness goals. The low barrier to entry on mobile app stores raises questions about the diligence and competence of the developers who publish these apps, especially regarding the practices they use for user data collection, processing, and storage. To help understand the nature of data that is collected, and how it is processed, as well as where it is sent, we developed a tool named PIT (Personal Information Tracker) and made it available as open source. We used PIT to perform a multi-faceted study on 2832 Android apps: 2211 Medical apps and 621 Health&Fitness apps. We first define Personal Information (PI) as 17 different groups of sensitive information, e.g., user’s identity, address and financial information, medical history or anthropometric data. PIT first extracts the elements in the app’s User Interface (UI) where this information is collected. The collected information could be processed by the app’s own code or third-party code; our approach disambiguates between the two. Next, PIT tracks, via static analysis, where the information is “leaked”, i.e., it escapes the scope of the app, either locally on the phone or remotely via the network. Then, we conduct a link analysis that examines the URLs an app connects with, to understand the origin and destination of data that apps collect and process. We found that most apps leak 1–5 PI items (email, credit card, phone number, address, name, being the most frequent). Leak destinations include the network (25%), local databases (37%), logs (23%), and files or I/O (15%). While Medical apps have more leaks overall, as they collect data on medical history, surprisingly, Health&Fitness apps also collect, and leak, medical data. We also found that leaks that are due to third-party code (e.g., code for ads, analytics, or user engagement) are much more numerous (2x–12x) than leaks due to app’s own code. Finally, our link analysis shows that most apps access 20–80 URLs (typically third-party URLs and Cloud APIs) though some apps could access more than 1,000 URLs.more » « less
 An official website of the United States government
An official website of the United States government 
				
			 
					 
					
 
                                    