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1. Understanding and securing device vulnerabilities through automated bug report analysis

   Feng, X; Liao, X; Wang, X; Wang, H; Li, Q; Yang, K; Zhu, H; Sun, L. (January 2019, SEC'19: Proceedings of the 28th USENIX Conference on Security Symposium)

   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. 

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2. Dissecting Operational Cellular IoT Service Security: Attacks and Defenses

   https://doi.org/10.1109/TNET.2023.3313557  

   Wang, Sihan; Xie, Tian; Chen, Min-Yue; Tu, Guan-Hua; Li, Chi-Yu; Lei, Xinyu; Chou, Po-Yi; Hsieh, Fucheng; Hu, Yiwen; Xiao, Li; et al (January 2023, IEEE/ACM Transactions on Networking)

   More than 150 cellular networks worldwide have rolled out LTE-M (LTE-Machine Type Communication) and/or NB-IoT (Narrow Band Internet of Things) technologies to support massive IoT services such as smart metering and environmental monitoring. Such cellular IoT services share the existing cellular network architecture with non-IoT (e.g., smartphone) ones. When they are newly integrated into the cellular network, new security vulnerabilities may happen from imprudent integration. In this work, we explore the security vulnerabilities of the cellular IoT from both system-integrated and service-integrated aspects. We discover several vulnerabilities spanning cellular standard design defects, network operation slips, and IoT device implementation flaws. Threateningly, they allow an adversary to remotely identify IP addresses and phone numbers assigned to cellular IoT devices, interrupt their power saving services, and launch various attacks, including data/text spamming, battery draining, device hibernation against them. We validate these vulnerabilities over five major cellular IoT carriers in the U.S. and Taiwan using their certified cellular IoT devices. The attack evaluation result shows that the adversary can raise an IoT data bill by up to $226 with less than 120 MB spam traffic, increase an IoT text bill at a rate of $5 per second, and prevent an IoT device from entering/leaving power saving mode; moreover, cellular IoT devices may suffer from denial of IoT services. We finally propose, prototype, and evaluate recommended solutions. 

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3. Cybersecurity Threat Intelligence Augmentation and Embedding Improvement - A Healthcare Usecase

   https://doi.org/10.1109/ISI49825.2020.9280482  

   Sills, Matthew; Ranade, Priyanka; Mittal, Sudip (November 2020, 2020 IEEE International Conference on Intelligence and Security Informatics (ISI))

   null (Ed.)

   The implementation of Internet of Things (IoT) devices in medical environments, has introduced a growing list of security vulnerabilities and threats. The lack of an extensible big data resource that captures medical device vulnerabilities limits the use of Artificial Intelligence (AI) based cyber defense systems in capturing, detecting, and preventing known and future attacks. We describe a system that generates a repository of Cyber Threat Intelligence (CTI) about various medical devices and their known vulnerabilities from sources such as manufacturer and ICS-CERT vulnerability alerts. We augment the intelligence repository with data sources such as Wikidata and public medical databases. The combined resources are integrated with threat intelligence in our Cybersecurity Knowledge Graph (CKG) from previous research. The augmented graph embeddings are useful in querying relevant information and can help in various AI assisted cybersecurity tasks. Given the integration of multiple resources, we found the augmented CKG produced higher quality graph representations. The augmented CKG produced a 31% increase in the Mean Average Precision (MAP) value, computed over an information retrieval task. 

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4. A Billion Open Interfaces for Eve and Mallory: MitM, DoS, and Tracking Attacks on iOS and macOS Through Apple Wireless Direct Link

   Mariotto, Alex; Heinrich, Alexander; Kreitschmann, David; Noubir, Guevara; Hollick, Matthias (January 2019, Proceedings of the 28th USENIX Security Symposium)

   Apple Wireless Direct Link (AWDL) is a key protocol in Apple’s ecosystem used by over one billion iOS and macOS devices for device-to-device communications. AWDL is a proprietary extension of the IEEE 802.11 (Wi-Fi) standard and integrates with Bluetooth Low Energy (BLE) for providing services such as Apple AirDrop. We conduct the first security and privacy analysis of AWDL and its integration with BLE. We uncover several security and privacy vulnerabilities ranging from design flaws to implementation bugs leading to a man-in-the-middle (MitM) attack enabling stealthy modification of files transmitted via AirDrop, denial-of-service (DoS) attacks preventing communication, privacy leaks that enable user identification and long-term tracking undermining MAC address randomization, and DoS attacks enabling targeted or simultaneous crashing of all neighboring devices. The flaws span across AirDrop’s BLE discovery mechanism, AWDL synchronization, UI design, and Wi-Fi driver implementation. Our analysis is based on a combination of reverse engineering of protocols and code supported by analyzing patents. We provide proof-of-concept implementations and demonstrate that the attacks can be mounted using a low-cost ($20) micro:bit device and an off-the-shelf Wi-Fi card. We propose practical and effective countermeasures. While Apple was able to issue a fix for a DoS attack vulnerability after our responsible disclosure, the other security and privacy vulnerabilities require the redesign of some of their services. 

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5. On Runtime Software Security of TrustZone-M Based IoT Devices

   https://doi.org/10.1109/GLOBECOM42002.2020.9322370  

   Luo, Lan; Zhang, Yue; Zou, Cliff; Shao, Xinhui; Ling, Zhen; Fu, Xinwen (December 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference)

   null (Ed.)

   Internet of Things (IoT) devices have been increasingly integrated into our daily life. However, such smart devices suffer a broad attack surface. Particularly, attacks targeting the device software at runtime are challenging to defend against if IoT devices use resource-constrained microcontrollers (MCUs). TrustZone-M, a TrustZone extension for MCUs, is an emerging security technique fortifying MCU based IoT devices. This paper presents the first security analysis of potential software security issues in TrustZone-M enabled MCUs. We explore the stack-based buffer overflow (BOF) attack for code injection, return-oriented programming (ROP) attack, heap-based BOF attack, format string attack, and attacks against Non-secure Callable (NSC) functions in the context of TrustZone-M. We validate these attacks using the Microchip SAM L11 MCU, which uses the ARM Cortex-M23 processor with the TrustZone-M technology. Strategies to mitigate these software attacks are also discussed. 

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