Bar Meyuhas

We explore the impact of device location on the communication endpoints of IoT devices within the context of Manufacturer Usage Description (MUD), an IETF security framework for IoT devices.
Two types of device location are considered: IP-based location, which corresponds to the physical location of the device based on its IP address; and user-defined location, which is chosen during device registration.
Our findings show that IP-based location barely affects the domain set with which IoT devices interact. Conversely, user-defined location drastically changes this set, mainly through region-specific domains that embody location identifiers selected by the user at registration.
We examine these findings’ effects on creating MUD file tools and IoT device identification. As MUD files rely on allowlists of domain allowlists, we show that security appliances supporting MUD need to manage a significantly larger number of MUD rules than initially anticipated. 
To address this challenge, we leverage EDNS Client Subnet (ECS) extension to differentiate user-defined locations without needing regional domains, consequently reducing the number of Access Control Entries (ACEs) required by security appliances.

Analyzing the network behavior of IoT devices, including which domains, protocols, and ports the device communicates with, is a fundamental challenge for IoT security and identification. Solutions that analyze and manage these areas must be able to learn what constitutes normal device behavior and then extract rules and features to permit only legitimate behavior or identify the device. The Manufacturer Usage Description (MUD) is an IETF white-list protection scheme that formalizes the authorized network behavior in a MUD file; this MUD file can then be used as a type of firewall mechanism.

We demonstrate that learning what is normal behavior for an IoT device is more challenging than expected. In many cases, the same IoT device, with the same firmware, can exhibit different behavior or connect to different domains with different protocols, depending on the device’s geographical location.

Then, we present a technique to generalize MUD files. By processing MUD files that originate in different locations, we can generalize and create a comprehensive MUD file that is applicable for all locations.
To conduct the research, we created MUDIS, a MUD Inspection System tool, that compares and generalizes MUD files. Our open-source MUDIS tool and dataset are available online to researchers and IoT manufacturers, allowing anyone to visualize, compare, and generalize MUD files.

Analyzing the network behavior of IoT devices, including which domains, protocols, and ports the device communicates with, is a fundamental challenge for IoT security and identification. Solutions that analyze and manage these areas must be able to learn what constitutes normal device behavior and then extract rules and features to permit only legitimate behavior or to identify the device. The Manufacturer Usage Description (MUD) is an IETF white-list protection scheme that formalizes the authorized network behavior in a MUD file; this MUD file can then be used as a type of firewall mechanism.
We demonstrate that learning what is normal behavior for an IoT device is more challenging than expected. In many cases, the same IoT device, with the same firmware, can exhibit different behavior or connect to different domains with different protocols. This behavior can even change, depending on the device’s geographical location. Thus, MUD functioning and IoT identification methods may not be effective in different locations. The reasons for this vary from country requirements to weak encryption, privacy regulations, CDN-like solutions, and more.

Cross-site search attacks allow a rogue website to expose private, sensitive user-information from web applications. The attacker exploits timing and other side channels to extract the information, using cleverly-designed cross-site queries.

In this work, we present a systematic approach to the study of cross-site search attacks. We begin with a comprehensive taxonomy, clarifying the relationships between different types of cross-site search attacks, as well as relationships to other attacks. We then present, analyze, and compare cross-site search attacks; We present new attacks that have improved efficiency and can circumvent browser defenses, and compare to already-published attacks. We developed and present a reproducibility framework, which allows study and evaluation of different cross-site attacks and defenses.

We also discuss defenses against cross-site search attacks, for both browsers and servers. We argue that server-based defenses are essential, including restricting cross-site search requests.