Serverless cloud functions transfer server management responsibilities to service providers, offering scalability and cost-efficiency. This convenience not only facilitates normal activities but also raises abuse concerns. So far, public understanding of real-world cloud functions remains limited. To fill this gap, we conducted an in-depth measurement study to uncover their practical usage and abuse. Through empirical analysis of nine leading providers (e.g., AWS, Tencent), we identified 531,089 function domains from a passive DNS dataset spanning April 2022 to March 2024. We first investigated the usage status of serverless cloud functions, showing the different practices between providers. Additionally, based on active requests to these functions, we pointed out privacy risks of unauthorized access and identified four abuse types, including covert C2 communication, hosting malicious websites, promoting illicit services, and abusing egress nodes as IP proxies. Alarmingly, 4.89% of cloud functions are being abused, with over 614k invocations recorded. Only four abused functions were flagged by existing threat intelligence systems, indicating critical gaps in security monitoring for serverless environments. Our work offers insights into the serverless cloud ecosystem and provides recommendations for better management. With responsible disclosure, we hope to raise awareness and improve protective measures against abuses among cloud function providers.

An IoT device today can be managed through different channels, e.g., by its device manufacturer's app, or third-party channels such as Apple's Home app, or a smart speaker. Supporting each channel is a management framework integrated in the device and provided by different parties. For example, a device that integrates Apple HomeKit framework can be managed by Apple Home app. We call the management framework of this kind, including all its device- and cloud-side components, a device management channel (DMC). 4 third-party DMCs are widely integrated in today's IoT devices along with the device manufacturer's own DMC: HomeKit, Zigbee/Z-Wave compatible DMC, and smart-speaker Seamless DMC. Each of these DMCs is a standalone system that has full mandate on the device; however, if their security policies and control are not aligned, consequences can be serious, allowing a malicious user to utilize one DMC to bypass the security control imposed by the device owner on another DMC. We call such a problem Chaotic Device Management (Codema).

The range query on encrypted databases is usually implemented using the order-preserving encryption (OPE) technique which preserves the order of plaintexts. Since the frequency leakage of plaintexts makes OPE vulnerable to frequency-analyzing attacks, some frequency-hiding order-preserving encryption (FH-OPE) schemes are proposed. However, existing FH-OPE schemes require either the large client storage of size O ( n ) or O (log n ) rounds of interactions for each query, where n is the total number of plaintexts. To this end, we propose a FH-OPE scheme that achieves the small client storage without additional client-server interactions. In detail, our scheme achieves O ( N ) client storage and 1 interaction per query, where N is the number of distinct plaintexts and N ≤ n . Especially, our scheme has a remarkable performance when N ≪ n . Moreover, we design a new coding tree for producing the order-preserving encoding which indicates the order of each ciphertext in the database. The coding strategy of our coding tree ensures that encodings update in the low frequency when inserting new ciphertexts. Experimental results show that the single round interaction and low-frequency encoding updates make our scheme more efficient than previous FH-OPE schemes.