Static malware detectors based on machine learning are integral to contemporary antivirus systems, but they are vulnerable to adversarial attacks. While existing research has demonstrated success with adversarial attacks in black-box hard-label scenarios, challenges such as high perturbation rates and incomplete retention of functional integrity remain. To address these issues, we propose a novel black-box hard-label attack method, MiniMal. MiniMal begins with initialized adversarial examples and utilizes binary search and particle swarm optimization algorithms to streamline the perturbation content, significantly reducing the perturbation rate of the adversarial examples. Furthermore, we propose a functionality verification method grounded in file format parsing and control flow graph comparisons to ensure the functional integrity of the adversarial examples. Experimental results indicate that MiniMal achieves an attack success rate of over 98% against three leading machine learning detectors, improving performance by approximately 4.8% to 7.1% compared to state-of-the-art methods. MiniMal reduces perturbation rates to below 40%, making them 9 to 11 times lower than those of previous methods. Additionally, functional verification via Cuckoo Sandbox revealed that the adversarial examples generated by MiniMal retained 100% functional integrity, even with various modifications applied.

With the increasing popularity of the Internet of Things (IoT), many IoT cloud platforms have emerged to help the IoT manufacturers connect their devices to their users. Serving the device-user communication is general messaging protocol deployed on the platforms. Less clear, however, is whether such protocols, which are not designed to work in the adversarial environment of IoT, introduce new risks. In this paper, we report the first systematic study on the protection of major IoT clouds (e.g., AWS, Microsoft, IBM) put in place for the arguably most popular messaging protocol - MQTT. We found that these platforms' security additions to the protocol are all vulnerable, allowing the adversary to gain control of the device, launch a large-scale denial-of-service attack, steal the victim's secrets data and fake the victim's device status for deception. We successfully performed end-to-end attacks on these popular IoT clouds and further conducted a measurement study, which demonstrates that the security impacts of our attacks are real, severe and broad. We reported our findings to related parties, which all acknowledged the importance. We further propose new design principles and an enhanced access model MOUCON. We implemented our protection on a popular open-source MQTT server. Our evaluation shows its high effectiveness and negligible performance overhead.