Despite the growing popularity of macOS among end users and enterprise systems, malware research has primarily focused on Windows and Android operating systems, leaving the problem of macOS malware detection relatively unexplored. Indeed, the specificity of the operating system and the unique characteristics of the Mach-O file format can play a fundamental role in the classification of unknown samples, drastically increasing the detection rate. In this work, for the first time in the literature, we employ new domain-specific features, i.e., static features specific to macOS binaries, such as embedded certificates, entitlements, persistence techniques and key system APIs, to train a machine learning malware detector. We perform a comprehensive experimental evaluation on a novel dataset of 41,129 samples, comprising 11,413 benign and 29,716 malicious executables, and demonstrate that our solution achieves state-of-the-art detection performance (98.50%), outperforming all existing approaches, with an average improvement of 16% in terms of detection rate. We also provide an in-depth analysis of the importance of the individual features, showing that our detector effectively leverages the new domain-specific features. Then, in order to evaluate the generalization capabilities of our detector over time, we perform a real-world evaluation on a new dataset of 9,000 fresh macOS executables. The results show that (i) our detector maintains a very high detection rate (99.50%), (ii) outperforms the state-of-the-art by 50%, and (iii) the domain-specific features are crucial for generalizing to novel malware samples, as their removal leads to a 15.92% drop in detection performance. Finally, we also release our dataset to the research community.

In order to maintain its presence on an infected system, malware employs a variety of persistence techniques. Although persistence is a well-known tactic of modern malware, our community lacks a comprehensive understanding of the types and prevalence of techniques adopted by Windows malware.

Memory forensics is a powerful technique commonly adopted to investigate compromised machines and to detect stealthy computer attacks that do not store data on non-volatile storage. To employ this technique effectively, the analyst has to first acquire a faithful copy of the system’s volatile memory after the incident. However, almost all memory acquisition tools capture the content of physical memory without stopping the system’s activity and by following the ascending order of the physical pages, which can lead to inconsistencies and errors in the dump. In this paper we developed a system to track all write operations performed by the OS kernel during a memory acquisition process. This allows us to quantify, for the first time, the exact number and type of inconsistencies observed in memory dumps. We examine the runtime activity of three different operating systems and the way they manage physical memory. Then, focusing on Linux, we quantify how different acquisition modes, file systems, and hardware targets influence the frequency of kernel writes during the dump. We also analyze the impact of inconsistencies on the reconstruction of page tables and major kernel data structures used by Volatility to extract forensic artifacts. Our results show that inconsistencies are very common and that their presence can undermine the reliability and validity of memory forensics analysis.

The first step required to perform any analysis of a physical memory image is the reconstruction of the virtual address spaces, which allows translating virtual addresses to their corresponding physical offsets. However, this phase is often overlooked, and the challenges related to it are rarely discussed in the literature. Practical tools solve the problem by using a set of custom heuristics tailored on a very small number of well-known operating systems (OSs) running on few architectures. In this article, we look for the first time at all the different ways the virtual to physical translation can be operated in 10 different CPU architectures. In each case, we study the inviolable constraints imposed by the memory management unit that can be used to build signatures to recover the required data structures from memory without any knowledge about the running OS. We build a proof-of-concept tool to experiment with the extraction of virtual address spaces showing the challenges of performing an OS-agnostic virtual to physical address translation in real-world scenarios. We conduct experiments on a large set of 26 different OSs and a use case on a real hardware device. Finally, we show a possible usage of our technique to retrieve information about user space processes running on an unknown OS without any knowledge of its internals.