The increasing dominance of Android smartphones for everyday communication and data processing makes long-term stealthy malware an even more dangerous threat. Recent malware campaigns like Flubot demonstrate that by employing stealthy malware techniques even at minimal capacity, malware is highly effective in making its way to millions of devices with little resistance from existing detection mechanisms. Consequential late detection demands comprehensive forensic timelines to reconstruct all malicious activities. However, the reduced forensic footprint of stealthy attacks with minimal malware involvement leaves investigators little evidence to work with even when utilising state-of-the-art digital forensics tools. Volatile memory forensics can be effective in such scenarios since app execution of any form is always bound to leave a trail of evidence in memory, even if it is short-lived. In this work, we motivate the need for JIT-MF (Just-in-time Memory Forensics), a technique that aims to address the challenges that arise with timely collection of short-lived evidence in volatile memory to solve the stealthiest of Android attacks. By taking an incident-response-centric approach, focused on protecting stock Android device users rather than treating them as potential adversaries, we show that JIT-MF tools can collect elusive attack steps in volatile memory without requiring device rooting. Furthermore, we build MobFor , a JIT-MF based tool focusing on capturing evidence related to messaging hijack attacks. This tool provides a context to explore solutions for JIT-MF implementation challenges, aiming to render JIT-MF tools practical for real-world requirements. Finally, we demonstrate that when compared to state-of-the-art digital forensic tools Belkasoft and XRY in a realistic attack scenario involving an enhanced version of the WhatsApp Pink malware and stock Android devices, only MobFor can recover the contents of messages sent by the malware, hence decisively contributing to an enriched forensic timeline.

Analytical security of cryptographic protocols does not immediately translate to operational security due to incorrect implementation and attacks targeting the execution environment. Code verification and hardware-based trusted execution solutions exist, however these leave it up to the implementer to assemble the complete solution, imposing a complete re-think of the hardware platforms and software development process. We rather aim for a comprehensive solution for secure cryptographic protocol execution, which takes the form of a trusted execution environment based on runtime verification and stock hardware security modules. RV-TEE can be deployed on existing platforms and protocol implementations. Runtime verification lends itself well at several conceptual levels of the execution environment, ranging from high level protocol properties, to lower level checks such as taint inference. The proposed architectural setup involving two runtime verification modules is instantiated through a case study using a popular web browser. We successfully monitor high and low level properties with promising results with respect to practicality.

Android accessibility features include a robust set of tools allowing developers to create apps for assisting people with disabilities. Unfortunately, this useful set of tools can also be abused and turned into an attack vector, providing malware with the ability to interact and read content from third-party apps. In this work, we are the first to study the impact that the stealthy exploitation of Android accessibility services can have on significantly reducing the forensic footprint of malware attacks, thus hindering both live and post-incident forensic investigations. We show that through Living off the Land (LotL) tactics, or by offering a malware-only substitute for attacks typically requiring more elaborate schemes, accessibility-based malware can be rendered virtually undetectable. In the LotL approach, we demonstrate accessibility-enabled SMS and command and control (C2) capabilities. As for the latter, we show a complete cryptocurrency wallet theft, whereby the accessibility trojan can hijack the entire withdrawal process of a widely used app, including two-factor authentication (2FA). In both cases, we demonstrate how the attacks result in significantly diminished forensic evidence when compared to similar attacks not employing accessibility tools, even to the extent of maintaining device take-over without requiring malware persistence.

The security of financial apps on smartphones is threatened by a class of advanced and persistent malware that can bypass all existing security measures. Strong cryptography and trusted on-chip hardware modules are powerless against sophisticated attacks that supplant device owners through device input record/replay functionality, effectively hijacking their credentials, privileges, and actions. In this paper, we introduce Proof-of-Presence and Locality (PoPL), a new security measure that tackles this threat by leveraging sensors to prove the physical presence of device owners and therefore discriminate between malware-initiated transaction requests and legitimate ones. Moreover, PoPL neither imposes the expense of additional hardware nor compromises app usability. In order to demonstrate PoPL’s practicality, we developed PoPLar , a challenge puzzle implementation of the PoPL concept that ensures usability even on limited screen sizes by the use of a dendrogram. We have made it available as an open-source library ready to be integrated with minimal effort with existing apps. We demonstrate PoPLar’s effectiveness and ease of integration through case studies involving apps from the three top cryptocurrency exchanges and an open-source crypto wallet.

File carving tools carry out file recovery whenever the file-system meta-data is not available, which makes them a valuable addition to the cyber crime investigator's toolkit. Existing file carvers either cannot handle fragmented files or require a probabilistic model derived using a number of training images. This training data may not always be feasible to aggregate or its sheer size could undermine practicality. Similar to existing techniques, our method exploits both the JPEG syntax and semantic-based analysis steps in order to distinguish the correct fragments required for recovering images. The thumbnail affinity-based semantic analysis constitutes the novel aspect of this approach. Comparative evaluation using three widely used benchmark test sets show that our carver compares with the state-of-the-art commercial tool that requires an a-priori model while beating a number of popular forensic tools. This outcome demonstrates the successful replacement of the probabilistic model with thumbnail affinity, rendering this technique the right complement for existing carvers in situations where thumbnail information is readily available.