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.

Attackers regularly target Android phones and come up with new ways to bypass detection mechanisms to achieve long-term stealth on a victim’s phone. One way attackers do this is by leveraging critical benign app functionality to carry out specific attacks.In this paper, we present a novel generalised framework, JIT-MF (Just-in-time Memory Forensics), which aims to address the problem of timely collection of short-lived evidence in volatile memory to solve the stealthiest of Android attacks. The main components of this framework are i) Identification of critical data objects in memory linked with critical benign application steps that may be misused by an attacker; and ii) Careful selection of trigger points, which identify when memory dumps should be taken during benign app execution.The effectiveness and cost of trigger point selection, a cornerstone of this framework, are evaluated in a preliminary qualitative study using Telegram and Pushbullet as the victim apps targeted by stealthy malware. Our study identifies that JIT-MF is successful in dumping critical data objects on time, providing evidence that eludes all other forensic sources. Experimentation offers insight into identifying categories of trigger points that can strike a balance between the effort required for selection and the resulting effectiveness and storage costs. Several optimisation measures for the JIT-MF tools are presented, considering the typical resource constraints of Android devices.

The ubiquity of Android smartphones makes them targets of sophisticated malware, which maintain long-term stealth, particularly by offloading attack steps to benign apps. Such malware leaves little to no trace in logs, and the attack steps become difficult to discern from benign app functionality. Endpoint detection and response (EDR) systems provide live forensic capabilities that enable anomaly detection techniques to detect anomalous behavior in application logs after an app hijack. However, this presents a challenge, as state-of-the-art EDRs rely on device and third-party application logs, which may not include evidence of attack steps, thus prohibiting anomaly detection techniques from exposing anomalous behavior. While, theoretically, all the evidence resides in volatile memory, its ephemerality necessitates timely collection, and its extraction requires device rooting or app repackaging. We present VEDRANDO, an enhanced EDR for Android that accomplishes (i) the challenge of timely collection of volatile memory artefacts and (ii) the detection of a class of stealthy attacks that hijack benign applications. VEDRANDO leverages memory forensics and app virtualization techniques to collect timely evidence from memory, which allows uncovering attack steps currently uncollected by the state-of-the-art EDRs. The results showed that, with less than 5% CPU overhead compared to normal usage, VEDRANDO could uniquely collect and fully reconstruct the stealthy attack steps of ten realistic messaging hijack attacks using standard anomaly detection techniques, without requiring device or app modification.