Mark Vella

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.

Difficulties with accessing device content or even the device itself can seriously hamper smartphone forensics. Mobile cloud storage, which extends on-device capacity, provides an avenue for a forensic collection process that does not require physical access to the device. Rather, it is possible to remotely retrieve credentials from a device of interest through undercover operations, followed by live cloud forensics. While technologically appealing, this approach raises concerns with evidence preservation, ranging from the use of malware-like operations, to linking the collected evidence with the physically absent smartphone, and possible mass surveillance accusations. In this paper, we propose a solution to ease these concerns by employing hardware security modules to provide for controlled live cloud forensics and tamper-evident access logs. A Google Drive-based proof of concept, using the SEcube hardware security module, demonstrates that D-Cloud-Collector is feasible whenever the performance penalty incurred is affordable.

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.

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.

Programming errors in Ethereum smart contracts can result in catastrophic financial losses from stolen cryptocurrency. While vulnerability detectors can prevent vulnerable contracts from being deployed, this does not mean that such contracts will not be deployed. Once a vulnerable contract is instantiated on the blockchain and becomes the target of attacks, the identification of exploit transactions becomes indispensable in assessing whether it has been actually exploited and identifying which malicious or subverted accounts were involved. In this work, we study the problem of post-factum investigation of Ethereum attacks using Indicators of Compromise (IoCs) specially crafted for use in the blockchain. IoC definitions need to capture the side-effects of successful exploitation in the context of the Ethereum blockchain. Therefore, we define a model for smart contract execution, comprising multiple abstraction levels that mirror the multiple views of code execution on a blockchain. Subsequently, we compare IoCs defined across the different levels in terms of their effectiveness and practicality through EtherClue, a prototype tool for investigating Ethereum security incidents. Our results illustrate that coarse-grained IoCs defined over blocks of transactions can detect exploit transactions with less computation; however, they are contract-specific and suffer from false negatives. On the other hand, fine-grained IoCs defined over virtual machine instructions can avoid these pitfalls at the expense of increased computation which are nevertheless applicable for practical use.

Incorrect cryptographic protocol implementation and malware attacks targeting its runtime may lead to insecure execution even if the protocol design has been proven safe. This research focuses on adapting a runtime-verification-centric trusted execution environment (RV-TEE) solution to a cryptographic protocol deployment --- particularly that of the Secure Shell Protocol (SSH). We aim to show that our approach, which does not require any specific security hardware or operating system modifications, is feasible through the design of a framework and work-in-progress empirical evaluation. We provide: (i) The design of the setup involving SSH, (ii) The provision of the RV-TEE setup with SSH implementation, including (iii) An overview of the property extraction process through a methodical analysis of the SSH protocol specifications.

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.