Recovering Container Class Types in C++ Binaries

We present TIARA, a novel approach to recovering container classes in c++ binaries. Given a variable address in a c++ binary, TIARA first applies a new type-relevant slicing algorithm incorporated with a decay function, TSLICE, to obtain an inter-procedural forward slice of instructions expressed as a CFG to summarize how the variable is used in the binary (as our primary contribution). TIARA then makes use of a GCN (Graph Convolutional Network) to learn and predict the container type for the variable (as our secondary contribution). According to our evaluation, TIARA can advance the state of the art in inferring commonly used container types in a set of eight large real-world COTS c++ binaries efficiently (in terms of the overall analysis time) and effectively (in terms of precision, recall and F1 score)

Lessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection

A well-known curse of computer security research is that it often produces systems that, while technically sound, fail operationally. To overcome this curse, the community generally seeks to assess proposed systems under a variety of settings in order to make explicit every potential bias. In this respect, recently, research achievements on machine learning based malware detection are being considered for thorough evaluation by the community. Such an effort of comprehensive evaluation supposes first and foremost the possibility to perform an independent reproduction study in order to sharpen evaluations presented by approaches’ authors. The question Can published approaches actually be reproduced? thus becomes paramount despite the little interest such mundane and practical aspects seem to attract in the malware detection field. In this paper, we attempt a complete reproduction of five Android Malware Detectors from the literature and discuss to what extent they are “reproducible”. Notably, we provide insights on the implications around the guesswork that may be required to finalise a working implementation. Finally, we discuss how barriers to reproduction could be lifted, and how the malware detection field would benefit from stronger reproducibility standards—like many various fields already have

NoVT: Eliminating C++ Virtual Calls to Mitigate Vtable Hijacking

The vast majority of nowadays remote code execution attacks target virtual function tables (vtables). Attackers hijack vtable pointers to change the control flow of a vulnerable program to their will, resulting in full control over the underlying system. In this paper, we present NoVT, a compiler-based defense against vtable hijacking. Instead of protecting vtables for virtual dispatch, our solution replaces them with switch-case constructs that are inherently control-flow safe, thus preserving control flow integrity of C++ virtual dispatch. NoVT extends Clang to perform a class hierarchy analysis on C++ source code. Instead of a vtable, each class gets unique identifier numbers which are used to dispatch the correct method implementation. Thereby, NoVT inherently protects all usages of a vtable, not just virtual dispatch. We evaluate NoVT on common benchmark applications and real-world programs including Chromium. Despite its strong security guarantees, NoVT improves runtime performance of most programs (mean overhead -0.5%, -3.7% min, 2% max). In addition, protected binaries are slightly smaller than unprotected ones. NoVT works on different CPU architectures and protects complex C++ programs against strong attacks like COOP and ShrinkWrap

A Survey of Subscription Privacy on the 5G Radio Interface - The Past, Present and Future

End-user privacy in mobile telephony systems is nowadays of great interest because of the envisaged hyper-connectivity and the potential of the unprecedented services (virtual reality, machine-type communication, vehicle-to-everything, IoT, etc.) being offered by the new 5G system. This paper reviews the state of subscription privacy in 5G systems. As the work on 5G Release 15 -- the first full set of 5G standards -- has recently been completed, this seems to be an appropriate occasion for such a review. The scope of the privacy study undertaken is limited to the wireless part of the 5G system which occurs between the service provider\u27s base station and the subscriber\u27s mobile phone. Although 5G offers better privacy guarantees than its predecessors, this work highlights that there still remain significant issues which need rectifying. We undertook an endeavor to (i) compile the privacy vulnerabilities that already existed in the previous mobile telephony generations. Thereafter, (ii) the privacy improvements offered by the recently finalized 5G standard were aggregated. Consequently, (iii) we were able to highlight privacy issues from previous generations that remain unresolved in 5G Release 15. For completeness, (iv) we also explore new privacy attacks which surfaced after the publication of the 5G standard. To address the identified privacy gaps, we also present future research directions in the form of proposed improvements

Undermining User Privacy on Mobile Devices Using AI

Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to the privacy of mobile phone users. This is because applications leave distinct footprints in the processor, which can be used by malware to infer user activities. In this work, we show that these inference attacks are considerably more practical when combined with advanced AI techniques. In particular, we focus on profiling the activity in the last-level cache (LLC) of ARM processors. We employ a simple Prime+Probe based monitoring technique to obtain cache traces, which we classify with Deep Learning methods including Convolutional Neural Networks. We demonstrate our approach on an off-the-shelf Android phone by launching a successful attack from an unprivileged, zeropermission App in well under a minute. The App thereby detects running applications with an accuracy of 98% and reveals opened websites and streaming videos by monitoring the LLC for at most 6 seconds. This is possible, since Deep Learning compensates measurement disturbances stemming from the inherently noisy LLC monitoring and unfavorable cache characteristics such as random line replacement policies. In summary, our results show that thanks to advanced AI techniques, inference attacks are becoming alarmingly easy to implement and execute in practice. This once more calls for countermeasures that confine microarchitectural leakage and protect mobile phone applications, especially those valuing the privacy of their users

Combatting Advanced Persistent Threat via Causality Inference and Program Analysis

Cyber attackers are becoming more and more sophisticated. In particular, Advanced Persistent Threat (APT) is a new class of attack that targets a specifc organization and compromises systems over a long time without being detected. Over the years, we have seen notorious examples of APTs including Stuxnet which disrupted Iranian nuclear centrifuges and data breaches affecting millions of users. Investigating APT is challenging as it occurs over an extended period of time and the attack process is highly sophisticated and stealthy. Also, preventing APTs is diffcult due to ever-expanding attack vectors. In this dissertation, we present proposals for dealing with challenges in attack investigation. Specifcally, we present LDX which conducts precise counter-factual causality inference to determine dependencies between system calls (e.g., between input and output system calls) and allows investigators to determine the origin of an attack (e.g., receiving a spam email) and the propagation path of the attack, and assess the consequences of the attack. LDX is four times more accurate and two orders of magnitude faster than state-of-the-art taint analysis techniques. Moreover, we then present a practical model-based causality inference system, MCI, which achieves precise and accurate causality inference without requiring any modifcation or instrumentation in end-user systems. Second, we show a general protection system against a wide spectrum of attack vectors and methods. Specifcally, we present A2C that prevents a wide range of attacks by randomizing inputs such that any malicious payloads contained in the inputs are corrupted. The protection provided by A2C is both general (e.g., against various attack vectors) and practical (7% runtime overhead)