Publications

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The recent paradigm shift introduced by the Internet of Things (IoT) has brought embedded systems into focus as a target for both security analysts and malicious adversaries. Typified by their lack of standardized hardware, diverse software, and opaque functionality, IoT devices present unique challenges to security analysts due to the tight coupling between their firmware and the hardware for which it was designed. In order to take advantage of modern program analysis techniques, such as fuzzing or symbolic execution, with any kind of scale or depth, analysts must have the ability to execute firmware code in emulated (or virtualized) environments. However, these emulation environments are rarely available and are cumbersome to create through manual reverse engineering, greatly limiting the analysis of binary firmware. In this work, we explore the problem of firmware re-hosting, the process by which firmware is migrated from its original hardware environment into a virtualized one. We show that an approach capable of creating virtual, interactive environments in an automated manner is a necessity to enable firmware analysis at scale. We present the first proof-of-concept system aiming to achieve this goal, called PRETENDER, which uses observations of the interactions between the original hardware and the firmware to automatically create models of peripherals, and allows for the execution of the firmware in a fully-emulated environment. Unlike previous approaches, these models are interactive, stateful, and transferable, meaning they are designed to allow the program to receive and process new input, a requirement of many analyses. We demonstrate our approach on multiple hardware platforms and firmware samples, and show that the models are flexible enough to allow for virtualized code execution, the exploration of new code paths, and the identification of security vulnerabilities.

In the past few years, both the industry and the academic communities have developed several approaches to detect malicious Android apps. State-of-the-art research approaches achieve very high accuracy when performing malware detection on existing datasets. These approaches perform their malware classification tasks in an "offline" scenario, where malware authors cannot learn from and adapt their malicious apps to these systems. In real-world deployments, however, adversaries get feedback about whether their app was detected, and can react accordingly by transforming their code until they are able to influence the classification. In this work, we propose a new approach for detecting Android malware that is designed to be resilient to feature-unaware pertur¬ bations without retraining. Our work builds on two key ideas. First, we consider only a subset of the codebase of a given app, both for precision and performance aspects. For this paper, our implementation focuses exclusively on the loops contained in a given app. We hypothesize, and empirically verify, that the code contained in apps' loops is enough to precisely detect malware. This provides the additional benefits of being less prone to noise and errors, and being more performant. The second idea is to build a feature space by extracting a set of labels for each loop, and by then considering each unique combination of these labels as a different feature: The combinatorial nature of this feature space makes it prohibitively difficult for an attacker to influence our feature vector and avoid detection, without access to the speciic model used for classiication. We assembled these techniques into a prototype, called L O O P M C, which can locate loops in applications, extract features, and perform classification, without requiring source code. We used L O O P M C to classify about 20,000 benign and malicious applications. While focusing on a smaller portion of the program may seem counter-intuitive, the results of these experiments are surprising: our system achieves a classification accuracy of 99.3% and 99.1% for the Malware Genome Project and VirusShare datasets respectively, which outperforms previous approaches. We also evaluated L O O P M C, along with the related work, in the context of various evasion techniques, and show that our system is more resilient to evasion.

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Outlier detection has been shown to be a promising machine learning technique for a diverse array of felds and problem areas. However, traditional, supervised outlier detection is not well suited for problems such as network intrusion detection, where proper labelled data is scarce. This has created a focus on extending these approaches to be unsupervised, removing the need for explicit labels, but at a cost of poorer performance compared to their supervised counterparts. Recent work has explored ways of making up for this, such as creating ensembles of diverse models, or even diverse learning algorithms, to jointly classify data. While using unsupervised, heterogeneous ensembles of learning algorithms has been proposed as a viable next step for research, the implications of how these ensembles are built and used has not been explored.

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