CALIPER: Continuous Authentication Layered with Integrated PKI Encoding Recognition

Architectures relying on continuous authentication require a secure way to challenge the user's identity without trusting that the Continuous Authentication Subsystem (CAS) has not been compromised, i.e., that the response to the layer which manages service/application access is not fake. In this paper, we introduce the CALIPER protocol, in which a separate Continuous Access Verification Entity (CAVE) directly challenges the user's identity in a continuous authentication regime. Instead of simply returning authentication probabilities or confidence scores, CALIPER's CAS uses live hard and soft biometric samples from the user to extract a cryptographic private key embedded in a challenge posed by the CAVE. The CAS then uses this key to sign a response to the CAVE. CALIPER supports multiple modalities, key lengths, and security levels and can be applied in two scenarios: One where the CAS must authenticate its user to a CAVE running on a remote server (device-server) for access to remote application data, and another where the CAS must authenticate its user to a locally running trusted computing module (TCM) for access to local application data (device-TCM). We further demonstrate that CALIPER can leverage device hardware resources to enable privacy and security even when the device's kernel is compromised, and we show how this authentication protocol can even be expanded to obfuscate direct kernel object manipulation (DKOM) malwares.Comment: Accepted to CVPR 2016 Biometrics Worksho

ERASMUS: Efficient Remote Attestation via Self- Measurement for Unattended Settings

Remote attestation (RA) is a popular means of detecting malware in embedded and IoT devices. RA is usually realized as an interactive protocol, whereby a trusted party -- verifier -- measures integrity of a potentially compromised remote device -- prover. Early work focused on purely software-based and fully hardware-based techniques, neither of which is ideal for low-end devices. More recent results have yielded hybrid (SW/HW) security architectures comprised of a minimal set of features to support efficient and secure RA on low-end devices. All prior RA techniques require on-demand operation, i.e, RA is performed in real time. We identify some drawbacks of this general approach in the context of unattended devices: First, it fails to detect mobile malware that enters and leaves the prover between successive RA instances. Second, it requires the prover to engage in a potentially expensive (in terms of time and energy) computation, which can be harmful for critical or real-time devices. To address these drawbacks, we introduce the concept of self-measurement where a prover device periodically (and securely) measures and records its own software state, based on a pre-established schedule. A possibly untrusted verifier occasionally collects and verifies these measurements. We present the design of a concrete technique called ERASMUS : Efficient Remote Attestation via Self-Measurement for Unattended Settings, justify its features and evaluate its performance. In the process, we also define a new metric -- Quality of Attestation (QoA). We argue that ERASMUS is well-suited for time-sensitive and/or safety-critical applications that are not served well by on-demand RA. Finally, we show that ERASMUS is a promising stepping stone towards handling attestation of multiple devices (i.e., a group or swarm) with high mobility

Malicious cryptography techniques for unreversable (malicious or not) binaries

Fighting against computer malware require a mandatory step of reverse engineering. As soon as the code has been disassemblied/decompiled (including a dynamic analysis step), there is a hope to understand what the malware actually does and to implement a detection mean. This also applies to protection of software whenever one wishes to analyze them. In this paper, we show how to amour code in such a way that reserse engineering techniques (static and dymanic) are absolutely impossible by combining malicious cryptography techniques developped in our laboratory and new types of programming (k-ary codes). Suitable encryption algorithms combined with new cryptanalytic approaches to ease the protection of (malicious or not) binaries, enable to provide both total code armouring and large scale polymorphic features at the same time. A simple 400 Kb of executable code enables to produce a binary code and around $2^{140}$ mutated forms natively while going far beyond the old concept of decryptor.Comment: 17 pages, 2 figures, accepted for presentation at H2HC'1

Adversarial Detection of Flash Malware: Limitations and Open Issues

During the past four years, Flash malware has become one of the most insidious threats to detect, with almost 600 critical vulnerabilities targeting Adobe Flash disclosed in the wild. Research has shown that machine learning can be successfully used to detect Flash malware by leveraging static analysis to extract information from the structure of the file or its bytecode. However, the robustness of Flash malware detectors against well-crafted evasion attempts - also known as adversarial examples - has never been investigated. In this paper, we propose a security evaluation of a novel, representative Flash detector that embeds a combination of the prominent, static features employed by state-of-the-art tools. In particular, we discuss how to craft adversarial Flash malware examples, showing that it suffices to manipulate the corresponding source malware samples slightly to evade detection. We then empirically demonstrate that popular defense techniques proposed to mitigate evasion attempts, including re-training on adversarial examples, may not always be sufficient to ensure robustness. We argue that this occurs when the feature vectors extracted from adversarial examples become indistinguishable from those of benign data, meaning that the given feature representation is intrinsically vulnerable. In this respect, we are the first to formally define and quantitatively characterize this vulnerability, highlighting when an attack can be countered by solely improving the security of the learning algorithm, or when it requires also considering additional features. We conclude the paper by suggesting alternative research directions to improve the security of learning-based Flash malware detectors

Teaching with Twitter:reflections on practices, opportunities and problems

In recent times there has been an increasing wave of interest in the use of Social Media for Teaching and Learning in Higher Education. In particular, the micro-blogging platform Twitter has been experimentally used in various Universities world-wide. There are relevant publications reporting on experimentations with Twitter for reaching diverse learning goals, including better engagement, informal learning or collaboration among students. Existing research papers on the use of Twitter however focus exclusively on the positive aspects of experimentations, on what went well in the use of Twitter. In our University we run a small project on the use of Twitter with goals that are similar to those of others: fostering participation and better learning processes. In this paper we report on our project and the strategies and best practices we adopted for using Twitter for teaching. We also reflect that in our experimentation however we encountered a number of practical problems connected for example with use of technology, with the class settings and with spam. In the conclusion we offer some recommendations for Teaching and Learning with Twitter based on our personal experience