We here present the PhD thesis and Masters dissertations developed in the SECRET lab and/or advised and/or co-advised by SECRET members.

Need for Speed : Analysis of Brazilian Malware Classifiers’ Expiration Date
Master Dissertation by Fabrício Ceschin (UFPR)
Advisor: André Grégio (UFPR)
Co-Advisor: David Menotti (UFPR)
Archived Here

New malware variants are produced and released daily to deceive users and overcome defense solutions, thus demanding continuous improvements on these mechanisms (e.g., antiviruses constant updates). Although most malware samples are usually “generic” enough to infect the same type of operating system world-widely, some of them are tied to the specificities regarding the cyberspace of certain target countries. In this work, we present an analysis of thousands of malware samples collected in the Brazilian cyberspace along several years, including their evolution and the impact of this evolution on malware classification. We also share a labeled dataset of this Brazilian malware set to allow other experiments and comparisons by the community. This dataset is representative of the Brazilian cyberspace and contains profiles of known-bad and known-good programs based on binaries’ static features. Our analysis leveraged machine learning algorithms (in particular, we evaluated four popular off-the-shelf classifiers: Support Vector Machines, Multilayer Perceptron, KNN and Random Forest) to classify the programs of our dataset as malware or goodware (including experiments with thresholds) and to identify the potential concept drift that occurs when the subject of a classification scheme evolves as time goes by. We also provide extensive details about our dataset, which is composed of 38, 000 programs – 20, 000 labeled as known malware, collected from malicious email attachments/infected users (triaged in both cases by a major Brazilian financial institution with a country-wide distributed network) between 2013 and early 2017. For the sake of reproducibility and unbiased comparison, we make the feature vectors produced from our database publicly available. Finally, we discuss the results of the conducted experiments, whose analysis evidences the existence of concept drift on programs, either goodware and malware, and shows that it is not possible to say that there is seasonality in our dataset.

Hardware-Assisted Malware Analysis
Master Dissertation by Marcus Botacin (UNICAMP)
Advisor: Paulo de Geus (UNICAMP)
Co-Advisor: André Grégio (UFPR)
Archived Here

Today’s world is driven by the usage of computer systems, which are present in all aspectsof everyday life. Therefore, the correct working of these systems is essential to ensure themaintenance of the possibilities brought about by technological developments. However,ensuring the correct working of such systems is not an easy task, as many people attemptto subvert systems working for their own benefit. The most common kind of subversionagainst computer systems are malware attacks, which can make an attacker to gain com-plete machine control. The fight against this kind of threat is based on analysis proceduresof the collected malicious artifacts, allowing the incident response and the developmentof future countermeasures. However, attackers have specialized in circumventing analysissystems and thus keeping their operations active. For this purpose, they employ a seriesof techniques called anti-analysis, able to prevent the inspection of their malicious codes.Among these techniques, I highlight the analysis procedure evasion, that is, the usage ofsamples able to detect the presence of an analysis solution and then hide their maliciousbehavior. Evasive examples have become popular, and their impact on systems securityis considerable, since automatic analysis now requires human supervision in order to findevasion signs, which significantly raises the cost of maintaining a protected system. Themost common ways for detecting an analysis environment are: i) Injected code detec-tion, since injection is used by analysts to inspect applications on their way; ii) Virtualmachine detection, since they are used in analysis environments due to scalability issues;iii) Execution side effects detection, usually caused by emulators, also used by analysts.To handle evasive malware, analysts have relied on the so-called transparent techniques,that is, those which do not require code injection nor cause execution side effects. Away to achieve transparency in an analysis process is to rely on hardware support. Inthis way, this work covers the application of the hardware support for the evasive threatsanalysis purpose. In the course of this text, I present an assessment of existing hardwaresupport technologies, including hardware virtual machines, BIOS support, performancemonitors and PCI cards. My critical evaluation of such technologies provides basis forcomparing different usage cases. In addition, I pinpoint development gaps that currentlyexists. More than that, I fill one of these gaps by proposing to expand the usage ofperformance monitors for malware monitoring purposes. More specifically, I propose theusage of the BTS monitor for the purpose of developing a tracer and a debugger. Theproposed framework is also able of dealing with ROP attacks, one of the most commonused technique for remote vulnerability exploitation. The framework evaluation shows noside-effect is introduced, thus allowing transparent analysis. Making use of this capability,I demonstrate how protected applications can be inspected and how evasion techniquescan be identified.