Studying JavaScript Security Through Static Analysis

Mit dem stetigen Wachstum des Internets wächst auch das Interesse von Angreifern. Ursprünglich sollte das Internet Menschen verbinden; gleichzeitig benutzen aber Angreifer diese Vernetzung, um Schadprogramme wirksam zu verbreiten. Insbesondere JavaScript ist zu einem beliebten Angriffsvektor geworden, da es Angreifer ermöglicht Bugs und weitere Sicherheitslücken auszunutzen, und somit die Sicherheit und Privatsphäre der Internetnutzern zu gefährden. In dieser Dissertation fokussieren wir uns auf die Erkennung solcher Bedrohungen, indem wir JavaScript Code statisch und effizient analysieren. Zunächst beschreiben wir unsere zwei Detektoren, welche Methoden des maschinellen Lernens mit statischen Features aus Syntax, Kontroll- und Datenflüssen kombinieren zur Erkennung bösartiger JavaScript Dateien. Wir evaluieren daraufhin die Verlässlichkeit solcher statischen Systeme, indem wir bösartige JavaScript Dokumente umschreiben, damit sie die syntaktische Struktur von bestehenden gutartigen Skripten reproduzieren. Zuletzt studieren wir die Sicherheit von Browser Extensions. Zu diesem Zweck modellieren wir Extensions mit einem Graph, welcher Kontroll-, Daten-, und Nachrichtenflüsse mit Pointer Analysen kombiniert, wodurch wir externe Flüsse aus und zu kritischen Extension-Funktionen erkennen können. Insgesamt wiesen wir 184 verwundbare Chrome Extensions nach, welche die Angreifer ausnutzen könnten, um beispielsweise beliebigen Code im Browser eines Opfers auszuführen.As the Internet keeps on growing, so does the interest of malicious actors. While the Internet has become widespread and popular to interconnect billions of people, this interconnectivity also simplifies the spread of malicious software. Specifically, JavaScript has become a popular attack vector, as it enables to stealthily exploit bugs and further vulnerabilities to compromise the security and privacy of Internet users. In this thesis, we approach these issues by proposing several systems to statically analyze real-world JavaScript code at scale. First, we focus on the detection of malicious JavaScript samples. To this end, we propose two learning-based pipelines, which leverage syntactic, control and data-flow based features to distinguish benign from malicious inputs. Subsequently, we evaluate the robustness of such static malicious JavaScript detectors in an adversarial setting. For this purpose, we introduce a generic camouflage attack, which consists in rewriting malicious samples to reproduce existing benign syntactic structures. Finally, we consider vulnerable browser extensions. In particular, we abstract an extension source code at a semantic level, including control, data, and message flows, and pointer analysis, to detect suspicious data flows from and toward an extension privileged context. Overall, we report on 184 Chrome extensions that attackers could exploit to, e.g., execute arbitrary code in a victim's browser

Techniques for advanced android malware triage

Mención Internacional en el título de doctorAndroid is the leading operating system in smartphones with a big difference. Statistics show that 88% of all smartphones sold to end users in the second quarter of 2018 were phones with the Android OS. Regardless of the operating systems which are running on smartphones, most of the functionalities of these devices are offered through applications. There are currently over 2 million apps only on the official Google store, known as Google Play. This huge market with billions of users is tempting for attackers to develop and distribute their malicious apps (or malware). Mobile malware has raised explosively since 2009. Symantec reported an increase of 54% in the new mobile malware variants in 2017 as compared to the previous year. Additionally, more incentive has been provided for profit-driven malware by the growth of black markets. This rise has happened for Android malware as well since only 20% of devices are running the newest major version of Android OS based on Symantec report in 2018. Android continued to be the most targeted platform with the biggest number of attacks in 2015. After that year, attacks against the Android platform slowed for the first time as attackers were faced with improved security architectures though Android is still the main appealing target OS for attackers. Moreover, advanced types of Android malware are found which make use of extensive anit-analysis techniques to evade static or dynamic analysis. To address the security and privacy concerns of complex Android malware, this dissertation focuses on three main objectives. First of all, we propose a light-weight yet efficient method to identify risky Android applications. Next, we present a precise approach to characterize Android malware based on their malicious behavior. Finally, we propose an adaptive learning system to address the security concerns of obfuscation in Android malware. Identifying potentially dangerous and risky applications is an important step in Android malware analysis. To this end, we develop a triage system to rank applications based on their potential risk. Our approach, called TriFlow, relies on static features which are quick to obtain. TriFlow combines a probabilistic model to predict the existence of information flows with a metric of how significant a flow is in benign and malicious apps. Based on this, TriFlow provides a score for each application that can be used to prioritize analysis. It also provides the analysts with an explanatory report of the associated risk. Our tool can also be used as a complement with computationally expensive static and dynamic analysis tools. Another important step towards Android malware analysis lies in their accurate characterization. Labeling Android malware is challenging yet crucially important, as it helps to identify upcoming malware samples and threats. A key challenge is that different researchers and anti-virus vendors assign labels using their own criteria, and it is not known to what extent these labels are aligned with the apps’ real behavior. Based on this, we propose a new behavioral characterization method for Android apps based on their extracted information flows. As information flows can be used to track why and how apps use specific pieces of information, a flowbased characterization provides a relatively easy-to-interpret summary of the malware sample’s behavior. Not all Android malware are easy to analyze due to advanced and easyto-apply anti-analysis techniques that are available nowadays. Obfuscation is the most common anti-analysis technique that Android malware use to evade detection. Obfuscation techniques modify an app’s source (or machine) code in order to make it more difficult to analyze. This is typically applied to protect intellectual property in benign apps, or to hinder the process of extracting actionable information in the case of malware. Since malware analysis often requires considerable resource investment, detecting the particular obfuscation technique used may contribute to apply the right analysis tools, thus leading to some savings. Therefore, we propose AndrODet, a mechanism to detect three popular types of obfuscation in Android applications, namely identifier renaming, string encryption, and control flow obfuscation. AndrODet leverages online learning techniques, thus being suitable for resource-limited environments that need to operate in a continuous manner. We compare our results with a batch learning algorithm using a dataset of 34,962 apps from both malware and benign apps. Experimental results show that online learning approaches are not only able to compete with batch learning methods in terms of accuracy, but they also save significant amount of time and computational resources. Finally, we present a number of open research directions based on the outcome of this thesis.Android es el sistema operativo líder en teléfonos inteligentes (también denominados con la palabra inglesa smartphones), con una gran diferencia con respecto al resto de competidores. Las estadísticas muestran que el 88% de todos los smartphones vendidos a usuarios finales en el segundo trimestre de 2018 fueron teléfonos con sistema operativo Android. Independientemente de su sistema operativo, la mayoría de las funcionalidades de estos dispositivos se ofrecen a través de aplicaciones. Actualmente hay más de 2 millones de aplicaciones solo en la tienda oficial de Google, conocida como Google Play. Este enorme mercado con miles de millones de usuarios es tentador para los atacantes, que buscan distribuir sus aplicaciones malintencionadas (o malware). El malware para dispositivos móviles ha aumentado de forma exponencial desde 2009. Symantec ha detectado un aumento del 54% en las nuevas variantes de malware para dispositivos móviles en 2017 en comparación con el año anterior. Además, el crecimiento del mercado negro (es decir, plataformas no oficiales de descargas de aplicaciones) supone un incentivo para los programas maliciosos con fines lucrativos. Este aumento también ha ocurrido en el malware de Android, aprovechando la circunstancia de que solo el 20% de los dispositivos ejecutan la versión mas reciente del sistema operativo Android, de acuerdo con el informe de Symantec en 2018. De hecho, Android ha sido la plataforma que ha centrado los esfuerzos de los atacantes desde 2015, aunque los ataques decayeron ligeramente tras ese año debido a las mejoras de seguridad incorporadas en el sistema operativo. En todo caso, existen formas avanzadas de malware para Android que hacen uso de técnicas sofisticadas para evadir el análisis estático o dinámico. Para abordar los problemas de seguridad y privacidad que causa el malware en Android, esta Tesis se centra en tres objetivos principales. En primer lugar, se propone un método ligero y eficiente para identificar aplicaciones de Android que pueden suponer un riesgo. Por otra parte, se presenta un mecanismo para la caracterización del malware atendiendo a su comportamiento. Finalmente, se propone un mecanismo basado en aprendizaje adaptativo para la detección de algunos tipos de ofuscación que son empleados habitualmente en las aplicaciones maliciosas. Identificar aplicaciones potencialmente peligrosas y riesgosas es un paso importante en el análisis de malware de Android. Con este fin, en esta Tesis se desarrolla un mecanismo de clasificación (llamado TriFlow) que ordena las aplicaciones según su riesgo potencial. La aproximación se basa en características estáticas que se obtienen rápidamente, siendo de especial interés los flujos de información. Un flujo de información existe cuando un cierto dato es recibido o producido mediante una cierta función o llamada al sistema, y atraviesa la lógica de la aplicación hasta que llega a otra función. Así, TriFlow combina un modelo probabilístico para predecir la existencia de un flujo con una métrica de lo habitual que es encontrarlo en aplicaciones benignas y maliciosas. Con ello, TriFlow proporciona una puntuación para cada aplicación que puede utilizarse para priorizar su análisis. Al mismo tiempo, proporciona a los analistas un informe explicativo de las causas que motivan dicha valoración. Así, esta herramienta se puede utilizar como complemento a otras técnicas de análisis estático y dinámico que son mucho más costosas desde el punto de vista computacional. Otro paso importante hacia el análisis de malware de Android radica en caracterizar su comportamiento. Etiquetar el malware de Android es un desafío de crucial importancia, ya que ayuda a identificar las próximas muestras y amenazas de malware. Una cuestión relevante es que los diferentes investigadores y proveedores de antivirus asignan etiquetas utilizando sus propios criterios, de modo no se sabe en qué medida estas etiquetas están en línea con el comportamiento real de las aplicaciones. Sobre esta base, en esta Tesis se propone un nuevo método de caracterización de comportamiento para las aplicaciones de Android en función de sus flujos de información. Como dichos flujos se pueden usar para estudiar el uso de cada dato por parte de una aplicación, permiten proporcionar un resumen relativamente sencillo del comportamiento de una determinada muestra de malware. A pesar de la utilidad de las técnicas de análisis descritas, no todos los programas maliciosos de Android son fáciles de analizar debido al uso de técnicas anti-análisis que están disponibles en la actualidad. Entre ellas, la ofuscación es la técnica más común que se utiliza en el malware de Android para evadir la detección. Dicha técnica modifica el código de una aplicación para que sea más difícil de entender y analizar. Esto se suele aplicar para proteger la propiedad intelectual en aplicaciones benignas o para dificultar la obtención de pistas sobre su funcionamiento en el caso del malware. Dado que el análisis de malware a menudo requiere una inversión considerable de recursos, detectar la técnica de ofuscación que se ha utilizado en un caso particular puede contribuir a utilizar herramientas de análisis adecuadas, contribuyendo así a un cierto ahorro de recursos. Así, en esta Tesis se propone AndrODet, un mecanismo para detectar tres tipos populares de ofuscación, a saber, el renombrado de identificadores, cifrado de cadenas de texto y la modificación del flujo de control de la aplicación. AndrODet se basa en técnicas de aprendizaje automático en línea (online machine learning), por lo que es adecuado para entornos con recursos limitados que necesitan operar de forma continua, sin interrupción. Para medir su eficacia respecto de las técnicas de aprendizaje automático tradicionales, se comparan los resultados con un algoritmo de aprendizaje por lotes (batch learning) utilizando un dataset de 34.962 aplicaciones de malware y benignas. Los resultados experimentales muestran que el enfoque de aprendizaje en línea no solo es capaz de competir con el basado en lotes en términos de precisión, sino que también ahorra una gran cantidad de tiempo y recursos computacionales. Tras la exposición de las contribuciones anteriormente mencionadas, esta Tesis concluye con la identificación de una serie de líneas abiertas de investigación con el fin de alentar el desarrollo de trabajos futuros en esta dirección.Omid Mirzaei is a Ph.D. candidate in the Computer Security Lab (COSEC) at the Department of Computer Science and Engineering of Universidad Carlos III de Madrid (UC3M). His Ph.D. is funded by the Community of Madrid and the European Union through the research project CIBERDINE (Ref. S2013/ICE-3095).Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Gregorio Martínez Pérez.- Secretario: Pedro Peris López.- Vocal: Pablo Picazo Sánche

Emerging & Unconventional Malware Detection Using a Hybrid Approach

Advancement in computing technologies made malware development easier for malware authors. Unconventional computing paradigms such as cloud computing, the internet of things, In-memory computing, etc. introduced new ways to develop more complex and effective malware. To demonstrate this, we designed and implemented a fileless malware that could infect any device that supports JavaScript and HTML5. In addition, another proof-of-concept is implemented that signifies the security threat of in-memory malware for in-memory data storage and computing platforms. Furthermore, a detailed analysis of unconventional malware has been performed using current state-of-the-art malware analysis and detection techniques. Our analysis shows that, by utilizing the unique characteristics of emerging technologies, malware attacks could easily deceive the anti-malware tools and evade themselves from detection. This clearly demonstrates the need for an innovative and effective detection mechanism. Because of the limitations of existing techniques, we propose a hybrid approach using specification-based and behavioral analysis techniques together as an effective solution against unconventional and emerging malware instances. Our approach begins with the specification development where we present the way of writing it in a succinct manner to describe the expected behavior of the application. Moreover, the behavior monitoring component of our approach makes the detection mechanism effective enough by matching the actual behavior with pre-defined specifications at run-time and alarms the system if any action violates the expected behavior. We demonstrate the effectiveness of the proposed approach by applying it for the detection of in-memory malware that threatens the HazelCast in-memory data grid platform. In our experiments, we evaluated the performance and effectiveness of the approach by considering the possible use cases where in-memory malware could affect the data present in the storage space of HazelCast IMDG

Program Similarity Analysis for Malware Classification and its Pitfalls

Malware classification, specifically the task of grouping malware samples into families according to their behaviour, is vital in order to understand the threat they pose and how to protect against them. Recognizing whether one program shares behaviors with another is a task that requires semantic reasoning, meaning that it needs to consider what a program actually does. This is a famously uncomputable problem, due to Rice\u2019s theorem. As there is no one-size-fits-all solution, determining program similarity in the context of malware classification requires different tools and methods depending on what is available to the malware defender. When the malware source code is readily available (or at least, easy to retrieve), most approaches employ semantic \u201cabstractions\u201d, which are computable approximations of the semantics of the program. We consider this the first scenario for this thesis: malware classification using semantic abstractions extracted from the source code in an open system. Structural features, such as the control flow graphs of programs, can be used to classify malware reasonably well. To demonstrate this, we build a tool for malware analysis, R.E.H.A. which targets the Android system and leverages its openness to extract a structural feature from the source code of malware samples. This tool is first successfully evaluated against a state of the art malware dataset and then on a newly collected dataset. We show that R.E.H.A. is able to classify the new samples into their respective families, often outperforming commercial antivirus software. However, abstractions have limitations by virtue of being approximations. We show that by increasing the granularity of the abstractions used to produce more fine-grained features, we can improve the accuracy of the results as in our second tool, StranDroid, which generates fewer false positives on the same datasets. The source code of malware samples is not often available or easily retrievable. For this reason, we introduce a second scenario in which the classification must be carried out with only the compiled binaries of malware samples on hand. Program similarity in this context cannot be done using semantic abstractions as before, since it is difficult to create meaningful abstractions from zeros and ones. Instead, by treating the compiled programs as raw data, we transform them into images and build upon common image classification algorithms using machine learning. This led us to develop novel deep learning models, a convolutional neural network and a long short-term memory, to classify the samples into their respective families. To overcome the usual obstacle of deep learning of lacking sufficiently large and balanced datasets, we utilize obfuscations as a data augmentation tool to generate semantically equivalent variants of existing samples and expand the dataset as needed. Finally, to lower the computational cost of the training process, we use transfer learning and show that a model trained on one dataset can be used to successfully classify samples in different malware datasets. The third scenario explored in this thesis assumes that even the binary itself cannot be accessed for analysis, but it can be executed, and the execution traces can then be used to extract semantic properties. However, dynamic analysis lacks the formal tools and frameworks that exist in static analysis to allow proving the effectiveness of obfuscations. For this reason, the focus shifts to building a novel formal framework that is able to assess the potency of obfuscations against dynamic analysis. We validate the new framework by using it to encode known analyses and obfuscations, and show how these obfuscations actually hinder the dynamic analysis process

Evaluation Methodologies in Software Protection Research

Man-at-the-end (MATE) attackers have full control over the system on which the attacked software runs, and try to break the confidentiality or integrity of assets embedded in the software. Both companies and malware authors want to prevent such attacks. This has driven an arms race between attackers and defenders, resulting in a plethora of different protection and analysis methods. However, it remains difficult to measure the strength of protections because MATE attackers can reach their goals in many different ways and a universally accepted evaluation methodology does not exist. This survey systematically reviews the evaluation methodologies of papers on obfuscation, a major class of protections against MATE attacks. For 572 papers, we collected 113 aspects of their evaluation methodologies, ranging from sample set types and sizes, over sample treatment, to performed measurements. We provide detailed insights into how the academic state of the art evaluates both the protections and analyses thereon. In summary, there is a clear need for better evaluation methodologies. We identify nine challenges for software protection evaluations, which represent threats to the validity, reproducibility, and interpretation of research results in the context of MATE attacks

Software for malicious macro detection

The objective of this work is to give a detailed study of the development process of a software tool for the detection of the Emotet virus in Microsoft Office files, Emotet is a virus that has been wreaking havoc mainly in the business environment, from its beginnings as a banking Trojan to nowadays. In fact, this polymorphic family has managed to generate evident, incalculable and global inconveniences in the business activity without discriminating by corporate typology, affecting any company regardless of its size or sector, even entering into government agencies, as well as the citizens themselves as a whole. The existence of two main obstacles for the detection of this virus, constitute an intrinsic reality to it, on the one hand, the obfuscation in its macros and on the other, its polymorphism, are essential pieces of the analysis, focusing our tool in facing precisely two obstacles, descending to the analysis of the macros features and the creation of a neuron network that uses machine learning to recognize the detection patterns and deliberate its malicious nature. With Emotet's in-depth nature analysis, our goal is to draw out a set of features from the malicious macros and build a machine learning model for their detection. After the feasibility study of this project, its design and implementation, the results that emerge endorse the intention to detect Emotet starting only from the static analysis and with the application of machine learning techniques. The detection ratios shown by the tests performed on the final model, present a accuracy of 84% and only 3% of false positives during this detection process.Grado en Ingeniería Informátic

Survey on representation techniques for malware detection system

Malicious programs are malignant software’s designed by hackers or cyber offenders with a harmful intent to disrupt computer operation. In various researches, we found that the balance between designing an accurate architecture that can detect the malware and track several advanced techniques that malware creators apply to get variants of malware are always a difficult line. Hence the study of malware detection techniques has become more important and challenging within the security field. This review paper provides a detailed discussion and full reviews for various types of malware, malware detection techniques, various researches on them, malware analysis methods and different dynamic programmingbased tools that could be used to represent the malware sampled. We have provided a comprehensive bibliography in malware detection, its techniques and analysis methods for malware researchers

The Effect of Code Obfuscation on Authorship Attribution of Binary Computer Files

In many forensic investigations, questions linger regarding the identity of the authors of the software specimen. Research has identified methods for the attribution of binary files that have not been obfuscated, but a significant percentage of malicious software has been obfuscated in an effort to hide both the details of its origin and its true intent. Little research has been done around analyzing obfuscated code for attribution. In part, the reason for this gap in the research is that deobfuscation of an unknown program is a challenging task. Further, the additional transformation of the executable file introduced by the obfuscator modifies or removes features from the original executable that would have been used in the author attribution process. Existing research has demonstrated good success in attributing the authorship of an executable file of unknown provenance using methods based on static analysis of the specimen file. With the addition of file obfuscation, static analysis of files becomes difficult, time consuming, and in some cases, may lead to inaccurate findings. This paper presents a novel process for authorship attribution using dynamic analysis methods. A software emulated system was fully instrumented to become a test harness for a specimen of unknown provenance, allowing for supervised control, monitoring, and trace data collection during execution. This trace data was used as input into a supervised machine learning algorithm trained to identify stylometric differences in the specimen under test and provide predictions on who wrote the specimen. The specimen files were also analyzed for authorship using static analysis methods to compare prediction accuracies with prediction accuracies gathered from this new, dynamic analysis based method. Experiments indicate that this new method can provide better accuracy of author attribution for files of unknown provenance, especially in the case where the specimen file has been obfuscated

Dissection of Modern Malicious Software

The exponential growth of the number of malicious software samples, known by malware in the specialized literature, constitutes nowadays one of the major concerns of cyber-security professionals. The objectives of the creators of this type of malware are varied, and the means used to achieve them are getting increasingly sophisticated. The increase of the computation and storage resources, as well as the globalization have been contributing to this growth, and fueling an entire industry dedicated to developing, selling and improving systems or solutions for securing, recovering, mitigating and preventing malware related incidents. The success of these systems typically depends of detailed analysis, often performed by humans, of malware samples captured in the wild. This analysis includes the search for patterns or anomalous behaviors that may be used as signatures to identify or counter-attack these threats. This Master of Science (Ms.C.) dissertation addresses problems related with dissecting and analyzing malware. The main objectives of the underlying work were to study and understand the techniques used by this type of software nowadays, as well as the methods that are used by specialists on that analysis, so as to conduct a detailed investigation and produce structured documentation for at least one modern malware sample. The work was mostly focused in malware developed for the Operating Systems (OSs) of the Microsoft Windows family for desktops. After a brief study of the state of the art, the dissertation presents the classifications applied to malware, which can be found in the technical literature on the area, elaborated mainly by an industry community or seller of a security product. The structuring of the categories is nonetheless the result of an effort to unify or complete different classifications. The families of some of the most popular or detected malware samples are also presented herein, initially in a tabular form and, subsequently, via a genealogical tree, with some of the variants of each previously described family. This tree provides an interesting perspective over malware and is one of the contributions of this programme. Within the context of the description of functionalities and behavior of malware, some advanced techniques, with which modern specimens of this type of software are equipped to ease their propagation and execution, while hindering their detection, are then discussed with more detail. The discussion evolves to the presentation of the concepts related to the detection and defense against modern malware, along with a small introduction to the main subject of this work. The analysis and dissection of two samples of malware is then the subject of the final chapters of the dissertation. A basic static analysis is performed to the malware known as Stuxnet, while the Trojan Banker known as Tinba/zuzy is subdued to both basic and advanced dynamic analysis. The results of this part of the work emphasize difficulties associated with these tasks and the sophistication and dangerous level of samples under investigation.O crescimento exponencial do número de amostras de software malicioso, conhecido na gíria informática como malware, constitui atualmente uma das maiores preocupações dos profissionais de cibersegurança. São vários os objetivos dos criadores deste tipo de software e a forma cada vez mais sofisticada como os mesmos são alcançados. O aumento da computação e capacidade de armazenamento, bem como a globalização, têm contribuído para este crescimento, e têm alimentado toda uma indústria dedicada ao desenvolvimento, venda e melhoramento de sistemas ou soluções de segurança, recuperação, mitigação e prevenção de incidentes relacionados com malware. O sucesso destes sistemas depende normalmente da análise detalhada, feita muitas vezes por humanos, de peças de malware capturadas no seu ambiente de atuação. Esta análise compreende a procura de padrões ou de comportamentos anómalos que possam servir de assinatura para identificar ou contra-atacar essas ameaças. Esta dissertação aborda a problemática da análise e dissecação de malware. O trabalho que lhe está subjacente tinha como objetivos estudar e compreender as técnicas utilizadas por este tipo de software hoje em dia, bem como as que são utilizadas por especialistas nessa análise, de forma a conduzir uma investigação detalhada e a produzir documentação estruturada sobre pelo menos uma amostra de malware moderna. O trabalho focou-se, sobretudo, em malware desenvolvido para os sistemas operativos da família Microsoft Windows para computadores de secretária. Após um breve estudo ao estado da arte, a dissertação apresenta as classificações de malware encontradas na literatura técnica da especialidade, principalmente usada pela indústria, resultante de um esforço de unificação das mesmas. São também apresentadas algumas das famílias de malware mais detetadas da atualidade, inicialmente através de uma tabela e, posteriormente, através de uma árvore geneológica, com algumas das variantes de cada uma das famílias descritas previamente. Esta árvore fornece uma perspetiva interessante sobre malware e constitui uma das contribuições deste programa de mestrado. Ainda no âmbito da descrição de funcionalidades e comportamentos do malware, são expostas, com algum detalhe, algumas técnicas avançadas com as quais os programas maliciosos mais modernos são por vezes munidos com o intuito a facilitar a sua propagação e execução, dificultando a sua deteção. A descrição evolui para a apresentação dos conceitos adjacentes à deteção e combate ao malware moderno, assim como para uma pequena introdução ao tema principal deste trabalho. A análise e dissecação de duas amostras de malware moderno surgem nos capítulos finais da dissertação. Ao malware conhecido por Stuxnet é feita a análise básica estática, enquanto que ao Trojan Banker Tinba/zusy é feita e demonstrada a análise dinâmica básica e avançada. Os resultados desta parte são demonstrativos do grau de sofisticação e perigosidade destas amostras e das dificuldades associadas a estas tarefas