CipherTrace: automatic detection of ciphers from execution traces to neutralize ransomware

In 2021, the largest US pipeline system for refined oil products suffered a 6-day shutdown due to a ransomware attack [1]. In 2023, the sensitive systems of the US Marshals Service were attacked by a ransomware [2]. One of the most effective ways to fight ransomware is to extract the secret keys. The challenge of detecting and identifying cryptographic primitives has been around for over a decade. Many tools have been proposed, but the vast majority of them use templates or signatures, and their support for different operating systems and processor architectures is rather limited; neither have there been enough tools capable of extracting the secret keys. In this paper, we present CipherTrace, a generic and automated system to detect and identify the class of cipher algorithms in binary programs, and additionally, locate and extract the secret keys and cryptographic states accessed by the cipher. We focus on product ciphers, and evaluate CipherTrace using four standard cipher algorithms, four different hashing algorithms, and five of the most recent and popular ransomware specimens. Our results show that CipherTrace is capable of fully dissecting Fixed S-Box block ciphers (e.g. AES and Serpent) and can extract the secret keys and other cryptographic artefacts, regardless of the operating system, implementation, or input- or key-size, and without using signatures or templates. We show a significant improvement in performance and functionality compared to the closely related works. CipherTrace helps in fighting ransomware, and aids analysts in their malware analysis and reverse engineering efforts

On the Effectiveness of System API-Related Information for Android Ransomware Detection

Ransomware constitutes a significant threat to the Android operating system. It can either lock or encrypt the target devices, and victims are forced to pay ransoms to restore their data. Hence, the prompt detection of such attacks has a priority in comparison to other malicious threats. Previous works on Android malware detection mainly focused on Machine Learning-oriented approaches that were tailored to identifying malware families, without a clear focus on ransomware. More specifically, such approaches resorted to complex information types such as permissions, user-implemented API calls, and native calls. However, this led to significant drawbacks concerning complexity, resilience against obfuscation, and explainability. To overcome these issues, in this paper, we propose and discuss learning-based detection strategies that rely on System API information. These techniques leverage the fact that ransomware attacks heavily resort to System API to perform their actions, and allow distinguishing between generic malware, ransomware and goodware. We tested three different ways of employing System API information, i.e., through packages, classes, and methods, and we compared their performances to other, more complex state-of-the-art approaches. The attained results showed that systems based on System API could detect ransomware and generic malware with very good accuracy, comparable to systems that employed more complex information. Moreover, the proposed systems could accurately detect novel samples in the wild and showed resilience against static obfuscation attempts. Finally, to guarantee early on-device detection, we developed and released on the Android platform a complete ransomware and malware detector (R-PackDroid) that employed one of the methodologies proposed in this paper

Identifying and combating cyber-threats in the field of online banking

This thesis has been carried out in the industrial environment external to the University, as an industrial PhD. The results of this PhD have been tested, validated, and implemented in the production environment of Caixabank and have been used as models for others who have followed the same ideas. The most burning threats against banks throughout the Internet environment are based on software tools developed by criminal groups, applications running on web environment either on the computer of the victim (Malware) or on their mobile device itself through downloading rogue applications (fake app's with Malware APP). Method of the thesis has been used is an approximation of qualitative exploratory research on the problem, the answer to this problem and the use of preventive methods to this problem like used authentication systems. This method is based on samples, events, surveys, laboratory tests, experiments, proof of concept; ultimately actual data that has been able to deduce the thesis proposal, using both laboratory research and grounded theory methods of data pilot experiments conducted in real environments. I've been researching the various aspects related to e-crime following a line of research focusing on intrinsically related topics: - The methods, means and systems of attack: Malware, Malware families of banker Trojans, Malware cases of use, Zeus as case of use. - The fixed platforms, mobile applications and as a means for malware attacks. - forensic methods to analyze the malware and infrastructure attacks. - Continuous improvement of methods of authentication of customers and users as a first line of defense anti- malware. - Using biometrics as innovative factor authentication.The line investigating Malware and attack systems intrinsically is closed related to authentication methods and systems to infect customer (executables, APP's, etc.), because the main purpose of malware is precisely steal data entered in the "logon "authentication system, to operate and thus, fraudulently, steal money from online banking customers. Experiments in the Malware allowed establishing a new method of decryption establishing guidelines to combat its effects describing his fraudulent scheme and operation infection. I propose a general methodology to break the encryption communications malware (keystream), extracting the system used to encrypt such communications and a general approach of the Keystream technique. We show that this methodology can be used to respond to the threat of Zeus and finally provide lessons learned highlighting some general principles of Malware (in general) and in particular proposing Zeus Cronus, an IDS that specifically seeks the Zeus malware, testing it experimentally in a network production and providing an effective skills to combat the Malware are discussed. The thesis is a research interrelated progressive evolution between malware infection systems and authentication methods, reflected in the research work cumulatively, showing an evolution of research output and looking for a progressive improvement of methods authentication and recommendations for prevention and preventing infections, a review of the main app stores for mobile financial services and a proposal to these stores. The most common methods eIDAMS (authentication methods and electronic identification) implemented in Europe and its robustness are analyzed. An analysis of adequacy is presented in terms of efficiency, usability, costs, types of operations and segments including possibilities of use as authentication method with biometrics as innovation.Este trabajo de tesis se ha realizado en el entorno industrial externo a la Universidad como un PhD industrial Los resultados de este PhD han sido testeados, validados, e implementados en el entorno de producción de Caixabank y han sido utilizados como modelos por otras que han seguido las mismas ideas. Las amenazas más candentes contra los bancos en todo el entorno Internet, se basan en herramientas software desarrolladas por los grupos delincuentes, aplicaciones que se ejecutan tanto en entornos web ya sea en el propio ordenador de la víctima (Malware) o en sus dispositivos móviles mediante la descarga de falsas aplicaciones (APP falsa con Malware). Como método se ha utilizado una aproximación de investigación exploratoria cualitativa sobre el problema, la respuesta a este problema y el uso de métodos preventivos a este problema a través de la autenticación. Este método se ha basado en muestras, hechos, encuestas, pruebas de laboratorio, experimentos, pruebas de concepto; en definitiva datos reales de los que se ha podido deducir la tesis propuesta, utilizando tanto investigación de laboratorio como métodos de teoría fundamentada en datos de experimentos pilotos realizados en entornos reales. He estado investigando los diversos aspectos relacionados con e-crime siguiendo una línea de investigación focalizada en temas intrínsecamente relacionadas: - Los métodos, medios y sistemas de ataque: Malware, familias de Malware de troyanos bancarios, casos de usos de Malware, Zeus como caso de uso. - Las plataformas fijas, los móviles y sus aplicaciones como medio para realizar los ataques de Malware. - Métodos forenses para analizar el Malware y su infraestructura de ataque. - Mejora continuada de los métodos de autenticación de los clientes y usuarios como primera barrera de defensa anti- malware. - Uso de la biometría como factor de autenticación innovador. La línea investiga el Malware y sus sistemas de ataque intrínsecamente relacionada con los métodos de autenticación y los sistemas para infectar al cliente (ejecutables, APP's, etc.) porque el objetivo principal del malware es robar precisamente los datos que se introducen en el "logon" del sistema de autenticación para operar de forma fraudulenta y sustraer así el dinero de los clientes de banca electrónica. Los experimentos realizados en el Malware permitieron establecer un método novedoso de descifrado que estableció pautas para combatir sus efectos fraudulentos describiendo su esquema de infección y funcionamiento Propongo una metodología general para romper el cifrado de comunicaciones del malware (keystream) extrayendo el sistema utilizado para cifrar dichas comunicaciones y una generalización de la técnica de Keystream. Se demuestra que esta metodología puede usarse para responder a la amenaza de Zeus y finalmente proveemos lecciones aprendidas resaltando algunos principios generales del Malware (en general) y Zeus en particular proponiendo Cronus, un IDS que persigue específicamente el Malware Zeus, probándolo experimentalmente en una red de producción y se discuten sus habilidades y efectividad. En la tesis hay una evolución investigativa progresiva interrelacionada entre el Malware, sistemas de infección y los métodos de autenticación, que se refleja en los trabajos de investigación de manera acumulativa, mostrando una evolución del output de investigación y buscando una mejora progresiva de los métodos de autenticación y de la prevención y recomendaciones para evitar las infecciones, una revisión de las principales tiendas de Apps para servicios financieros para móviles y una propuesta para estas tiendas. Se analizan los métodos más comunes eIDAMS (Métodos de Autenticación e Identificación electrónica) implementados en Europa y su robustez y presentamos un análisis de adecuación en función de eficiencia, usabilidad, costes, tipos de operación y segmentos incluyendo un análisis de posibilidades con métodos biométricos como innovación.Postprint (published version

Eight years of rider measurement in the Android malware ecosystem: evolution and lessons learned

Despite the growing threat posed by Android malware, the research community is still lacking a comprehensive view of common behaviors and trends exposed by malware families active on the platform. Without such view, the researchers incur the risk of developing systems that only detect outdated threats, missing the most recent ones. In this paper, we conduct the largest measurement of Android malware behavior to date, analyzing over 1.2 million malware samples that belong to 1.2K families over a period of eight years (from 2010 to 2017). We aim at understanding how the behavior of Android malware has evolved over time, focusing on repackaging malware. In this type of threats different innocuous apps are piggybacked with a malicious payload (rider), allowing inexpensive malware manufacturing. One of the main challenges posed when studying repackaged malware is slicing the app to split benign components apart from the malicious ones. To address this problem, we use differential analysis to isolate software components that are irrelevant to the campaign and study the behavior of malicious riders alone. Our analysis framework relies on collective repositories and recent advances on the systematization of intelligence extracted from multiple anti-virus vendors. We find that since its infancy in 2010, the Android malware ecosystem has changed significantly, both in the type of malicious activity performed by the malicious samples and in the level of obfuscation used by malware to avoid detection. We then show that our framework can aid analysts who attempt to study unknown malware families. Finally, we discuss what our findings mean for Android malware detection research, highlighting areas that need further attention by the research community.Accepted manuscrip

Predictors of Ransomware From Binary Analysis

Ransomware, a type of malware that extorts payment from a victim by encrypting her data, is a growing threat that is becoming more sophisticated with each generation. Attackers have shifted from targeting individuals to entire organizations, raising extortions from hundreds of dollars to hundreds of thousands of dollars. In this work, we analyze a variety of ransomware and benign software binaries in order to identify indicators that may be used to detect ransomware. We find that several combinations of strings, cryptographic constants, and a large number loops are key indicators useful for detecting ransomware

Malware Analysis and Detection with Explainable Machine Learning

Malware detection is one of the areas where machine learning is successfully employed due to its high discriminating power and the capability of identifying novel variants of malware samples. Typically, the problem formulation is strictly correlated to the use of a wide variety of features covering several characteristics of the entities to classify. Apparently, this practice allows achieving considerable detection performance. However, it hardly permits us to gain insights into the knowledge extracted by the learning algorithm, causing two main issues. First, detectors might learn spurious patterns; thus, undermining their effectiveness in real environments. Second, they might be particularly vulnerable to adversarial attacks; thus, weakening their security. These concerns give rise to the necessity to develop systems that are tailored to the specific peculiarities of the attacks to detect. Within malware detection, Android ransomware represents a challenging yet illustrative domain for assessing the relevance of this issue. Ransomware represents a serious threat that acts by locking the compromised device or encrypting its data, then forcing the device owner to pay a ransom in order to restore the device functionality. Attackers typically develop such dangerous apps so that normally-legitimate components and functionalities perform malicious behaviour; thus, making them harder to be distinguished from genuine applications. In this sense, adopting a well-defined variety of features and relying on some kind of explanations about the logic behind such detectors could improve their design process since it could reveal truly characterising features; hence, guiding the human expert towards the understanding of the most relevant attack patterns. Given this context, the goal of the thesis is to explore strategies that may improve the design process of malware detectors. In particular, the thesis proposes to evaluate and integrate approaches based on rising research on Explainable Machine Learning. To this end, the work follows two pathways. The first and main one focuses on identifying the main traits that result to be characterising and effective for Android ransomware detection. Then, explainability techniques are used to propose methods to assess the validity of the considered features. The second pathway broadens the view by exploring the relationship between explainable machine learning and adversarial attacks. In this regard, the contribution consists of pointing out metrics extracted from explainability techniques that can reveal models' robustness to adversarial attacks, together with an assessment of the practical feasibility for attackers to alter the features that affect models' output the most. Ultimately, this work highlights the necessity to adopt a design process that is aware of the weaknesses and attacks against machine learning-based detectors, and proposes explainability techniques as one of the tools to counteract them

Protecting Software through Obfuscation:Can It Keep Pace with Progress in Code Analysis?

Software obfuscation has always been a controversially discussed research area. While theoretical results indicate that provably secure obfuscation in general is impossible, its widespread application in malware and commercial software shows that it is nevertheless popular in practice. Still, it remains largely unexplored to what extent today’s software obfuscations keep up with state-of-the-art code analysis and where we stand in the arms race between software developers and code analysts. The main goal of this survey is to analyze the effectiveness of different classes of software obfuscation against the continuously improving deobfuscation techniques and off-the-shelf code analysis tools. The answer very much depends on the goals of the analyst and the available resources. On the one hand, many forms of lightweight static analysis have difficulties with even basic obfuscation schemes, which explains the unbroken popularity of obfuscation among malware writers. On the other hand, more expensive analysis techniques, in particular when used interactively by a human analyst, can easily defeat many obfuscations. As a result, software obfuscation for the purpose of intellectual property protection remains highly challenging.</jats:p

Ransomware Detection and Classification Strategies

Ransomware uses encryption methods to make data inaccessible to legitimate users. To date a wide range of ransomware families have been developed and deployed, causing immense damage to governments, corporations, and private users. As these cyberthreats multiply, researchers have proposed a range of ransomware detection and classification schemes. Most of these methods use advanced machine learning techniques to process and analyze real-world ransomware binaries and action sequences. Hence this paper presents a survey of this critical space and classifies existing solutions into several categories, i.e., including network-based, host-based, forensic characterization, and authorship attribution. Key facilities and tools for ransomware analysis are also presented along with open challenges.Comment: 9 pages, 2 figure