Longitudinal performance analysis of machine learning based Android malware detectors

This paper presents a longitudinal study of the performance of machine learning classifiers for Android malware detection. The study is undertaken using features extracted from Android applications first seen between 2012 and 2016. The aim is to investigate the extent of performance decay over time for various machine learning classifiers trained with static features extracted from date-labelled benign and malware application sets. Using date-labelled apps allows for true mimicking of zero-day testing, thus providing a more realistic view of performance than the conventional methods of evaluation that do not take date of appearance into account. In this study, all the investigated machine learning classifiers showed progressive diminishing performance when tested on sets of samples from a later time period. Overall, it was found that false positive rate (misclassifying benign samples as malicious) increased more substantially compared to the fall in True Positive rate (correct classification of malicious apps) when older models were tested on newer app samples

A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection

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

Automated Test Input Generation for Android: Are We There Yet?

Mobile applications, often simply called "apps", are increasingly widespread, and we use them daily to perform a number of activities. Like all software, apps must be adequately tested to gain confidence that they behave correctly. Therefore, in recent years, researchers and practitioners alike have begun to investigate ways to automate apps testing. In particular, because of Android's open source nature and its large share of the market, a great deal of research has been performed on input generation techniques for apps that run on the Android operating systems. At this point in time, there are in fact a number of such techniques in the literature, which differ in the way they generate inputs, the strategy they use to explore the behavior of the app under test, and the specific heuristics they use. To better understand the strengths and weaknesses of these existing approaches, and get general insight on ways they could be made more effective, in this paper we perform a thorough comparison of the main existing test input generation tools for Android. In our comparison, we evaluate the effectiveness of these tools, and their corresponding techniques, according to four metrics: code coverage, ability to detect faults, ability to work on multiple platforms, and ease of use. Our results provide a clear picture of the state of the art in input generation for Android apps and identify future research directions that, if suitably investigated, could lead to more effective and efficient testing tools for Android

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

DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection

Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect. Due to the exponential growth in malware, manual methods of malware are increasingly ineffective. Although prior writers have proposed numerous high-quality approaches, static and dynamic assessments inherently necessitate intricate procedures. The obfuscation methods used by modern malware are incredibly complex and clever. As a result, it cannot be detected using only static malware analysis. As a result, this work presents a hybrid analysis approach, partially tailored for multiple-feature data, for identifying Android malware and classifying malware families to improve Android malware detection and classification. This paper offers a hybrid method that combines static and dynamic malware analysis to give a full view of the threat. Three distinct phases make up the framework proposed in this research. Normalization and feature extraction procedures are used in the first phase of pre-processing. Both static and dynamic features undergo feature selection in the second phase. Two feature selection strategies are proposed to choose the best subset of features to use for both static and dynamic features. The third phase involves applying a newly proposed detection model to classify android apps; this model uses a neural network optimized with an improved version of HHO. Application of binary and multi-class classification is used, with binary classification for benign and malware apps and multi-class classification for detecting malware categories and families. By utilizing the features gleaned from static and dynamic malware analysis, several machine-learning methods are used for malware classification. According to the results of the experiments, the hybrid approach improves the accuracy of detection and classification of Android malware compared to the scenario when considering static and dynamic information separately

Explainable Malware Detection System Using Transformers-Based Transfer Learning and Multi-Model Visual Representation

Android has become the leading mobile ecosystem because of its accessibility and adaptability. It has also become the primary target of widespread malicious apps. This situation needs the immediate implementation of an effective malware detection system. In this study, an explainable malware detection system was proposed using transfer learning and malware visual features. For effective malware detection, our technique leverages both textual and visual features. First, a pre-trained model called the Bidirectional Encoder Representations from Transformers (BERT) model was designed to extract the trained textual features. Second, the malware-to-image conversion algorithm was proposed to transform the network byte streams into a visual representation. In addition, the FAST (Features from Accelerated Segment Test) extractor and BRIEF (Binary Robust Independent Elementary Features) descriptor were used to efficiently extract and mark important features. Third, the trained and texture features were combined and balanced using the Synthetic Minority Over-Sampling (SMOTE) method; then, the CNN network was used to mine the deep features. The balanced features were then input into the ensemble model for efficient malware classification and detection. The proposed method was analyzed extensively using two public datasets, CICMalDroid 2020 and CIC-InvesAndMal2019. To explain and validate the proposed methodology, an interpretable artificial intelligence (AI) experiment was conducted

Resilient and Scalable Android Malware Fingerprinting and Detection

Malicious software (Malware) proliferation reaches hundreds of thousands daily. The manual analysis of such a large volume of malware is daunting and time-consuming. The diversity of targeted systems in terms of architecture and platforms compounds the challenges of Android malware detection and malware in general. This highlights the need to design and implement new scalable and robust methods, techniques, and tools to detect Android malware. In this thesis, we develop a malware fingerprinting framework to cover accurate Android malware detection and family attribution. In this context, we emphasize the following: (i) the scalability over a large malware corpus; (ii) the resiliency to common obfuscation techniques; (iii) the portability over different platforms and architectures. In the context of bulk and offline detection on the laboratory/vendor level: First, we propose an approximate fingerprinting technique for Android packaging that captures the underlying static structure of the Android apps. We also propose a malware clustering framework on top of this fingerprinting technique to perform unsupervised malware detection and grouping by building and partitioning a similarity network of malicious apps. Second, we propose an approximate fingerprinting technique for Android malware's behavior reports generated using dynamic analyses leveraging natural language processing techniques. Based on this fingerprinting technique, we propose a portable malware detection and family threat attribution framework employing supervised machine learning techniques. Third, we design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. We leverage graph analysis techniques to generate relevant, actionable, and granular intelligence that can be used to identify the threat effects induced by malicious Internet activity associated to Android malicious apps. In the context of the single app and online detection on the mobile device level, we further propose the following: Fourth, we design a portable and effective Android malware detection system that is suitable for deployment on mobile and resource constrained devices, using machine learning classification on raw method call sequences. Fifth, we elaborate a framework for Android malware detection that is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. We also evaluate the portability of the proposed techniques and methods beyond Android platform malware, as follows: Sixth, we leverage the previously elaborated techniques to build a framework for cross-platform ransomware fingerprinting relying on raw hybrid features in conjunction with advanced deep learning techniques

Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks

Malware still constitutes a major threat in the cybersecurity landscape, also due to the widespread use of infection vectors such as documents. These infection vectors hide embedded malicious code to the victim users, facilitating the use of social engineering techniques to infect their machines. Research showed that machine-learning algorithms provide effective detection mechanisms against such threats, but the existence of an arms race in adversarial settings has recently challenged such systems. In this work, we focus on malware embedded in PDF files as a representative case of such an arms race. We start by providing a comprehensive taxonomy of the different approaches used to generate PDF malware, and of the corresponding learning-based detection systems. We then categorize threats specifically targeted against learning-based PDF malware detectors, using a well-established framework in the field of adversarial machine learning. This framework allows us to categorize known vulnerabilities of learning-based PDF malware detectors and to identify novel attacks that may threaten such systems, along with the potential defense mechanisms that can mitigate the impact of such threats. We conclude the paper by discussing how such findings highlight promising research directions towards tackling the more general challenge of designing robust malware detectors in adversarial settings