Windows credential theft: Methods and mitigations

Compromising Windows account credentials, especially in a domain environment, is a critical phase in an attack against an organization. This paper will first survey the most common tools and techniques used to uncover usernames and their plaintext credentials in standard red team procedures. These methods are compared against the new proposed method that uses low level hooking in the local security authority subsystem service to stealthily compromise plaintext credentials upon login. The latter has many advantages over pre-existing tools designed to capture credentials on Windows based computers. Finally, mitigation procedures will be examined that are designed to thwart credential theft or limit further domain compromise

A Survey on Malware Analysis Techniques: Static, Dynamic, Hybrid and Memory Analysis

Now a day the threat of malware is increasing rapidly. A software that sneaks to your computer system without your knowledge with a harmful intent to disrupt your computer operations. Due to the vast number of malware, it is impossible to handle malware by human engineers. Therefore, security researchers are taking great efforts to develop accurate and effective techniques to detect malware. This paper presents a semantic and detailed survey of methods used for malware detection like signature-based and heuristic-based. The Signature-based technique is largely used today by anti-virus software to detect malware, is fast and capable to detect known malware. However, it is not effective in detecting zero-day malware and it is easily defeated by malware that use obfuscation techniques. Likewise, a considerable false positive rate and high amount of scanning time are the main limitations of heuristic-based techniques. Alternatively, memory analysis is a promising technique that gives a comprehensive view of malware and it is expected to become more popular in malware analysis. The main contributions of this paper are: (1) providing an overview of malware types and malware detection approaches, (2) discussing the current malware analysis techniques, their findings and limitations, (3) studying the malware obfuscation, attacking and anti-analysis techniques, and (4) exploring the structure of memory-based analysis in malware detection. The detection approaches have been compared with each other according to their techniques, selected features, accuracy rates, and their advantages and disadvantages. This paper aims to help the researchers to have a general view of malware detection field and to discuss the importance of memory-based analysis in malware detection

解析回避機能を持つマルウェアの解析手法: テイント伝播によるアプローチ

早大学位記番号:新8140早稲田大

Kernel-Level Interception and Applications on Mobile Devices

The techniques of kernel-level system call interception are well known today for many different operating systems. This work starts with transferring these technique to the Windows CE type of operating systems. Afterwards, two current problems are solved. The first solution uses the technique for dynamic malware analysis with a sandbox approach, extending previous solutions in terms of effectiveness. The second solution enhances the expressiveness of security policies by implementing the concept of a reference monitor on the operating system level. Windows CE based devices are now enabled to enforce sophisticated security policies without the need to change the underlying operating system

IronNetInjector: Weaponizing .NET Dynamic Language Runtime Engines

As adversaries evolve their Tactics, Techniques, and Procedures (TTPs) to stay ahead of defenders, Microsoft’s .NET Framework emerges as a common component found in the tradecraft of many contemporary Advanced Persistent Threats (APTs), whether through PowerShell or C#. Because of .NET’s ease of use and availability on every recent Windows system, it is at the forefront of modern TTPs and is a primary means of exploitation. This article considers the .NET Dynamic Language Runtime as an attack vector, and how APTs have utilized it for offensive purposes. The technique under scrutiny is Bring Your Own Interpreter (BYOI), which is the ability of developers to embed dynamic languages into .NET using an engine. The focus of this analysis is an adversarial use case in which APT Turla utilized BYOI as an evasion technique, using an IronPython .NET Injector named IronNetInjector. This research analyzes IronNetInjector and how it was used to reflectively load .NET assemblies. It also evaluates the role of Antimalware Scan Interface (AMSI) in defending Windows. Due to AMSI being at the core of Windows malware mitigation, this article further evaluates the memory patching bypass technique by demonstrating a novel AMSI bypass method in IronPython using Platform Invoke (P/Invoke)

Análise de malware com suporte de hardware

Orientadores: Paulo Lício de Geus, André Ricardo Abed GrégioDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O mundo atual é impulsionado pelo uso de sistemas computacionais, estando estes pre- sentes em todos aspectos da vida cotidiana. Portanto, o correto funcionamento destes é essencial para se assegurar a manutenção das possibilidades trazidas pelos desenvolvi- mentos tecnológicos. Contudo, garantir o correto funcionamento destes não é uma tarefa fácil, dado que indivíduos mal-intencionados tentam constantemente subvertê-los visando benefíciar a si próprios ou a terceiros. Os tipos mais comuns de subversão são os ataques por códigos maliciosos (malware), capazes de dar a um atacante controle total sobre uma máquina. O combate à ameaça trazida por malware baseia-se na análise dos artefatos coletados de forma a permitir resposta aos incidentes ocorridos e o desenvolvimento de contramedidas futuras. No entanto, atacantes têm se especializado em burlar sistemas de análise e assim manter suas operações ativas. Para este propósito, faz-se uso de uma série de técnicas denominadas de "anti-análise", capazes de impedir a inspeção direta dos códigos maliciosos. Dentre essas técnicas, destaca-se a evasão do processo de análise, na qual são empregadas exemplares capazes de detectar a presença de um sistema de análise para então esconder seu comportamento malicioso. Exemplares evasivos têm sido cada vez mais utilizados em ataques e seu impacto sobre a segurança de sistemas é considerá- vel, dado que análises antes feitas de forma automática passaram a exigir a supervisão de analistas humanos em busca de sinais de evasão, aumentando assim o custo de se manter um sistema protegido. As formas mais comuns de detecção de um ambiente de análise se dão através da detecção de: (i) código injetado, usado pelo analista para inspecionar a aplicação; (ii) máquinas virtuais, usadas em ambientes de análise por questões de escala; (iii) efeitos colaterais de execução, geralmente causados por emuladores, também usados por analistas. Para lidar com malware evasivo, analistas tem se valido de técnicas ditas transparentes, isto é, que não requerem injeção de código nem causam efeitos colaterais de execução. Um modo de se obter transparência em um processo de análise é contar com suporte do hardware. Desta forma, este trabalho versa sobre a aplicação do suporte de hardware para fins de análise de ameaças evasivas. No decorrer deste texto, apresenta-se uma avaliação das tecnologias existentes de suporte de hardware, dentre as quais máqui- nas virtuais de hardware, suporte de BIOS e monitores de performance. A avaliação crítica de tais tecnologias oferece uma base de comparação entre diferentes casos de uso. Além disso, são enumeradas lacunas de desenvolvimento existentes atualmente. Mais que isso, uma destas lacunas é preenchida neste trabalho pela proposição da expansão do uso dos monitores de performance para fins de monitoração de malware. Mais especificamente, é proposto o uso do monitor BTS para fins de construção de um tracer e um debugger. O framework proposto e desenvolvido neste trabalho é capaz, ainda, de lidar com ataques do tipo ROP, um dos mais utilizados atualmente para exploração de vulnerabilidades. A avaliação da solução demonstra que não há a introdução de efeitos colaterais, o que per- mite análises de forma transparente. Beneficiando-se desta característica, demonstramos a análise de aplicações protegidas e a identificação de técnicas de evasãoAbstract: Today¿s world is driven by the usage of computer systems, which are present in all aspects of everyday life. Therefore, the correct working of these systems is essential to ensure the maintenance of the possibilities brought about by technological developments. However, ensuring the correct working of such systems is not an easy task, as many people attempt to subvert systems working for their own benefit. The most common kind of subversion against 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 procedures of the collected malicious artifacts, allowing the incident response and the development of future countermeasures. However, attackers have specialized in circumventing analysis systems and thus keeping their operations active. For this purpose, they employ a series of 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 of samples able to detect the presence of an analysis solution and then hide their malicious behavior. Evasive examples have become popular, and their impact on systems security is considerable, since automatic analysis now requires human supervision in order to find evasion signs, which significantly raises the cost of maintaining a protected system. The most 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) Virtual machine 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. A way to achieve transparency in an analysis process is to rely on hardware support. In this way, this work covers the application of the hardware support for the evasive threats analysis purpose. In the course of this text, I present an assessment of existing hardware support technologies, including hardware virtual machines, BIOS support, performance monitors and PCI cards. My critical evaluation of such technologies provides basis for comparing different usage cases. In addition, I pinpoint development gaps that currently exists. More than that, I fill one of these gaps by proposing to expand the usage of performance monitors for malware monitoring purposes. More specifically, I propose the usage of the BTS monitor for the purpose of developing a tracer and a debugger. The proposed framework is also able of dealing with ROP attacks, one of the most common used technique for remote vulnerability exploitation. The framework evaluation shows no side-effect is introduced, thus allowing transparent analysis. Making use of this capability, I demonstrate how protected applications can be inspected and how evasion techniques can be identifiedMestradoCiência da ComputaçãoMestre em Ciência da ComputaçãoCAPE

A WYSIWYG ADD-ON DEVELOPMENT ENVIRONMENT FOR THIRD PARTY SOFTWARE APPLICATIONS

Master'sMASTER OF SCIENC

Techniques for the reverse engineering of banking malware

Malware attacks are a significant and frequently reported problem, adversely affecting the productivity of organisations and governments worldwide. The well-documented consequences of malware attacks include financial loss, data loss, reputation damage, infrastructure damage, theft of intellectual property, compromise of commercial negotiations, and national security risks. Mitiga-tion activities involve a significant amount of manual analysis. Therefore, there is a need for automated techniques for malware analysis to identify malicious behaviours. Research into automated techniques for malware analysis covers a wide range of activities. This thesis consists of a series of studies: an anal-ysis of banking malware families and their common behaviours, an emulated command and control environment for dynamic malware analysis, a technique to identify similar malware functions, and a technique for the detection of ransomware. An analysis of the nature of banking malware, its major malware families, behaviours, variants, and inter-relationships are provided in this thesis. In doing this, this research takes a broad view of malware analysis, starting with the implementation of the malicious behaviours through to detailed analysis using machine learning. The broad approach taken in this thesis differs from some other studies that approach malware research in a more abstract sense. A disadvantage of approaching malware research without domain knowledge, is that important methodology questions may not be considered. Large datasets of historical malware samples are available for countermea-sures research. However, due to the age of these samples, the original malware infrastructure is no longer available, often restricting malware operations to initialisation functions only. To address this absence, an emulated command and control environment is provided. This emulated environment provides full control of the malware, enabling the capabilities of the original in-the-wild operation, while enabling feature extraction for research purposes. A major focus of this thesis has been the development of a machine learn-ing function similarity method with a novel feature encoding that increases feature strength. This research develops techniques to demonstrate that the machine learning model trained on similarity features from one program can find similar functions in another, unrelated program. This finding can lead to the development of generic similar function classifiers that can be packaged and distributed in reverse engineering tools such as IDA Pro and Ghidra. Further, this research examines the use of API call features for the identi-fication of ransomware and shows that a failure to consider malware analysis domain knowledge can lead to weaknesses in experimental design. In this case, we show that existing research has difficulty in discriminating between ransomware and benign cryptographic software. This thesis by publication, has developed techniques to advance the disci-pline of malware reverse engineering, in order to minimize harm due to cyber-attacks on critical infrastructure, government institutions, and industry.Doctor of Philosoph

Analysis avoidance techniques of malicious software

Anti Virus (AV) software generally employs signature matching and heuristics to detect the presence of malicious software (malware). The generation of signatures and determination of heuristics is dependent upon an AV analyst having successfully determined the nature of the malware, not only for recognition purposes, but also for the determination of infected files and startup mechanisms that need to be removed as part of the disinfection process. If a specimen of malware has not been previously extensively analyzed, it is unlikely to be detected by AV software. In addition, malware is becoming increasingly profit driven and more likely to incorporate stealth and deception techniques to avoid detection and analysis to remain on infected systems for a myriad of nefarious purposes. Malware extends beyond the commonly thought of virus or worm, to customized malware that has been developed for specific and targeted miscreant purposes. Such customized malware is highly unlikely to be detected by AV software because it will not have been previously analyzed and a signature will not exist. Analysis in such a case will have to be conducted by a digital forensics analyst to determine the functionality of the malware. Malware can employ a plethora of techniques to hinder the analysis process conducted by AV and digital forensics analysts. The purpose of this research has been to answer three research questions directly related to the employment of these techniques as: 1. What techniques can malware use to avoid being analyzed? 2. How can the use of these techniques be detected? 3. How can the use of these techniques be mitigated

Gestão de ameaças de segurança em sistemas android

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e ComputadoresWith the exponential use of mobile phones to handle sensitive information, the intrusion systems development has also increased. Malicious software is constantly being developed and the intrusion techniques are increasingly more sophisticated. Security protection systems trying to counteract these intrusions are constantly being improved and updated. Being Android one of the most popular operating systems, it became an intrusion’s methods development target. Developed security solutions constantly monitor their host system and by accessing a set of defined parameters they try to find potentially harmful changes. An important topic when addressing malicious applications detection is the malware identification and characterization. Usually, to separate the normal system behavior from the malicious behavior, security systems employ machine learning or data mining techniques. However, with the constant evolution of malicious applications, such techniques are still far from being capable of completely responding to the market needs. This dissertation aim was to verify if malicious behavior patterns definition is a viable way of addressing this challenge. As part of the proposed research two data mining classification models were built and tested with the collected data, and their performances were compared. the RapidMiner software was used for the proposed model development and testing, and data was collected from the FlowDroid application. To facilitate the understanding of the security potential of the Android framework, research was done on the its architecture, overall structure, and security methods, including its protection mechanisms and breaches. It was also done a study on models threats/attacks’ description, as well as, on the current existing applications for anti-mobile threats, analyzing their strengths and weaknesses.Com o uso exponencial de telefones para lidar com informações sensíveis, o desenvolvimento de sistemas de intrusão também aumentou. Softwares maliciosos estão constantemente a ser desenvolvidos e as técnicas de intrusão são cada vez mais sofisticadas. Para neutralizar essas intrusões, os sistemas de proteção de segurança precisam constantemente de ser melhorados e atualizados. Sendo o Android um dos sistemas operativos (SO) mais populares, tornou-se também num alvo de desenvolvimento de métodos de intrusão. As soluções de segurança desenvolvidas monitoram constantemente o sistema em que se encontram e acedendo a um o conjunto definido de parâmetros procuram alterações potencialmente prejudiciais. Um tópico importante ao abordar aplicações mal-intencionadas é a identificação e caracterização do malware. Normalmente, para separar o comportamento normal do sistema do comportamento mal-intencionado, os sistemas de segurança empregam técnicas de machine learning ou de data mining. No entanto, com a constante evolução das aplicações maliciosas, tais técnicas ainda estão longe de serem capazes de responder completamente às necessidades do mercado. Esta dissertação teve como objetivo verificar se os padrões de comportamento malicioso são uma forma viável de enfrentar esse desafio. Para responder à pesquisa proposta foram construídos e testados dois modelos de classificação de dados, usando técnicas de data mining, e com os dados recolhidos compararam-se os seus desempenhos. Para o desenvolvimento e teste do modelo proposto foi utilizado o software RapidMiner, e os dados foram recolhidos através do uso da aplicação FlowDroid. Para facilitar a compreensão sobre as potencialidades de segurança da framework do Android, realizou-se uma pesquisa sobre a sua arquitetura, estrutura geral e métodos de segurança, incluindo seus mecanismos de defesa e algumas das suas limitações. Além disso, realizou-se um estudo sobre algumas das atuais aplicações existentes para a defesa contra aplicações maliciosas, analisando os seus pontos fortes e fracos