Automating Mobile Device File Format Analysis

Forensic tools assist examiners in extracting evidence from application files from mobile devices. If the file format for the file of interest is known, this process is straightforward, otherwise it requires the examiner to manually reverse engineer the data structures resident in the file. This research presents the Automated Data Structure Slayer (ADSS), which automates the process to reverse engineer unknown file for- mats of Android applications. After statically parsing and preparing an application, ADSS dynamically runs it, injecting hooks at selected methods to uncover the data structures used to store and process data before writing to media. The resultant association between application semantics and bytes in a file reveal the structure and file format. ADSS has been successfully evaluated against Uber and Discord, both popular Android applications, and reveals the format used by the respective proprietary application files stored on the filesystem

Convicted by memory: Automatically recovering spatial-temporal evidence from memory images

Memory forensics can reveal “up to the minute” evidence of a device’s usage, often without requiring a suspect’s password to unlock the device, and it is oblivious to any persistent storage encryption schemes, e.g., whole disk encryption. Prior to my work, researchers and investigators alike considered data-structure recovery the ultimate goal of memory image forensics. This, however, was far from sufficient, as investigators were still largely unable to understand the content of the recovered evidence, and hence efficiently locating and accurately analyzing such evidence locked in memory images remained an open research challenge. In this dissertation, I propose breaking from traditional data-recovery-oriented forensics, and instead I present a memory forensics framework which leverages program analysis to automatically recover spatial-temporal evidence from memory images by understanding the programs that generated it. This framework consists of four techniques, each of which builds upon the discoveries of the previous, that represent this new paradigm of program-analysis-driven memory forensics. First, I present DSCRETE, a technique which reuses a program’s own interpretation and rendering logic to recover and present in-memory data structure contents. Following that, VCR developed vendor-generic data structure identification for the recovery of in-memory photographic evidence produced by an Android device’s cameras. GUITAR then realized an app-independent technique which automatically reassembles and redraws an app’s GUI from the multitude of GUI data elements found in a smartphone’s memory image. Finally, different from any traditional memory forensics technique, RetroScope introduced the vision of spatial-temporal memory forensics by retargeting an Android app’s execution to recover sequences of previous GUI screens, in their original temporal order, from a memory image. This framework, and the new program analysis techniques which enable it, have introduced encryption-oblivious forensics capabilities far exceeding traditional data-structure recovery

MEMORY FORENSIC DEVELOPMENT AND CHALLENGES IN IDENTIFYING DIGITAL CRIME : A REVIEW

Digital forensic technology is currently advancing along with the demands to uncover various crimes using technology. Memory Forensic is one of the investigative fields in digital forensics. We use the Systematic Literature Review method to identify the developments and challenges of Forensic Memory in identifying digital crimes, analyzed from various reference papers according to the Include and Exclude Criteria and based on the specified Research Question. Authors chose from 30 reference journals from 3 online journal databases namely IEEE Explore, Sciencedirect, and Springer with themes related to forensic memory based on certain criteria for further review to determine the development of digital crime. The results of the SLR that we convey are the result of a study related to the use of Memory Forensic in identifying various digital attacks and challenges faced in the future

Memory FORESHADOW: Memory FOREnSics of HArDware CryptOcurrency Wallets – A Tool and Visualization Framework

We present Memory FORESHADOW: Memory FOREnSics of HArDware cryptOcurrency Wallets. To the best of our knowledge, this is the primary account of cryptocurrency hardware wallet client memory forensics. Our exploratory analysis revealed forensically relevant data in memory including transaction history, extended public keys, passphrases, and unique device identifiers. Data extracted with FORESHADOW can be used to associate a hardware wallet with a computer and allow an observer to deanonymize all past and future transactions due to hierarchical deterministic wallet address derivation. Additionally, our novel visualization framework enabled us to measure both the persistence and integrity of artifacts produced by the Ledger and Trezor hardware wallet clients. The framework can be generalized for use in future memory forensics work

Malware detection at runtime for resource-constrained mobile devices: data-driven approach

The number of smart and connected mobile devices is increasing, bringing enormous possibilities to users in various domains and transforming everything that we get in touch with into smart. Thus, we have smart watches, smart phones, smart homes, and finally even smart cities. Increased smartness of mobile devices means that they contain more valuable information about their users, more decision making capabilities, and more control over sometimes even life-critical systems. Although, on one side, all of these are necessary in order to enable mobile devices maintain their main purpose to help and support people, on the other, it opens new vulnerabilities. Namely, with increased number and volume of smart devices, also the interest of attackers to abuse them is rising, making their security one of the main challenges. The main mean that the attackers use in order to abuse mobile devices is malicious software, shortly called malware. One way to protect against malware is by using static analysis, that investigates the nature of software by analyzing its static features. However, this technique detects well only known malware and it is prone to obfuscation, which means that it is relatively easy to create a new malicious sample that would be able to pass the radar. Thus, alone, is not powerful enough to protect the users against increasing malicious attacks. The other way to cope with malware is through dynamic analysis, where the nature of the software is decided based on its behavior during its execution on a device. This is a promising solution, because while the code of the software can be easily changed to appear as new, the same cannot be done with ease with its behavior when being executed. However, in order to achieve high accuracy dynamic analysis usually requires computational resources that are beyond suitable for battery-operated mobile devices. This is further complicated if, in addition to detecting the presence of malware, we also want to understand which type of malware it is, in order to trigger suitable countermeasures. Finally, the decisions on potential infections have to happen early enough, to guarantee minimal exposure to the attacks. Fulfilling these requirements in a mobile, battery-operated environments is a challenging task, for which, to the best of our knowledge, a suitable solution is not yet proposed. In this thesis, we pave the way towards such a solution by proposing a dynamic malware detection system that is able to early detect malware that appears at runtime and that provides useful information to discriminate between diverse types of malware while taking into account limited resources of mobile devices. On a mobile device we monitor a set of the representative features for presence of malware and based on them we trigger an alarm if software infection is observed. When this happens, we analyze a set of previously stored information relevant for malware classification, in order to understand what type of malware is being executed. In order to make the detection efficient and suitable for resource-constrained environments of mobile devices, we minimize the set of observed system parameters to only the most informative ones for both detection and classification. Additionally, since sampling period of monitoring infrastructure is directly connected to the power consumption, we take it into account as an important parameter of the development of the detection system. In order to make detection effective, we use dynamic features related to memory, CPU, system calls and network as they reflect well the behavior of a system. Our experiments show that the monitoring with a sampling period of eight seconds gives a good trade-off between detection accuracy, detection time and consumed power. Using it and by monitoring a set of only seven dynamic features (six related to the behavior of memory and one of CPU), we are able to provide a detection solution that satisfies the initial requirements and to detect malware at runtime with F- measure of 0.85, within 85.52 seconds of its execution, and with consumed average power of 20mW. Apart from observed features containing enough information to discriminate between malicious and benign applications, our results show that they can also be used to discriminate between diverse behavior of malware, reflected in different malware families. Using small number of features we are able to identify the presence of the malicious records from the considered family with precision of up to 99.8%. In addition to the standalone use of the proposed detection solution, we have also used it in a hybrid scenario where the applications were first analyzed by a static method, and it was able to detect correctly all the malware previously undetected by static analysis with false positive rate of 3.81% and average detection time of 44.72s. The method, we have designed, tested and validated, has been applied on a smartphone running on Android Operating System. However, since in the design of this method efficient usage of available computational resources was one of our main criteria, we are confident that the method as such can be applied also on the other battery-operated mobile devices of Internet of Things, in order to provide an effective and efficient system able to counter the ever-increasing and ever-evolving number and a variety of malicious attacks

Cyber-security protection techniques to mitigate memory errors exploitation

Tesis por compendio[EN] Practical experience in software engineering has demonstrated that the goal of building totally fault-free software systems, although desirable, is impossible to achieve. Therefore, it is necessary to incorporate mitigation techniques in the deployed software, in order to reduce the impact of latent faults. This thesis makes contributions to three memory corruption mitigation techniques: the stack smashing protector (SSP), address space layout randomisation (ASLR) and automatic software diversification. The SSP is a very effective protection technique used against stack buffer overflows, but it is prone to brute force attacks, particularly the dangerous byte-for-byte attack. A novel modification, named RenewSSP, has been proposed which eliminates brute force attacks, can be used in a completely transparent way with existing software and has negligible overheads. There are two different kinds of application for which RenewSSP is especially beneficial: networking servers (tested in Apache) and application launchers (tested on Android). ASLR is a generic concept with multiple designs and implementations. In this thesis, the two most relevant ASLR implementations of Linux have been analysed (Vanilla Linux and PaX patch), and several weaknesses have been found. Taking into account technological improvements in execution support (compilers and libraries), a new ASLR design has been proposed, named ASLR-NG, which maximises entropy, effectively addresses the fragmentation issue and removes a number of identified weaknesses. Furthermore, ASLR-NG is transparent to applications, in that it preserves binary code compatibility and does not add overheads. ASLR-NG has been implemented as a patch to the Linux kernel 4.1. Software diversification is a technique that covers a wide range of faults, including memory errors. The main problem is how to create variants, i.e. programs which have identical behaviours on normal inputs but where faults manifest differently. A novel form of automatic variant generation has been proposed, using multiple cross-compiler suites and processor emulators. One of the main goals of this thesis is to create applicable results. Therefore, I have placed particular emphasis on the development of real prototypes in parallel with the theoretical study. The results of this thesis are directly applicable to real systems; in fact, some of the results have already been included in real-world products.[ES] La creación de software supone uno de los retos más complejos para el ser humano ya que requiere un alto grado de abstracción. Aunque se ha avanzado mucho en las metodologías para la prevención de los fallos software, es patente que el software resultante dista mucho de ser confiable, y debemos asumir que el software que se produce no está libre de fallos. Dada la imposibilidad de diseñar o implementar sistemas libres de fallos, es necesario incorporar técnicas de mitigación de errores para mejorar la seguridad. La presente tesis realiza aportaciones en tres de las principales técnicas de mitigación de errores de corrupción de memoria: Stack Smashing Protector (SSP), Address Space Layout Randomisation (ASLR) y Automatic Software Diversification. SSP es una técnica de protección muy efectiva contra ataques de desbordamiento de buffer en pila, pero es sensible a ataques de fuerza bruta, en particular al peligroso ataque denominado byte-for-byte. Se ha propuesto una novedosa modificación del SSP, llamada RenewSSP, la cual elimina los ataques de fuerza bruta. Puede ser usada de manera completamente transparente con los programas existentes sin introducir sobrecarga. El RenewSSP es especialmente beneficioso en dos áreas de aplicación: Servidores de red (probado en Apache) y lanzadores de aplicaciones eficientes (probado en Android). ASLR es un concepto genérico, del cual hay multitud de diseños e implementaciones. Se han analizado las dos implementaciones más relevantes de Linux (Vanilla Linux y PaX patch), encontrándose en ambas tanto debilidades como elementos mejorables. Teniendo en cuenta las mejoras tecnológicas en el soporte a la ejecución (compiladores y librerías), se ha propuesto un nuevo diseño del ASLR, llamado ASLR-NG, el cual: maximiza la entropía, soluciona el problema de la fragmentación y elimina las debilidades encontradas. Al igual que la solución propuesta para el SSP, la nueva propuesta de ASLR es transparente para las aplicaciones y compatible a nivel binario sin introducir sobrecarga. ASLR-NG ha sido implementado como un parche del núcleo de Linux para la versión 4.1. La diversificación software es una técnica que cubre una amplia gama de fallos, incluidos los errores de memoria. La principal dificultad para aplicar esta técnica radica en la generación de las "variantes", que son programas que tienen un comportamiento idéntico entre ellos ante entradas normales, pero tienen un comportamiento diferenciado en presencia de entradas anormales. Se ha propuesto una novedosa forma de generar variantes de forma automática a partir de un mismo código fuente, empleando la emulación de sistemas. Una de las máximas de esta investigación ha sido la aplicabilidad de los resultados, por lo que se ha hecho especial hincapié en el desarrollo de prototipos sobre sistemas reales a la par que se llevaba a cabo el estudio teórico. Como resultado, las propuestas de esta tesis son directamente aplicables a sistemas reales, algunas de ellas ya están siendo explotadas en la práctica.[CA] La creació de programari suposa un dels reptes més complexos per al ser humà ja que requerix un alt grau d'abstracció. Encara que s'ha avançat molt en les metodologies per a la prevenció de les fallades de programari, és palès que el programari resultant dista molt de ser confiable, i hem d'assumir que el programari que es produïx no està lliure de fallades. Donada la impossibilitat de dissenyar o implementar sistemes lliures de fallades, és necessari incorporar tècniques de mitigació d'errors per a millorar la seguretat. La present tesi realitza aportacions en tres de les principals tècniques de mitigació d'errors de corrupció de memòria: Stack Smashing Protector (SSP), Address Space Layout Randomisation (ASLR) i Automatic Software Diversification. SSP és una tècnica de protecció molt efectiva contra atacs de desbordament de buffer en pila, però és sensible a atacs de força bruta, en particular al perillós atac denominat byte-for-byte. S'ha proposat una nova modificació del SSP, RenewSSP, la qual elimina els atacs de força bruta. Pot ser usada de manera completament transparent amb els programes existents sense introduir sobrecàrrega. El RenewSSP és especialment beneficiós en dos àrees d'aplicació: servidors de xarxa (provat en Apache) i llançadors d'aplicacions eficients (provat en Android). ASLR és un concepte genèric, del qual hi ha multitud de dissenys i implementacions. S'han analitzat les dos implementacions més rellevants de Linux (Vanilla Linux i PaX patch), trobant-se en ambdues tant debilitats com elements millorables. Tenint en compte les millores tecnològiques en el suport a l'execució (compiladors i llibreries), s'ha proposat un nou disseny de l'ASLR: ASLR-NG, el qual, maximitza l'entropia, soluciona el problema de la fragmentació i elimina les debilitats trobades. Igual que la solució proposada per al SSP, la nova proposta d'ASLR és transparent per a les aplicacions i compatible a nivell binari sense introduir sobrecàrrega. ASLR-NG ha sigut implementat com un pedaç del nucli de Linux per a la versió 4.1. La diversificació de programari és una tècnica que cobrix una àmplia gamma de fa\-llades, inclosos els errors de memòria. La principal dificultat per a aplicar esta tècnica radica en la generació de les "variants", que són programes que tenen un comportament idèntic entre ells davant d'entrades normals, però tenen un comportament diferenciat en presència d'entrades anormals. S'ha proposat una nova forma de generar variants de forma automàtica a partir d'un mateix codi font, emprant l'emulació de sistemes. Una de les màximes d'esta investigació ha sigut l'aplicabilitat dels resultats, per la qual cosa s'ha fet especial insistència en el desenrotllament de prototips sobre sistemes reals al mateix temps que es duia a terme l'estudi teòric. Com a resultat, les propostes d'esta tesi són directament aplicables a sistemes reals, algunes d'elles ja estan sent explotades en la pràctica.Marco Gisbert, H. (2015). Cyber-security protection techniques to mitigate memory errors exploitation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/57806TESISCompendi

Identifying Code Injection and Reuse Payloads In Memory Error Exploits

Today's most widely exploited applications are the web browsers and document readers we use every day. The immediate goal of these attacks is to compromise target systems by executing a snippet of malicious code in the context of the exploited application. Technical tactics used to achieve this can be classified as either code injection - wherein malicious instructions are directly injected into the vulnerable program - or code reuse, where bits of existing program code are pieced together to form malicious logic. In this thesis, I present a new code reuse strategy that bypasses existing and up-and-coming mitigations, and two methods for detecting attacks by identifying the presence of code injection or reuse payloads. Fine-grained address space layout randomization efficiently scrambles program code, limiting one's ability to predict the location of useful instructions to construct a code reuse payload. To expose the inadequacy of this exploit mitigation, a technique for "just-in-time" exploitation is developed. This new technique maps memory on-the-fly and compiles a code reuse payload at runtime to ensure it works in a randomized application. The attack also works in face of all other widely deployed mitigations, as demonstrated with a proof-of-concept attack against Internet Explorer 10 in Windows 8. This motivates the need for detection of such exploits rather than solely relying on prevention. Two new techniques are presented for detecting attacks by identifying the presence of a payload. Code reuse payloads are identified by first taking a memory snapshot of the target application, then statically profiling the memory for chains of code pointers that reuse code to implement malicious logic. Code injection payloads are identified with runtime heuristics by leveraging hardware virtualization for efficient sandboxed execution of all buffers in memory. Employing both detection methods together to scan program memory takes about a second and produces negligible false positives and false negatives provided that the given exploit is functional and triggered in the target application version. Compared to other strategies, such as the use of signatures, this approach requires relatively little effort spent on maintenance over time and is capable of detecting never before seen attacks. Moving forward, one could use these contributions to form the basis of a unique and effective network intrusion detection system (NIDS) to augment existing systems.Doctor of Philosoph

Jarvis: Bridging the Semantic Gap between Android APIs and System Calls

Android - an open-source operating system based on the Linux kernel and currently developed by Google - is widely the most used operating system for mobile devices. Although it has rich documentation for high-level APIs and applications development, some low-level mechanisms are still obscure, especially the functionality added to the Kernel in order to adapt it to run on mobile devices. In this dissertation we give an overview of Android, focusing on one of the most peculiar feature: the Binder, which is the framework that provides the Inter-Process Communication mechanism. We analyse with special attention the low-level communication protocol, presenting some code and implementation details in order to better comprehend the working of this component. We present Jarvis, a first version of a tool whose main purpose is to bridge the semantic gap between high-level Android APIs and low-level System Calls. To do this, Jarvis contains a classic kernel-level log mechanism with a smart component that deepen into Binder call in order to capture data exchanged among applications. It exploits all level of Android software stack to perform data interpretation. Jarvis operates in two phases: an on-line log collection and an off-line data analysis, hence it implements a filter mechanism for not overload Android kernel. We carry out a first trial of mapping for few representative APIs to figure out if this kind of approach can be viable, if the key features of the tool - like filter and de-serializer - are effective and what are the main challenges. Results show the substantial goodness of the approach, but also reveal some problem as far as concerns Android APIs that directly reproduce low-level functionality, because they produce recurrent pattern that may confuse the high-level behavior reconstruction. We suggest some way to improve the tool to increase powerful of it and to automatize mapping and rebuilding process

High-Fidelity Provenance:Exploring the Intersection of Provenance and Security

In the past 25 years, the World Wide Web has disrupted the way news are disseminated and consumed. However, the euphoria for the democratization of news publishing was soon followed by scepticism, as a new phenomenon emerged: fake news. With no gatekeepers to vouch for it, the veracity of the information served over the World Wide Web became a major public concern. The Reuters Digital News Report 2020 cites that in at least half of the EU member countries, 50% or more of the population is concerned about online fake news. To help address the problem of trust on information communi- cated over the World Wide Web, it has been proposed to also make available the provenance metadata of the information. Similar to artwork provenance, this would include a detailed track of how the information was created, updated and propagated to produce the result we read, as well as what agents—human or software—were involved in the process. However, keeping track of provenance information is a non-trivial task. Current approaches, are often of limited scope and may require modifying existing applications to also generate provenance information along with thei regular output. This thesis explores how provenance can be automatically tracked in an application-agnostic manner, without having to modify the individual applications. We frame provenance capture as a data flow analysis problem and explore the use of dynamic taint analysis in this context. Our work shows that this appoach improves on the quality of provenance captured compared to traditonal approaches, yielding what we term as high-fidelity provenance. We explore the performance cost of this approach and use deterministic record and replay to bring it down to a more practical level. Furthermore, we create and present the tooling necessary for the expanding the use of using deterministic record and replay for provenance analysis. The thesis concludes with an application of high-fidelity provenance as a tool for state-of-the art offensive security analysis, based on the intuition that software too can be misguided by "fake news". This demonstrates that the potential uses of high-fidelity provenance for security extend beyond traditional forensics analysis