Formalization and Detection of Host-Based Code Injection Attacks in the Context of Malware

The Host-Based Code Injection Attack (HBCIAs) is a technique that malicious software utilizes in order to avoid detection or steal sensitive information. In a nutshell, this is a local attack where code is injected across process boundaries and executed in the context of a victim process. Malware employs HBCIAs on several operating systems including Windows, Linux, and macOS. This thesis investigates the topic of HBCIAs in the context of malware. First, we conduct basic research on this topic. We formalize HBCIAs in the context of malware and show in several measurements, amongst others, the high prevelance of HBCIA-utilizing malware. Second, we present Bee Master, a platform-independent approach to dynamically detect HBCIAs. This approach applies the honeypot paradigm to operating system processes. Bee Master deploys fake processes as honeypots, which are attacked by malicious software. We show that Bee Master reliably detects HBCIAs on Windows and Linux. Third, we present Quincy, a machine learning-based system to detect HBCIAs in post-mortem memory dumps. It utilizes up to 38 features including memory region sparseness, memory region protection, and the occurence of HBCIA-related strings. We evaluate Quincy with two contemporary detection systems called Malfind and Hollowfind. This evaluation shows that Quincy outperforms them both. It is able to increase the detection performance by more than eight percent

THREATS ON REAL, EMULATED AND VIRTUALIZED INTEL X86 MACHINE CODE EXECUTION

Cybercriminals have all the interest in not being detected while perpetrating their intentions. Impeding such threats to spread has become of valuable importance. This goal can be achieved working on the threat vectors cybercriminals use or directly on the threat once identified. Among threat vectors we can cite application software vulnerabilities which can be abused by malware and malicious users to gain access to systems and confidential data. To be able to impede exploitation of such vulnerabilities, security specialists need to be aware of attack techniques used by malware and malicious users for to be able to design and implement effective protection techniques. For identifying threats, it is of vital importance to use effective analysis tools which expose no weaknesses to malware authors giving them the chance to evade detection. This dissertation presents two approaches for testing CPU emulators and system virtual machines which represent a fundamental component of dynamic malware analysis. These testing methodologies can be used to identify behavioural differences between real and emulated hardware. Differences exploitable by malware authors to detect emulation and hide their malicious behaviour. This dissertation also presents a new exploitation technique against memory error vulnerabilities able to circumvent widely adopted protection strategies like W^X and ASLR and the related countermeasure to impede exploitation

Address-Space Randomization for Windows Systems

Address-space randomization (ASR) is a promising solution to defend against memory corruption attacks that have contributed to about three-quarters of US-CERT advisories in the past few years. Several techniques have been proposed for implementing ASR on Linux, but its application to Microsoft Windows, the largest monoculture on the Internet, has not received as much attention. We address this problem in this paper and describe a solution that provides about 15-bits of randomness in the locations of all (code or data) objects. Our randomization is applicable to all processes on a Windows box, including all core system services, as well as applications such as web browsers, office applications, and so on. Our solution has been deployed continuously for about a year on a desktop system used daily, and is robust enough for production use