Markov modeling of moving target defense games

We introduce a Markov-model-based framework for Moving Target Defense (MTD) analysis. The framework allows modeling of broad range of MTD strategies, provides general theorems about how the probability of a successful adversary defeating an MTD strategy is related to the amount of time/cost spent by the adversary, and shows how a multi-level composition of MTD strategies can be analyzed by a straightforward combination of the analysis for each one of these strategies. Within the proposed framework we define the concept of security capacity which measures the strength or effectiveness of an MTD strategy: the security capacity depends on MTD specific parameters and more general system parameters. We apply our framework to two concrete MTD strategies

Moving Target Defense for Web Applications

abstract: Web applications continue to remain as the most popular method of interaction for businesses over the Internet. With it's simplicity of use and management, they often function as the "front door" for many companies. As such, they are a critical component of the security ecosystem as vulnerabilities present in these systems could potentially allow malicious users access to sensitive business and personal data. The inherent nature of web applications enables anyone to access them anytime and anywhere, this includes any malicious actors looking to exploit vulnerabilities present in the web application. In addition, the static configurations of these web applications enables attackers the opportunity to perform reconnaissance at their leisure, increasing their success rate by allowing them time to discover information on the system. On the other hand, defenders are often at a disadvantage as they do not have the same temporal opportunity that attackers possess in order to perform counter-reconnaissance. Lastly, the unchanging nature of web applications results in undiscovered vulnerabilities to remain open for exploitation, requiring developers to adopt a reactive approach that is often delayed or to anticipate and prepare for all possible attacks which is often cost-prohibitive. Moving Target Defense (MTD) seeks to remove the attackers' advantage by reducing the information asymmetry between the attacker and defender. This research explores the concept of MTD and the various methods of applying MTD to secure Web Applications. In particular, MTD concepts are applied to web applications by implementing an automated application diversifier that aims to mitigate specific classes of web application vulnerabilities and exploits. Evaluation is done using two open source web applications to determine the effectiveness of the MTD implementation. Though developed for the chosen applications, the automation process can be customized to fit a variety of applications.Dissertation/ThesisMasters Thesis Computer Science 201

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