A game-theoretic model of kleptoparasitic behavior in polymorphic populations

Kleptoparasitism, the stealing of food by one animal from another, is a widespread biological phenomenon. In this paper we build upon earlier models to investigate a population of conspecifics involved in foraging and, potentially, kleptoparasitism. We assume that the population is composed of four types of individuals, according to their strategic choices when faced with an opportunity to steal and to resist an attack. The fitness of each type of individual depends upon various natural parameters, for example food density, the handling time of a food item and the probability of mounting a successful attack against resistance, as well as the choices that they make. We find the evolutionarily stable strategies (ESSs) for all parameter combinations and show that there are six possible ESSs, four pure and two mixtures of two strategies, that can occur. We show that there is always at least one ESS, and sometimes two or three. We further investigate the influence of the different parameters on when each type of solution occurs

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

HyperDbg: Reinventing Hardware-Assisted Debugging (Extended Version)

Software analysis, debugging, and reverse engineering have a crucial impact in today's software industry. Efficient and stealthy debuggers are especially relevant for malware analysis. However, existing debugging platforms fail to address a transparent, effective, and high-performance low-level debugger due to their detectable fingerprints, complexity, and implementation restrictions. In this paper, we present HyperDbg, a new hypervisor-assisted debugger for high-performance and stealthy debugging of user and kernel applications. To accomplish this, HyperDbg relies on state-of-the-art hardware features available in today's CPUs, such as VT-x and extended page tables. In contrast to other widely used existing debuggers, we design HyperDbg using a custom hypervisor, making it independent of OS functionality or API. We propose hardware-based instruction-level emulation and OS-level API hooking via extended page tables to increase the stealthiness. Our results of the dynamic analysis of 10,853 malware samples show that HyperDbg's stealthiness allows debugging on average 22% and 26% more samples than WinDbg and x64dbg, respectively. Moreover, in contrast to existing debuggers, HyperDbg is not detected by any of the 13 tested packers and protectors. We improve the performance over other debuggers by deploying a VMX-compatible script engine, eliminating unnecessary context switches. Our experiment on three concrete debugging scenarios shows that compared to WinDbg as the only kernel debugger, HyperDbg performs step-in, conditional breaks, and syscall recording, 2.98x, 1319x, and 2018x faster, respectively. We finally show real-world applications, such as a 0-day analysis, structure reconstruction for reverse engineering, software performance analysis, and code-coverage analysis

Invesitigation of Malware and Forensic Tools on Internet

Malware is an application that is harmful to your forensic information. Basically, malware analyses is the process of analysing the behaviours of malicious code and then create signatures to detect and defend against it.Malware, such as Trojan horse, Worms and Spyware severely threatens the forensic security. This research observed that although malware and its variants may vary a lot from content signatures, they share some behaviour features at a higher level which are more precise in revealing the real intent of malware. This paper investigates the various techniques of malware behaviour extraction and analysis. In addition, we discuss the implications of malware analysis tools for malware detection based on various techniques

Dynamic and Transparent Analysis of Commodity Production Systems

We propose a framework that provides a programming interface to perform complex dynamic system-level analyses of deployed production systems. By leveraging hardware support for virtualization available nowadays on all commodity machines, our framework is completely transparent to the system under analysis and it guarantees isolation of the analysis tools running on its top. Thus, the internals of the kernel of the running system needs not to be modified and the whole platform runs unaware of the framework. Moreover, errors in the analysis tools do not affect the running system and the framework. This is accomplished by installing a minimalistic virtual machine monitor and migrating the system, as it runs, into a virtual machine. In order to demonstrate the potentials of our framework we developed an interactive kernel debugger, nicknamed HyperDbg. HyperDbg can be used to debug any critical kernel component, and even to single step the execution of exception and interrupt handlers.Comment: 10 pages, To appear in the 25th IEEE/ACM International Conference on Automated Software Engineering, Antwerp, Belgium, 20-24 September 201

Kernel Integrity Analysis

Rootkits are dangerous and hard to detect. A rootkit is malware specifically designed to be stealthy and maintain control of a computer. Existing detection mechanisms are insufficient to reliably detect rootkits, due to fundamental problems with the way they operate. This MQP has two major contributions. The first is a Red Team analysis of WinKIM, a rootkit detection tool. The analysis shows my attempts to find flaws in WinKIM\u27s ability to detect rootkits. WinKIM monitors a subset of Windows data structures; I show that this set is insufficient to detect all possible rootkits. The second is the enumeration of data structures in the Windows kernel which can be targeted by a rootkit. These structures are those which a detector would have to measure in order to detect any rootkit