Biblio

Historically, attackers have sought to manipulate programs through the corruption of return addresses, function pointers, and other control flow data. However, as protections like ASLR, stack canaries, and no-execute bits have made such attacks more difficult, data-oriented exploits have received increasing attention. Such exploits try to subvert a program by reading or writing non-control data, without introducing any foreign code or violating the program’s legitimate control flow graph. Recently, a data-oriented exploitation technique called memory cartography was introduced, in which an attacker navigates between allocated memory regions using a precompiled map to disclose sensitive program data. The efficacy of memory cartography is dependent on inter-region pointers being located at constant offsets within memory regions; thus, cartographic attacks are difficult to launch against memory regions like heaps and stacks that have nondeterministic layouts. In this paper, we lower the barrier to successful attacks against nondeterministic memory, demonstrating that pointers between regions of memory often possess unique “signatures” that allow attackers to identify them with high accuracy. These signatures are accurate even when the pointers reside in non-deterministic memory areas. In many real-world programs, this allows an attacker that is capable of reading bytes from a single heap to access all of process memory. Our findings underscore the importance of memory isolation via separate address spaces.

Malware is any software that causes harm to the user information, computer systems or network. Modern computing and internet systems are facing increase in malware threats from the internet. It is observed that different malware follows the same patterns in their structure with minimal alterations. The type of threats has evolved, from file-based malware to fileless malware, such kind of threats are also known as Advance Volatile Threat (AVT). Fileless malware is complex and evasive, exploiting pre-installed trusted programs to infiltrate information with its malicious intent. Fileless malware is designed to run in system memory with a very small footprint, leaving no artifacts on physical hard drives. Traditional antivirus signatures and heuristic analysis are unable to detect this kind of malware due to its sophisticated and evasive nature. This paper provides information relating to detection, mitigation and analysis for such kind of threat.

Malware is one of the biggest threats in cloud computing. Malware running inside virtual machines or containers could steal critical information or continue to attack other cloud nodes. To detect malware in cloud, especially zero-day malware, signature-and machine-learning-based approaches are proposed to analyze the execution binary. However, malicious binary files may not permanently be stored in the file system of virtual machine or container, periodically scanner may not find the target files. Dynamic analysis approach usually introduce run-time overhead to virtual machines, which is not widely used in cloud. To solve these problems, we propose a memory analysis approach to detect malware, employing the deep learning technology. The system analyzes the memory image periodically during malware execution, which will not introduce run-time overhead. We first extract the memory snapshot from running virtual machines or containers. Then, the snapshot is converted to a grayscale image. Finally, we employ CNN to detect malware. In the learning phase, malicious and benign software are trained. In the testing phase, we test our system with real-world malwares.

The difficult of detecting, response, tracing the malicious behavior in cloud has brought great challenges to the law enforcement in combating cybercrimes. This paper presents a malicious behavior oriented framework of detection, emergency response, traceability, and digital forensics in cloud environment. A cloud-based malicious behavior detection mechanism based on SDN is constructed, which implements full-traffic flow detection technology and malicious virtual machine detection based on memory analysis. The emergency response and traceability module can clarify the types of the malicious behavior and the impacts of the events, and locate the source of the event. The key nodes and paths of the infection topology or propagation path of the malicious behavior will be located security measure will be dispatched timely. The proposed IaaS service based forensics module realized the virtualization facility memory evidence extraction and analysis techniques, which can solve volatile data loss problems that often happened in traditional forensic methods.

As recently shown in 2013, Android-driven smartphones and tablet PCs are vulnerable to so-called cold boot attacks. With physical access to an Android device, forensic memory dumps can be acquired with tools like FROST that exploit the remanence effect of DRAM to read out what is left in memory after a short reboot. While FROST can in some configurations be deployed to break full disk encryption, encrypted user partitions are usually wiped during a cold boot attack, such that a post-mortem analysis of main memory remains the only source of digital evidence. Therefore, we provide an in-depth analysis of Android's memory structures for system and application level memory. To leverage FROST in the digital investigation process of Android cases, we provide open-source Volatility plugins to support an automated analysis and extraction of selected Dalvik VM memory structures.