Memory Forensic Analysis Research Articles

Towards generic memory forensic framework for programmable logic controllers

A Programmable Logic Controller (PLC) is a microprocessor-based controller that is used to automate physical processes in critical infrastructure and various other industries and manufacturing sectors. Initially, PLCs were completely isolated from the Internet, and cyber security was not incorporated at the time of development. The introduction of industry 4.0 and the evolution of ICS systems to communicate over public IP addresses from the Internet enhanced productivity and efficiency, but Internet connectivity exposed the systems and their vulnerabilities, which led to an increase in cyber attacks. When a system is sabotaged/compromised, security analysts need to get to the root cause of the attack as quickly as possible to recover the system. To do so, memory forensic analysis is critical to provide a unique insight into the run-time memory activities and extract a reliable source of evidence. In this paper, we analyze the memory structure of the Schneider Electric Modicon M221 PLC. To build a memory profile, we reverse engineer the communication protocol and conduct differential analysis to gain knowledge about the structure of the memory and the low-level representation of control logic instructions. We then identify dynamic and static memory regions by modifying different project fields and conducting differential analysis, which allows us to identify boundaries of critical memory structures and extract important forensic artifacts that can be found in the memory. The Python implementation of the memory profile can help reduce the time and effort required for manual analysis in case of cyber incident or system failure.

Memory forensics tools: a comparative analysis

ABSTRACT As part of the incident response process, the memory forensics tools extract forensic artifacts and display them. Many memory forensics analysis tools are being developed to address the challenges of modern cybercrimes. Investigations are successful when they have an accurate analysis provided by a memory forensics tool that consumes resources reasonably. This paper presents a comparative analysis of three dominant memory forensics tools: Volatility, Autopsy, and Redline. We consider three malware behaviour scenarios and evaluate the forensics capabilities of these tools in each. We also experimentally measure the CPU and memory consumption of each for memory analysis in other operational states. We find that Volatility provides the most accurate memory analysis. Our measurements show that Redline consumes more CPU resources, and Autopsy needs more memory resources to analyze a memory image file. The results of our investigation will hopefully lead to tool improvement and more informed choices by users.

MDCD: A malware detection approach in cloud using deep learning

AbstractWith the increasing popularity of cloud computing applications, the threat of malware attack against cloud environments is getting worse. To defend against malware attacks in the cloud, some virtualization‐based approaches are proposed. However, the existing methods suffer from limitations in terms of detection accuracy, deployment effort, and performance cost. To address these issues, we propose MDCD, a novel dynamic malware detection solution for cloud environments. This method first utilizes a lightweight agent to collect the run‐time utilization information from the target virtual machine (VM). Then, it leverages the memory forensics analysis technique to extract the memory object information from the target VM's memory. To fully make use of the run‐time utilization and memory object information for malware detection, we propose a multi‐CNN model, which combines multiple convolutional neural networks (CNNs) efficiently. The evaluation shows that our approach can achieve an average detection accuracy, precision, recall, and F1 Score of 98.89%, 97.01%, 98.17%, and 97.89% respectively. Compared with the existing solutions, our method can detect multiple malicious processes effectively with little deployment effort.

Memory forensic analysis of a programmable logic controller in industrial control systems

In industrial control systems (ICS), programmable logic controllers (PLCs) are used to automate physical processes such as nuclear plants and power grid stations, and are often subject to cyber attacks. As in conventional IT domain, the memory analysis of the PLCs can help answer important forensic questions about the attack, such as the presence of malicious firmware, injection of modified control logic (the program running on the PLC), and manipulation of I/O devices (e.g., sensors and actuators). Unlike conventional IT domain, PLCs have heterogeneous hardware architecture, proprietary firmware and control software, making it challenging to employ a unified framework for their memory forensics. For merely extracting artifacts of forensic importance, reverse-engineering the firmware is a tedious task, and the effort needs to be repeated for every PLC model. As a community, a step-wise approach to tackle this challenge is to analyze the memory of specific PLCs, and subsequently find a generic framework applicable to all PLCs. Our work is a step forward in this direction. By following a methodology that focuses on the functional layer of PLCs instead of reverse engineering the firmware, we analyze the digital forensic artifacts available in a common PLC, Allen-Bradley ControlLogix 1756-L61. Before diving into the memory dump, we analyze the PLC control software to create a list of important artifacts that are sure to exist in the PLC memory dump. The approach employs a setup where PLC control software RSLogix-5000 is connected to the PLC, and the memory dump can be obtained as and when needed. We create test cases that sequentially highlight each category of artifacts, followed by an examination of the resultant impact on memory. After attaining the listed artifacts, we employ conventional string and known data searches to extract interesting information present in this PLC's memory. The memory analysis profile, presented as a Python library and shared with the community, can help a forensic investigator to readily extract forensic artifacts from the same model's controller. The adopted approach may help researchers in creating memory profile of other PLCs, and ultimately formulating a generic PLC memory analysis framework.

JTAG-based PLC memory acquisition framework for industrial control systems

In industrial control systems (ICS), programmable logic controllers (PLC) are the embedded devices that directly control and monitor critical industrial infrastructure processes such as nuclear plants and power grid stations. Cyberattacks often target PLCs to sabotage a physical process. A memory forensic analysis of a suspect PLC can answer questions about an attack, including compromised firmware and manipulation of PLC control logic code and I/O devices. Given physical access to a PLC, collecting forensic information from the PLC memory at the hardware-level is risky and challenging. It may cause the PLC to crash or hang since PLCs have proprietary, legacy hardware with heterogeneous architecture. This paper addresses this research problem and proposes a novel JTAG (Joint Test Action Group)-based framework, Kyros, for reliable PLC memory acquisition. Kyros systematically creates a JTAG profile of a PLC through hardware assessment, JTAG pins identification, memory map creation, and optimizing acquisition parameters. It also facilitates the community of interest (such as ICS owners, operators, and vendors) to develop the JTAG profiles of PLCs. Further, we present a case study of Kyros implementation over Allen-Bradley 1756-A10/B to help understand the framework's application on a real-world PLC used in industry settings. The sample PLC memory dumps are shared with the research community to facilitate further research.

Zooming into the pandemic! A forensic analysis of the Zoom Application

The global pandemic of COVID-19 has turned the spotlight on video conferencing applications like never before. In this critical time, applications such as Zoom have experienced a surge in its user base jump over the 300 million daily mark (ZoomBlog, 2020). The increase in use has led malicious actors to exploit the application, and in many cases perform Zoom Bombings. Therefore forensically examining Zoom is inevitable. Our work details the primary disk, network, and memory forensic analysis of the Zoom video conferencing application. Results demonstrate it is possible to find users' critical information in plain text and/or encrypted/encoded, such as chat messages, names, email addresses, passwords, and much more through network captures, forensic imaging of digital devices, and memory forensics. Furthermore we elaborate on interesting anti-forensics techniques employed by the Zoom application when contacts are deleted from the Zoom application's contact list.

Malware Detection Based on Multi-level and Dynamic Multi-feature Using Ensemble Learning at Hypervisor

As more and more applications migrate to clouds, the type and amount of malware attack against virtualized environments are increasing, which is a key factor that restricts the widespread deployment and application of cloud platforms. Traditional in-VM-based security software is not effective against malware attacks, as the security software itself becomes the target of malware attacks and can easily be tampered with or even subverted. In this paper, we propose a new malware detection method to improve virtual machine security performance and ensure the security of the entire cloud platform. This paper uses the virtual machine introspection(VMI) combined with the memory forensics analysis(MFA) technology to extract multiple types of dynamic features from the virtual machine memory, the hypervisor layer and the hardware layer. Furthermore, this paper proposes an adaptive feature selection method. By combining three different search strategies, three types of features are compared and analyzed from three aspects: effectiveness, system load and security. By adjusting the weight of each feature, it meets the detection requirements of different malware in the cloud environment as expected. Finally, the detection method improves the detection accuracy and generalization ability of the overall classifier using the AdaBoost ensemble learning method with Voting’s combination strategy. The experiment used a large number of real malicious samples, and achieved an accuracy of 0.999 (AUC), with a maximum performance overhead of 5.6%.

TKRD: Trusted kernel rootkit detection for cybersecurity of VMs based on machine learning and memory forensic analysis.

The promotion of cloud computing makes the virtual machine (VM) increasingly a target of malware attacks in cybersecurity such as those by kernel rootkits. Memory forensic, which observes the malicious tracks from the memory aspect, is a useful way for malware detection. In this paper, we propose a novel TKRD method to automatically detect kernel rootkits in VMs from private cloud, by combining VM memory forensic analysis with bio-inspired machine learning technology. Malicious features are extracted from the memory dumps of the VM through memory forensic analysis method. Based on these features, various machine learning classifiers are trained including Decision tree, Rule based classifiers, Bayesian and Support vector machines (SVM). The experiment results show that the Random Forest classifier has the best performance which can effectively detect unknown kernel rootkits with an Accuracy of 0.986 and an AUC value (the area under the receiver operating characteristic curve) of 0.998.

Signature based volatile memory forensics: a detection based approach for analyzing sophisticated cyber attacks

Volatile memory forensics—a live forensic approach to collect real time activity based artifacts which may not be possible through postmortem forensics. Volatile memory forensics techniques inspect RAM to extract information such as passwords, encryption keys, network activity, open files and the set of processes and threads currently running within an operating system. Volatile memory dump is used for offline investigation of live data. In this research signature based artifacts identification done using keywords and default hex values. In this research various challenging scenario is discussed and using regular expressions evidence signatures are identified. Besides these scenarios recent Ransomware attacks can also be solved using volatile memory forensic analysis.

Leveraging virtual machine introspection with memory forensics to detect and characterize unknown malware using machine learning techniques at hypervisor

The Virtual Machine Introspection (VMI) has emerged as a fine-grained, out-of-VM security solution that detects malware by introspecting and reconstructing the volatile memory state of the live guest Operating System (OS). Specifically, it functions by the Virtual Machine Monitor (VMM), or hypervisor. The reconstructed semantic details obtained by the VMI are available in a combination of benign and malicious states at the hypervisor. In order to distinguish between these two states, the existing out-of-VM security solutions require extensive manual analysis. In this paper, we propose an advanced VMM-based, guest-assisted Automated Internal-and-External (A-IntExt) introspection system by leveraging VMI, Memory Forensics Analysis (MFA), and machine learning techniques at the hypervisor. Further, we use the VMI-based technique to introspect digital artifacts of the live guest OS to obtain a semantic view of the processes details. We implemented an Intelligent Cross View Analyzer (ICVA) and implanted it into our proposed A-IntExt system, which examines the data supplied by the VMI to detect hidden, dead, and dubious processes, while also predicting early symptoms of malware execution on the introspected guest OS in a timely manner. Machine learning techniques are used to analyze the executables that are mined and extracted using MFA-based techniques and ascertain the malicious executables. The practicality of the A-IntExt system is evaluated by executing large real-world malware and benign executables onto the live guest OSs. The evaluation results achieved 99.55% accuracy and 0.004 False Positive Rate (FPR) on the 10-fold cross-validation to detect unknown malware on the generated dataset. Additionally, the proposed system was validated against other benchmarked malware datasets and the A-IntExt system outperforms the detection of real-world malware at the VMM with performance exceeding 6.3%.

Automated multi-level malware detection system based on reconstructed semantic view of executables using machine learning techniques at VMM

In order to fulfill the requirements like stringent timing restraints and demand on resources, Cyber–Physical System (CPS) must deploy on the virtualized environment such as cloud computing. To protect Virtual Machines (VMs) in which CPSs are functioning against malware-based attacks, malware detection and mitigation technique is emerging as a highly crucial concern. The traditional VM-based anti-malware software themselves a potential target for malware-based attack since they are easily subverted by sophisticated malware. Thus, a reliable and robust malware monitoring and detection systems are needed to detect and mitigate rapidly the malware based cyber-attacks in real time particularly for virtualized environment. The Virtual Machine Introspection (VMI) has emerged as a fine-grained out-of-VM security solution to detect malware by introspecting and reconstructing the volatile memory state of the live guest Operating System (OS) by functioning at the Virtual Machine Monitor (VMM) or hypervisor. However, the reconstructed semantic details by the VMI are available in a combination of benign and malicious states at the hypervisor. In order to distinguish between these two states, extensive manual analysis is required by the existing out-of-VM security solutions. To address the foremost issue, in this paper, we propose an advanced VMM-based guest-assisted Automated Multilevel Malware Detection System (AMMDS) that leverages both VMI and Memory Forensic Analysis (MFA) techniques to predict early symptoms of malware execution by detecting stealthy hidden processes on a live guest OS. More specifically, the AMMDS system detects and classifies the actual running malicious executables from the semantically reconstructed process view of the guest OS. The two sub-components of the AMMDS are: Online Malware Detector (OMD) and Offline Malware Classifier (OFMC). The OMD recognizes whether the running processes are benign or malicious using its Local Malware Signature Database (LMSD) and online malware scanner and the OFMC classify unknown malware by adopting machine learning techniques at the hypervisor. The AMMDS has been evaluated by executing large real-world malware and benign executables on to the live guest OSs. The evaluation results achieved 100% of accuracy and zero False Positive Rate (FPR) on the 10-fold cross-validation in classifying unknown malware with maximum performance overhead of 5.8%.

Live memory forensics of mobile phones

In this paper, we proposed an automated system to perform a live memory forensic analysis for mobile phones. We investigated the dynamic behavior of the mobile phone’s volatile memory, and the analysis is useful in real-time evidence acquisition analysis of communication based applications. Different communication scenarios with varying parameters were investigated. Our experimental results showed that outgoing messages (from the phone) have a higher persistency than the incoming messages. In our experiments, we consistently achieved a 100% evidence acquisition rate with the outgoing messages. For the incoming messages, the acquisition rates ranged from 75.6% to 100%, considering a wide range of varying parameters in different scenarios. Hence, in a more realistic scenario where the parties may occasionally take turns to send messages and consecutively send a few messages, our acquisition can capture most of the data to facilitate further detailed forensic investigation.

FATKit: A framework for the extraction and analysis of digital forensic data from volatile system memory

We present the Forensic Analysis ToolKit (FATKit) – a modular, extensible framework that increases the practical applicability of volatile memory forensic analysis by freeing human analysts from the prohibitively-tedious aspects of low-level data extraction. FATKit allows analysts to focus on higher-level tasks by providing novel methods for automatically deriving digital object definitions from C source code, extracting those objects from memory images, and visualizing the underlying data in various ways. FATKit presently includes modules for general virtual address space reconstruction and visualization, as well as Linux- and Windows-specific kernel analysis.