Detection Of Kernel Rootkits Research Articles

Kernel rootkit detection multi class on deep learning techniques

The harmful code application known as a rootkit is designed to be loaded and run directly from the operating system's (OSs') Kernel. Rootkits deployed in the Kernel, called Kernel-mode rootkits, can alter the OS. The intention behind these Kernel changes is to conceal the hack. Detecting a Kernel rootkit in a target machine is found to be quite challenging. Numerous techniques can be employed to modify the Kernel of a system. Kernel rootkits also create hidden access for attacks, enabling unauthorized entry to be gained by attackers on the machine. The ultimate consequence is that essential computer data can be modified, personal information can be gathered, and hackers can observe behavior. Synthetic neural networks support artificial intelligence, a branch of deep learning that models the human brain and operates on large datasets. This study proposed the Kernel rootkit detection multi-class deep learning techniques (KRDMCDLT). Deep learning algorithms are utilized to recognize the Kernel rootkit from a batch of data by selecting essential properties for learning tracking models. Thus, by identifying the OS malware, trojan assaults can be stopped before they can access infected data. This Kernel rootkit detection was tested in a Google Cloud Platform (GCP) computing system.

Non-Volatile Kernel Root kit Detection and Prevention in Cloud Computing

The field of web has turned into a basic part in everyday life. Security in the web has dependably been a significant issue. Malware is utilized to rupture into the objective framework. There are various kinds of malwares, for example, infection, worms, rootkits, trojan pony, ransomware, etc. Each malware has its own way to deal with influence the objective framework in various ways, in this manner making hurt the framework. The rootkit may be in some arbitrary records, which when opened can change or erase the substance or information in the objective framework. Likewise, by opening the rootkit contaminated record may debase the framework execution. Hence, in this paper, a Kernel Rootkit Detection and Prevention (KRDP) framework is proposed an avert the records. The avoidance system in this paper utilizes a calculation to forestall the opening of the rootkit influenced record as portrayed. By and large, the framework comprises of a free antivirus programming which is restricted to certain functionalities. The proposed model beats the functionalities by utilizing a calculation, in this way identifying the rootkits first and afterward cautioning the client to react to the rootkit tainted record. In this way, keeping the client from opening the rootkit contaminated record. Inevitably, in the wake of expelling the tainted document from the framework will give an improvement in the general framework execution.

A Kernel Rootkit Detection Approach Based on Virtualization and Machine Learning

OS kernel is the core part of the operating system, and it plays an important role for OS resource management. A popular way to compromise OS kernel is through a kernel rootkit (i.e., malicious kernel module). Once a rootkit is loaded into the kernel space, it can carry out arbitrary malicious operations with high privilege. To defeat kernel rootkits, many approaches have been proposed in the past few years. However, existing methods suffer from some limitations: 1) most methods focus on user-mode rootkit detection; 2) some methods are limited to detect obfuscated kernel modules; and 3) some methods introduce significant performance overhead. To address these problems, we propose VKRD, a kernel rootkit detection system based on the hardware assisted virtualization technology. Compared with previous methods, VKRD can provide a transparent and an efficient execution environment for the target kernel module to reveal its run-time behavior. To select the important run-time features for training our detection models, we utilize the TF-IDF method. By combining the hardware assisted virtualization and machine learning techniques, our kernel rootkit detection solution could be potentially applied in the cloud environment. The experiments show that our system can detect windows kernel rootkits with high accuracy and moderate performance cost.

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.

Reusing Hardware Performance Counters to Detect and Identify Kernel Control-Flow Modifying Rootkits

Kernel rootkits are formidable threats to computer systems. They are stealthy and can have unrestricted access to system resources. This paper presents NumChecker, a new virtual machine (VM) monitor based framework to detect and identify control-flow modifying kernel rootkits in a guest VM. NumChecker detects and identifies malicious modifications to a system call in the guest VM by measuring the number of certain hardware events that occur during the system call’s execution. To automatically count these events, NumChecker leverages the hardware performance counters (HPCs), which exist in modern processors. By using HPCs, the checking cost is significantly reduced and the tamper-resistance is enhanced. We implement a prototype of NumChecker on Linux with the kernel-based VM. An HPC-based two-phase kernel rootkit detection and identification technique is presented and evaluated on a number of real-world kernel rootkits. The results demonstrate its practicality and effectiveness.