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Rootkits are malicious programs designed to conceal their activities on compromised systems, making them challenging to detect using conventional methods. As the threat landscape continually evolves, rootkits pose a serious threat by stealthily concealing malicious activities, making their early detection crucial to prevent data breaches and system compromise. A promising strategy for monitoring system activities involves analyzing volatile memory. The process can be made more reliable by the use of Machine learning and deep learning algorithms too.
Memory analysis with tools like Volatility can greatly aid in the detection of rootkits by examining the contents of a system's volatile memory. In this process, a memory dump is acquired from the target system, and Volatility is utilized to parse and interpret the information within. Initial steps involve identifying the profile of the memory dump to ensure compatibility with Volatility's analysis tools. Subsequently, the framework is employed to list running processes, scrutinize loaded kernel modules, and unveil potentially hidden processes or threads. Rootkits often utilize these tactics to remain stealthy on a compromised system. Moreover, the analysis extends to inspecting network connections, registry entries, and files in memory, as rootkits commonly manipulate these elements for persistence and covert operations. By leveraging the capabilities of Volatility, analysts gain a comprehensive view of a system's memory, enabling the identification of irregularities indicative of rootkit activity.
In practical terms, commands such as `pslist`, `modscan`, `dlllist`, `netscan`, `svscan`, `filescan`, and `dumpfiles` are executed with appropriate parameters to extract and analyze relevant information from the memory dump. The output from these commands aids in uncovering any anomalies or indicators of malicious activity. Rootkit detection through memory analysis is an essential aspect of digital forensics and incident response, allowing security professionals to understand and mitigate the impact of these sophisticated forms of malware on compromised systems.
Study | Learning Algorithm | Performance Metrics | Dataset | Strengths | Limitations |
---|---|---|---|---|---|
Djenna et al. [5] | DNN, CNN, RF, DT | Accuracy, precision, recall, F1-score | CICAndMal2017 [14] | Utilizes dynamic deep learning and heuristic. | Lack of in-depth analysis on potential false positives. Limited exploration of feature engineering. |
Sihwail et al. [6] | SVM, Naïve Bayes, k-NN, RF, DT | Accuracy, precision, recall, F1-score, False positive rates | Sihwail [7] | Utilizes memory features extracted from memory images. Incorporates feature engineering and binary vectors for training and testing. | Potential over-fitting due to high accuracy rate on training data. |
Bokzir et al. [8] | CNN | Accuracy, precision, recall, F1-score, ROC-AUC | Dampware10 [15] | Comprehensive dataset with malware and benign samples. Inclusion of GIST descriptors and HOG features. | Limited explanation of dataset creation process. Limited discussion on feature extraction methods. |
Lashkari et al. [9] | Adaboost, RF, k-NN, DT | False positives, False negatives, Accuracy, F1- Score, Precision | VolMemLyzer [16] | Handles feature selection automatically. Resistant to over-fitting. | Could be computationally expensive. |
Carrier et al. [10] | RF, DT, k-NN, Naïve Bayes, SVM, Logistic Regression | Accuracy, F1- Score, Precision, Recall | CIC-MalMem-2022 [17] | High accuracy and fast classification. | Prone to over-fitting. |
Wang et al. [11] | RF, DT, Bayesian | TPR, FPR, AUC, F-measure, Accuracy | [11] | Integration of memory forensics analysis. | Limited exploration of feature selection. |
In conclusion, the escalating threat landscape posed by rootkits necessitates advanced detection mechanisms, particularly in the face of their evolving sophistication and the increasing frequency of attacks. Memory analysis, exemplified by tools like Volatility, emerges as a crucial aspect of rootkit detection, offering insight into a system's volatile memory to identify irregularities indicative of malicious activity. The comprehensive review of contemporary research underscores the effectiveness of diverse methodologies, including dynamic deep learning, SVM, RF, computer vision, and dynamic analysis, in combating rootkit threats. The summarized comparison of related work provides a valuable reference for understanding the strengths and limitations of different approaches. Collectively, these studies illuminate a multi-dimensional approach, combining memory analysis and various detection methodologies, as a promising strategy to address the complex and evolving challenges posed by rootkits in the dynamic landscape of cybersecurity.