Ransomware Detection Using Machine Learning in the Linux Kernel
- URL: http://arxiv.org/abs/2409.06452v1
- Date: Tue, 10 Sep 2024 12:17:23 GMT
- Title: Ransomware Detection Using Machine Learning in the Linux Kernel
- Authors: Adrian Brodzik, Tomasz Malec-Kruszyński, Wojciech Niewolski, Mikołaj Tkaczyk, Krzysztof Bocianiak, Sok-Yen Loui,
- Abstract summary: Linux-based cloud environments have become lucrative targets for ransomware attacks.
We propose leveraging the extended Berkeley Packet Filter (eBPF) to collect system call information regarding active processes and infer about the data directly at the kernel level.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Linux-based cloud environments have become lucrative targets for ransomware attacks, employing various encryption schemes at unprecedented speeds. Addressing the urgency for real-time ransomware protection, we propose leveraging the extended Berkeley Packet Filter (eBPF) to collect system call information regarding active processes and infer about the data directly at the kernel level. In this study, we implement two Machine Learning (ML) models in eBPF - a decision tree and a multilayer perceptron. Benchmarking latency and accuracy against their user space counterparts, our findings underscore the efficacy of this approach.
Related papers
- Cryptanalysis via Machine Learning Based Information Theoretic Metrics [58.96805474751668]
We propose two novel applications of machine learning (ML) algorithms to perform cryptanalysis on any cryptosystem.
These algorithms can be readily applied in an audit setting to evaluate the robustness of a cryptosystem.
We show that our classification model correctly identifies the encryption schemes that are not IND-CPA secure, such as DES, RSA, and AES ECB, with high accuracy.
arXiv Detail & Related papers (2025-01-25T04:53:36Z) - A Sysmon Incremental Learning System for Ransomware Analysis and Detection [1.495391051525033]
In the face of increasing cyber threats, particularly ransomware attacks, there is a pressing need for advanced detection and analysis systems.
Most of these proposals leverage non-incremental learning approaches that require the underlying models to be updated from scratch to detect new ransomware.
This approach is problematic because it leaves sensitive data vulnerable to attack during retraining, as newly emerging ransomware strains may go undetected until the model is updated.
We present the Sysmon Incremental Learning System for Analysis and Detection (SILRAD), which enables continuous updates to the underlying model and effectively closes the training gap.
arXiv Detail & Related papers (2025-01-02T06:22:58Z) - Leveraging eBPF and AI for Ransomware Nose Out [0.9012198585960441]
We propose a two-phased approach for real-time detection and deterrence of ransomware.
We leverage the capabilities of eBPF and artificial intelligence to develop both proactive and reactive methods.
Our solution achieves an impressive 99.76% accuracy in identifying ransomware incidents within a few seconds on the onset of zero-day attacks.
arXiv Detail & Related papers (2024-06-20T06:35:15Z) - Privacy preserving layer partitioning for Deep Neural Network models [0.21470800327528838]
Trusted Execution Environments (TEEs) can introduce significant performance overhead due to additional layers of encryption, decryption, security and integrity checks.
We introduce layer partitioning technique and offloading computations to GPU.
We conduct experiments to demonstrate the effectiveness of our approach in protecting against input reconstruction attacks developed using trained conditional Generative Adversarial Network(c-GAN)
arXiv Detail & Related papers (2024-04-11T02:39:48Z) - Fight Hardware with Hardware: System-wide Detection and Mitigation of Side-Channel Attacks using Performance Counters [45.493130647468675]
We present a kernel-level infrastructure that allows system-wide detection of malicious applications attempting to exploit cache-based side-channel attacks.
This infrastructure relies on hardware performance counters to collect information at runtime from all applications running on the machine.
High-level detection metrics are derived from these measurements to maximize the likelihood of promptly detecting a malicious application.
arXiv Detail & Related papers (2024-02-18T15:45:38Z) - Overload: Latency Attacks on Object Detection for Edge Devices [47.9744734181236]
This paper investigates latency attacks on deep learning applications.
Unlike common adversarial attacks for misclassification, the goal of latency attacks is to increase the inference time.
We use object detection to demonstrate how such kind of attacks work.
arXiv Detail & Related papers (2023-04-11T17:24:31Z) - Interpretable Machine Learning for Detection and Classification of
Ransomware Families Based on API Calls [5.340730281227837]
This research work utilizes the frequencies of different API calls to detect and classify ransomware families.
A WebCrawler is developed to automate collecting the Windows Portable Executable PE files of 15 different ransomware families.
Logistic Regression can efficiently classify ransomware into their corresponding families securing 9915 accuracy.
arXiv Detail & Related papers (2022-10-16T15:54:45Z) - A flow-based IDS using Machine Learning in eBPF [3.631024220680066]
eBPF is a new technology which allows dynamically loading pieces of code into the Linux kernel.
We show that it is possible to develop a flow based network intrusion detection system based on machine learning entirely in eBPF.
arXiv Detail & Related papers (2021-02-19T15:20:51Z) - Detection of Adversarial Supports in Few-shot Classifiers Using Feature
Preserving Autoencoders and Self-Similarity [89.26308254637702]
We propose a detection strategy to highlight adversarial support sets.
We make use of feature preserving autoencoder filtering and also the concept of self-similarity of a support set to perform this detection.
Our method is attack-agnostic and also the first to explore detection for few-shot classifiers to the best of our knowledge.
arXiv Detail & Related papers (2020-12-09T14:13:41Z) - Adversarial EXEmples: A Survey and Experimental Evaluation of Practical
Attacks on Machine Learning for Windows Malware Detection [67.53296659361598]
adversarial EXEmples can bypass machine learning-based detection by perturbing relatively few input bytes.
We develop a unifying framework that does not only encompass and generalize previous attacks against machine-learning models, but also includes three novel attacks.
These attacks, named Full DOS, Extend and Shift, inject the adversarial payload by respectively manipulating the DOS header, extending it, and shifting the content of the first section.
arXiv Detail & Related papers (2020-08-17T07:16:57Z) - Bayesian Optimization with Machine Learning Algorithms Towards Anomaly
Detection [66.05992706105224]
In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique.
The performance of the considered algorithms is evaluated using the ISCX 2012 dataset.
Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
arXiv Detail & Related papers (2020-08-05T19:29:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.