A Survey of Machine Learning Algorithms for Detecting Ransomware Encryption Activity

• URL: http://arxiv.org/abs/2110.07636v1
• Date: Thu, 14 Oct 2021 18:02:31 GMT
• Title: A Survey of Machine Learning Algorithms for Detecting Ransomware Encryption Activity
• Authors: Erik Larsen, David Noever, Korey MacVittie
• Abstract summary: A survey of machine learning techniques trained to detect ransomware is presented. This work builds upon the efforts of Taylor et al. in using sensor-based methods to identify encryption activity. A random forest model produces scores of 93% accuracy and 92% F1, showing that sensor-based detection is currently a viable option to detect even zero-day ransomware attacks before the code fully executes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A survey of machine learning techniques trained to detect ransomware is presented. This work builds upon the efforts of Taylor et al. in using sensor-based methods that utilize data collected from built-in instruments like CPU power and temperature monitors to identify encryption activity. Exploratory data analysis (EDA) shows the features most useful from this simulated data are clock speed, temperature, and CPU load. These features are used in training multiple algorithms to determine an optimal detection approach. Performance is evaluated with accuracy, F1 score, and false-negative rate metrics. The Multilayer Perceptron with three hidden layers achieves scores of 97% in accuracy and F1 and robust data preparation. A random forest model produces scores of 93% accuracy and 92% F1, showing that sensor-based detection is currently a viable option to detect even zero-day ransomware attacks before the code fully executes.

Related papers

• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• A Machine Learning-based Algorithm for Automated Detection of Frequency-based Events in Recorded Time Series of Sensor Data [0.0]
  This work proposes a novel event detection method that allows to identify frequency-based events in time series data. For the analysis of unseen time series data, events can be detected in their scalograms with the trained object detection model. The algorithm, evaluated on unseen datasets, achieves a precision rate of 0.97 in event detection.
  arXiv  Detail & Related papers  (2023-10-16T21:35:23Z)
• Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature [36.31281981509264]
  Large language models (LLMs) have shown the ability to produce fluent and cogent content. To build trustworthy AI systems, it is imperative to distinguish between machine-generated and human-authored content. Fast-DetectGPT is an optimized zero-shot detector that substitutes DetectGPT's perturbation step with a more efficient sampling step.
  arXiv  Detail & Related papers  (2023-10-08T11:41:28Z)
• PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning [58.85063149619348]
  We propose PULL, an iterative log analysis method for reactive anomaly detection based on estimated failure time windows. Our evaluation shows that PULL consistently outperforms ten benchmark baselines across three different datasets.
  arXiv  Detail & Related papers  (2023-01-25T16:34:43Z)
• Effective Metaheuristic Based Classifiers for Multiclass Intrusion Detection [0.0]
  Intrusion detection plays an important role in the security of information systems or networks devices. Having a large amount of data is one of the key problems in detecting attacks. A feature selection method plays a key role to select best features to achieve maximum accuracy.
  arXiv  Detail & Related papers  (2022-10-06T04:56:01Z)
• A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics [56.86150790999639]
  We present two anomaly detection and classification approaches, namely the Matrix Profile algorithm and anomaly transformer. The Matrix Profile algorithm is shown to be well suited as a generalizable approach for detecting real-time anomalies in streaming time-series data. A series of custom filters is created and added to the detector to tune its sensitivity, recall, and detection accuracy.
  arXiv  Detail & Related papers  (2022-09-23T06:09:35Z)
• MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge [110.7678032481059]
  We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings.
  arXiv  Detail & Related papers  (2022-09-22T16:44:59Z)
• Modern Cybersecurity Solution using Supervised Machine Learning [0.456877715768796]
  Traditional Firewall and Intrusion Detection system fails to detect new attacks, zero-day attacks, and traffic patterns that do not match with configured rules. We used Netflow datasets to extract features after applying data analysis. Our experiments focus on how efficient machine learning algorithms can detect Bot traffic, Malware traffic, and background traffic.
  arXiv  Detail & Related papers  (2021-09-15T22:03:50Z)
• Smartphone Impostor Detection with Behavioral Data Privacy and Minimalist Hardware Support [7.374079197112307]
  Impostors are attackers who take over a smartphone and gain access to the legitimate user's confidential and private information. This paper proposes a defense-in-depth mechanism to detect impostors quickly with simple Deep Learning algorithms. We also show how a minimalist hardware module, dubbed SID for Smartphone Impostor Detector, can be designed and integrated into smartphones for self-contained impostor detection.
  arXiv  Detail & Related papers  (2021-03-11T04:39:53Z)
• 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)
• Detection of gravitational-wave signals from binary neutron star mergers using machine learning [52.77024349608834]
  We introduce a novel neural-network based machine learning algorithm that uses time series strain data from gravitational-wave detectors. We find an improvement by a factor of 6 in sensitivity to signals with signal-to-noise ratio below 25. A conservative estimate indicates that our algorithm introduces on average 10.2 s of latency between signal arrival and generating an alert.
  arXiv  Detail & Related papers  (2020-06-02T10:20:11Z)

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.