Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines

• URL: http://arxiv.org/abs/2501.02493v2
• Date: Mon, 27 Jan 2025 00:26:28 GMT
• Title: Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines
• Authors: Marzieh Esnaashari, Nima Moradi,
• Abstract summary: This study addresses the need for effective malware detection strategies by leveraging Machine Learning (ML) techniques. Our research aims to develop an advanced ML model that accurately predicts malware vulnerabilities based on the specific conditions of individual machines.
• Score: 0.0
• License:
• Abstract: In an era of escalating cyber threats, malware poses significant risks to individuals and organizations, potentially leading to data breaches, system failures, and substantial financial losses. This study addresses the urgent need for effective malware detection strategies by leveraging Machine Learning (ML) techniques on extensive datasets collected from Microsoft Windows Defender. Our research aims to develop an advanced ML model that accurately predicts malware vulnerabilities based on the specific conditions of individual machines. Moving beyond traditional signature-based detection methods, we incorporate historical data and innovative feature engineering to enhance detection capabilities. This study makes several contributions: first, it advances existing malware detection techniques by employing sophisticated ML algorithms; second, it utilizes a large-scale, real-world dataset to ensure the applicability of findings; third, it highlights the importance of feature analysis in identifying key indicators of malware infections; and fourth, it proposes models that can be adapted for enterprise environments, offering a proactive approach to safeguarding extensive networks against emerging threats. We aim to improve cybersecurity resilience, providing critical insights for practitioners in the field and addressing the evolving challenges posed by malware in a digital landscape. Finally, discussions on results, insights, and conclusions are presented.

Related papers

• Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects [0.33554367023486936]
  This paper provides a comprehensive review of machine learning-based Network Intrusion Detection Systems (NIDS) We critically examine existing research in NIDS, highlighting key trends, strengths, and limitations. We discuss emerging challenges in the field and offer insights for the development of more robust and resilient NIDS.
  arXiv  Detail & Related papers  (2024-09-27T13:27:29Z)
• Verification of Machine Unlearning is Fragile [48.71651033308842]
  We introduce two novel adversarial unlearning processes capable of circumventing both types of verification strategies. This study highlights the vulnerabilities and limitations in machine unlearning verification, paving the way for further research into the safety of machine unlearning.
  arXiv  Detail & Related papers  (2024-08-01T21:37:10Z)
• Safety in Graph Machine Learning: Threats and Safeguards [84.26643884225834]
  Despite their societal benefits, recent research highlights significant safety concerns associated with the widespread use of Graph ML models. Lacking safety-focused designs, these models can produce unreliable predictions, demonstrate poor generalizability, and compromise data confidentiality. In high-stakes scenarios such as financial fraud detection, these vulnerabilities could jeopardize both individuals and society at large.
  arXiv  Detail & Related papers  (2024-05-17T18:11:11Z)
• Transfer Learning in Pre-Trained Large Language Models for Malware Detection Based on System Calls [3.5698678013121334]
  This work presents a novel framework leveraging large language models (LLMs) to classify malware based on system call data. Experiments with a dataset of over 1TB of system calls demonstrate that models with larger context sizes, such as BigBird and Longformer, achieve superior accuracy and F1-Score of approximately 0.86. This approach shows significant potential for real-time detection in high-stakes environments, offering a robust solution to evolving cyber threats.
  arXiv  Detail & Related papers  (2024-05-15T13:19:43Z)
• Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection [0.5475886285082937]
  This study conducts a thorough examination of malware detection using machine learning techniques. The aim is to advance cybersecurity capabilities by identifying and mitigating threats more effectively.
  arXiv  Detail & Related papers  (2024-03-04T17:22:43Z)
• Graph Mining for Cybersecurity: A Survey [61.505995908021525]
  The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities. With the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
  arXiv  Detail & Related papers  (2023-04-02T08:43:03Z)
• Harnessing the Speed and Accuracy of Machine Learning to Advance Cybersecurity [0.0]
  Traditional signature-based methods of malware detection have limitations in detecting complex threats. In recent years, machine learning has emerged as a promising solution to detect malware effectively. ML algorithms are capable of analyzing large datasets and identifying patterns that are difficult for humans to identify.
  arXiv  Detail & Related papers  (2023-02-24T02:42:38Z)
• Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection Systems [0.0]
  This paper evaluates the robustness of six recent deep learning algorithms for intrusion detection on contaminated data. Our experiments suggest that the state-of-the-art algorithms used in this study are sensitive to data contamination and reveal the importance of self-defense against data perturbation.
  arXiv  Detail & Related papers  (2022-06-25T02:28:39Z)
• Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System [78.60415450507706]
  Recent advancements in predictive machine learning has led to its application in various use cases in manufacturing. Most research focused on maximising predictive accuracy without addressing the uncertainty associated with it. In this paper, we determine the sources of uncertainty in machine learning and establish the success criteria of a machine learning system to function well under uncertainty.
  arXiv  Detail & Related papers  (2021-07-28T10:28:05Z)
• Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses [150.64470864162556]
  This work systematically categorizes and discusses a wide range of dataset vulnerabilities and exploits. In addition to describing various poisoning and backdoor threat models and the relationships among them, we develop their unified taxonomy.
  arXiv  Detail & Related papers  (2020-12-18T22:38:47Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)

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.