API-Based Ransomware Detection Using Machine Learning-Based Threat Detection Models

Almousa, May; Basavaraju, Sai; Anwar, Mohd

doi:10.1109/pst52912.2021.9647816

Cited by 21 publications

(6 citation statements)

References 23 publications

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“…AI/ML algorithms have also been successful in detecting threats and ransomware detection [43]. AI/ML algorithms have shown promising performance on the classification task of the ransomware samples [44], and deep learning architectures have been employed to detect Trojan attacks on encrypted DNA data. The supply chain is also a crucial sector of the biotechnology Industry 5.0 and directly affects every aspect of the bioeconomy [45], from transportation to industrial machinery in labs.…”

Section: The Role Of Mlsecops In the Biotechnology Industry 50mentioning

confidence: 99%

Integrating MLSecOps in the Biotechnology Industry 5.0

Pervez,

J. Titus

2024

The Role of Cybersecurity in the Industry 5.0 Era [Working Title]

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Biotechnology Industry 5.0 is advancing with the integration of cutting-edge technologies such as Machine Learning (ML), the Internet of Things (IoT), and cloud computing. It is no surprise that an industry that utilizes data from customers and can alter their lives is a target of a variety of attacks. This chapter provides a perspective on how Machine Learning Security Operations (MLSecOps) can help secure the biotechnology Industry 5.0. The chapter provides an analysis of the threats in the biotechnology Industry 5.0 and how ML algorithms can help secure with industry best practices. This chapter explores the scope of MLSecOps in the biotechnology Industry 5.0, highlighting how crucial it is to comply with current regulatory frameworks. With biotechnology Industry 5.0 developing innovative solutions in healthcare, supply chain management, biomanufacturing, pharmaceutical sectors, and more, the chapter also discusses the MLSecOps best practices that industry and enterprises should follow while also considering ethical responsibilities. Overall, the chapter provides a discussion of how to integrate MLSecOps into the design, deployment, and regulation of the processes in the biotechnology Industry 5.0.

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Section: The Role Of Mlsecops In the Biotechnology Industry 50mentioning

confidence: 99%

Integrating MLSecOps in the Biotechnology Industry 5.0

Pervez,

J. Titus

2024

The Role of Cybersecurity in the Industry 5.0 Era [Working Title]

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“…They showed that their approach is effective with high accuracy and a low false-positive rate. Almousa et al [77] designed a new approach for ransomware detection based on Application Programming Interface and Machine Learning. Their study used dynamic analysis on the Windows platform and sandbox analysis for sampling.…”

Section: A: Desktop Platformsmentioning

confidence: 99%

The Age of Ransomware: A Survey on the Evolution, Taxonomy, and Research Directions

et al. 2023

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The proliferation of ransomware has become a significant threat to cybersecurity in recent years, causing significant financial, reputational, and operational damage to individuals and organizations. This paper aims to provide a comprehensive overview of the evolution of ransomware, its taxonomy, and its state-of-the-art research contributions. We begin by tracing the origins of ransomware and its evolution over time, highlighting the key milestones and major trends. Next, we propose a taxonomy of ransomware that categorizes different types of ransomware based on their characteristics and behavior. Subsequently, we review the existing research over several years in regard to detection, prevention, mitigation, and prediction techniques. Our extensive analysis, based on more than 150 references, has revealed that significant research, specifically 72.8%, has focused on detecting ransomware. However, a lack of emphasis has been placed on predicting ransomware. Additionally, of the studies focused on ransomware detection, a significant portion, 70%, have utilized machine learning methods. We further discuss the challenges found such as the ones related to obtaining ransomware datasets. In addition, our study uncovers a range of shortcomings in research pertaining to real-time protection and identifying zero-day ransomware. Adversarial machine learning exploitation has been identified as an under-researched area in the field. This survey is a constructive roadmap for researchers interested in ransomware research matters.

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“…Initially, investigations into this domain have highlighted the shortcomings in traditional machine learning methodologies when applied to the analysis of PE files [18,12]. Often, these limitations stem from the selection of inadequate features, which fail to capture the nuanced characteristics of ransomware [13,34]. Following this, a significant amount of research has delved into the extraction and comprehensive analysis of metadata derived from PE files [5,1].…”

Section: Portable Executable Files In Ransomware Analysismentioning

confidence: 99%

Efficient Ransomware Detection via Portable Executable File Image Analysis By LLaMA-7b

Li,

Zhu,

Zhang

2023

Preprint

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This research focuses on developing a novel ransomware detection methodology leveraging the capabilities of the open source large language model LLaMA-7b and image analysis of Portable Executable (PE) files. By transforming PE files into grayscale bitmap images and analyzing these using the LLaMA-7b model, the study introduces an innovative approach in cybersecurity. The model demonstrates high accuracy in distinguishing ransomware from benignware, with a significant true positive rate and minimal false positives and negatives. This method overcomes the limitations of traditional static and dynamic analysis, proving effective against modern ransomware variants. The findings suggest that integrating advanced technologies like LLMs in cybersecurity offers a promising direction for enhancing ransomware detection and prevention.

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API-Based Ransomware Detection Using Machine Learning-Based Threat Detection Models

Cited by 21 publications

References 23 publications

Integrating MLSecOps in the Biotechnology Industry 5.0

Integrating MLSecOps in the Biotechnology Industry 5.0

The Age of Ransomware: A Survey on the Evolution, Taxonomy, and Research Directions

Efficient Ransomware Detection via Portable Executable File Image Analysis By LLaMA-7b

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