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Salud, Ciencia y Tecnología Metaverse Basic and Applied Research Data & Metadata Interdisciplinary Rehabilitation Salud, Ciencia y Tecnología - Serie de Conferencias Community and Interculturality in Dialogue Seminars in Medical Writing and Education Health Leadership and Quality of Life Gamification and Augmented Reality Southern perspective / Perspectiva austral
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Published: 2023-12-29
DOI: https://doi.org/10.56294/dm2023139

Achieving Organizational Effectiveness through Machine Learning Based Approaches for Malware Analysis and Detection

By
Md Alimul Haque ,
Md Alimul Haque

Department of Computer Science, Veer Kunwar Singh University, Ara, 802301, India

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Sultan Ahmad ,
Sultan Ahmad

Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia

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Deepa Sonal ,
Deepa Sonal

Department of Computer Science, Patna Women's College, Patna, India

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Hikmat A. M. Abdeljaber ,
Hikmat A. M. Abdeljaber

Department of Computer Science, Faculty of Information Technology, Applied Science Private University, Amman, Jordan

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B.K.Mishra ,
B.K.Mishra

Department of Physics, Veer Kunwar Singh University, Ara, 802301, India

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A.E.M. Eljialy ,
A.E.M. Eljialy

Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, P.O.Box. 151, Alkharj, 11942, Saudi Arabia

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Sultan Alanazi ,
Sultan Alanazi

Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University. Alkharj, 11942, Saudi Arabia

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Jabeen Nazeer ,
Jabeen Nazeer

Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia

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Abstract

Introduction: As technology usage grows at an exponential rate, cybersecurity has become a primary concern. Cyber threats have become increasingly advanced and specific, posing a severe risk to individuals, businesses, and even governments. The growing complexity and sophistication of cyber-attacks are posing serious challenges to traditional cybersecurity methods. As a result, machine learning (ML) techniques have emerged as a promising solution for detecting and preventing these attacks.
Aim: This research paper offers an extensive examination of diverse machine learning algorithms that have the potential to enhance the intelligence and overall functionality of applications.
Methods: The main focus of this study is to present the core principles of distinct machine learning methods and demonstrate their versatile applications in various practical fields such as cybersecurity systems, smart cities, healthcare, e-commerce, and agriculture. By exploring these applications, this paper contributes to the understanding of how machine learning techniques can be effectively employed across different domains. The article then explores the current and future prospects of ML in cybersecurity.
Results: This paper highlights the growing importance of ML in cybersecurity and the increasing demand for skilled professionals who can develop and implement ML-based solutions.
Conclusion: Overall, the present article presents a thorough examination of the role of machine learning (ML) in cybersecurity, as well as its current and future prospects. It can be a valuable source of information for researchers, who seek to grasp the potential of ML in enhancing cybersecurity.

Keywords:

Cybersecurity dataset,
Data analysis,
Cybercrime,
ML Algorithms,
Threats,
Security,

How to Cite

1.
Haque MA, Ahmad S, Sonal D, M. Abdeljaber HA, Mishra B, Eljialy A, Alanazi S, Nazeer J. Achieving Organizational Effectiveness through Machine Learning Based Approaches for Malware Analysis and Detection. Data and Metadata [Internet]. 2023 Dec. 29 [cited 2024 May 17];2:139. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/139

Copyright Notice

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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Vol. 2 (2023)
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