Subject Review - Enhancing Cyber Security through Machine Learning Model in Cloud Computing Environment

Aug 3, 2026

Cloud computing has become widely adopted for distributed processing, where shared software, resources, and data can be accessed on demand. Machine learning has gained prominence in cybersecurity within cloud environments, supporting tasks such as biometric authentication and malware or intrusion detection. At the same time, adversaries can target machine learning algorithms during both training and testing phases, reducing performance and creating safety breaches. This study reviews machine learning approaches to strengthen cybersecurity and compares the techniques while outlining directions for future improvements.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

INTRODUCTION

Benefits of Machine Learning (ML) in Cybersecurity (CS)

Machine learning (ML) Challenges for Cybersecurity (CS)

Subject Review: Enhancing Cyber Security through Machine Learning Model in Cloud Computing Environment Raniah Ali Mustafa1, Haitham Salman Chyad2, Dena Nadir George3 1,2,3Computer Science Department College of Education Mustansiriyah University Baghdad, Iraq ABSTRACT Cloud computing (CC) has become more and more popular in environments that use distributed computing. Cloudbased processing and data storage is becoming a global trend. Perhaps might put up the effect that Software as a Service (SaaS) has on numerous commercial applications and daily lives as disruptive technology. Cloud computing (CC) is an extension of Internet-based computing, where devices can access shared software, resources, and data as required. Machine learning (ML) has become a popular tool in the area of cybersecurity (CS) through the cloud computing environment in recent years. Examples of its applications include biometric-based user authentication and the detection of malware or intrusions. However, attacks on machine learning (ML) algorithms may occur in both the testing and training phases, which typically results in significant performance reductions and safety breaches. This article provides a comprehensive study on the use of machine learning (ML) algorithms to enhance cybersecurity (CS), as well as a comparison of most of the machine learning (ML) techniques mentioned in the work. The article presents future work of previous works that help researchers find ways to improve cybersecurity. Key Words: Attacks cyber (AC), Cyber Security (CS), Cloud computing (CC), Intrusion detection (ID), Machine learning (ML), Software as a Service (SaaS) . 1. INTRODUCTION Because cloud computing enables modern enterprises to access, store and analyse data accurately and efficiently[1], they have embraced it as a crucial component. But continued use of cloud computing also raises data security issues [2,3] . Therefore, to protect sensitive data from theft and unwanted access, cloud computing security must be improved. Experts have recognized machine learning (ML) as a potential approach to improve cloud computing security (CCS) [4] . Machine learning (ML) techniques are utilized to address data more efficiently and solve securityrelated problems [5] . Machine learning (ML) is the application of artificial intelligence (AI) which allows structures to learn and adapt naturally without the required for special customization [6] . Machine learning (ML) is concerned with expanding computer programs which can learn on their own at a suitable rate. In order to obtain patterns in data and subsequently make best decisions about the models presented, a learning strategy begins with perceptions or data, including models, guidance, or direct understanding [7] . In order to leverage security capabilities as attacks become increasingly sophisticated, the cybersecurity industry is leveraging cutting-edge technologies such as machine learning (ML) . Detecting even the smallest malware or ransomware attack activity before it infiltrates the system is the aim of machine learning (ML) in cybersecurity (CS) . 1.1 Benefits of Machine Learning (ML) in Cybersecurity (CS) [8,9] There are many benefits to using machine learning (ML) to address cybersecurity (CS)-related problems. These comprise: 1. Synthesize huge amounts of data rapidly: One of their biggest problems is the analysis requirement to quickly gather intelligence from across the attack surface, often produced much faster than their teams can manually assess. Machine learning can rapidly analyse large amounts (LA) of historical and dynamic intelligence, enabling teams to operationalize data from disparate sources in near real-time. 2. Expand expert intelligence by using long training cycles that let models learn for a changing population sample. This sample can include discoveries that have been classified by analysts or alerts that have been reviewed by analysts. Furthermore, it helps prevent

FAQs

Why does cloud computing increase the need for cybersecurity improvements?

Cloud computing enables enterprises to access, store, and analyze data efficiently, but continued use also raises data security issues. Protecting sensitive data from theft and unwanted access requires stronger cloud security measures.

What advantages does machine learning bring to cybersecurity in cloud environments?

Machine learning can rapidly analyze large volumes of intelligence for near real-time operations, expand expert intelligence via long training cycles, automate repetitive security tasks, and provide real-time intelligence to improve analysts’ productivity.

What challenges limit machine learning effectiveness for cybersecurity systems?

Key challenges include higher accuracy requirements, difficulty obtaining large amounts of especially labeled training data (such as malware samples), and the dynamic nature of cybersecurity where reliable data availability and coverage can change over time.

Aug 3, 2026

Upload by River Wang