Enhancing Cloud Security with Machine Learning-Based Anomaly Detection - Aisha Mohammed, Theresa Ojevwe Akroh, Chinwe Sheila Nwachukwu

Aug 4, 2026

Upload by Levi

With cloud computing’s rapid adoption across industries, ensuring robust security measures is a critical priority. Traditional defenses such as rule-based intrusion detection and signature methods often miss novel, sophisticated threats in real time. This paper investigates ML-based anomaly detection to identify and mitigate cloud security risks, comparing supervised, unsupervised, and reinforcement learning and evaluating their ability to spot deviations in network traffic, user activity, and system logs, while addressing data quality, computational overhead, interpretability, adversarial risks, and privacy concerns.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Introduction

Background on Cloud Security Challenges

Increasing Cyber Threats in Cloud Environments

Limitations of Traditional Security Measures

Role of Machine Learning in Cybersecurity

How ML Enhances Threat Detection and Response

AMERICAN Journal of Engineering, Mechanics and Architecture Volume 3, Issue 3, 2025 ISSN (E): 2993-2637 Enhancing Cloud Security with Machine Learning-Based Anomaly Detection Aisha Mohammed, Theresa Ojevwe Akroh, Chinwe Sheila Nwachukwu Doctorate in Cloud Computing and Cybersecurity, Researcher at the University of Derby, United Kingdom Abstract: With the increasing adoption of cloud computing across industries, ensuring robust security measures has become a critical priority. Traditional security approaches, such as rulebased intrusion detection systems and signature-based methods, often fail to detect novel and sophisticated cyber threats in real-time. As a result, the integration of machine learning (ML) for anomaly detection has emerged as a powerful solution for enhancing cloud security. This paper explores the implementation of ML-based anomaly detection techniques to identify and mitigate security threats in cloud environments. Specifically, it examines various ML approaches, including supervised, unsupervised, and reinforcement learning, and their effectiveness in detecting deviations from normal system behavior. By analyzing patterns in network traffic, user activity, and system logs, ML models can identify potential threats such as insider attacks, unauthorized access, malware infiltration, and distributed denial-of-service (DDoS) attacks. Furthermore, the paper discusses the challenges associated with ML-driven security solutions, including the need for high-quality training data, computational overhead, model interpretability, and potential adversarial attacks on learning algorithms. Additionally, privacy concerns related to data collection and processing in cloud environments are highlighted. Despite these challenges, ML-based anomaly detection offers significant advantages over conventional security mechanisms by enabling adaptive, scalable, and proactive threat detection. Through an in-depth review of recent advancements and case studies, this research underscores the transformative potential of ML in strengthening cloud security. By leveraging artificial intelligence and data-driven anomaly detection techniques, cloud service providers can improve their security posture, reduce false positives in threat detection, and enhance real-time incident response. Ultimately, this study advocates for the integration of ML-based anomaly detection asa fundamental component of modern cloud security frameworks to ensure the resilience and integrity of cloud-based systems against evolving cyber threats. I. Introduction Background on Cloud Security Challenges Cloud computing has revolutionized the way businesses and individuals store, process, and access data, offering scalability, flexibility, and cost-efficiency. However, as cloud adoption continues to grow, so do the security challenges associated with it. Organizations relying on cloud services face a range of cyber threats, including unauthorized access, data breaches, distributed denial-of-service (DDoS) attacks, insider threats, and advanced persistent threats (APTs) . The dynamic nature of cloud environments, characterized by multi-tenancy, remote access, and resource virtualization, further complicates security enforcement. Increasing Cyber Threats in Cloud Environments The rapid expansion of cloud services has attracted cybercriminals who exploit vulnerabilities in cloud infrastructure, applications, and user authentication mechanisms. Cyberattacks targeting cloud platforms have become more sophisticated, leveraging techniques such as phishing, ransomware, and zero-day exploits. Additionally, the rise of hybrid and multi-cloud deployments has introduced new security challenges, including inconsistent security policies, misconfigurations, and insecure application programming interfaces (APIs) . The increasing number of connected devices and remote work arrangements has further expanded the attack surface, making cloud environments prime targets

FAQs

Why do traditional cloud security measures struggle against modern threats?

Rule-based intrusion detection and signature-based methods depend on predefined rules and known attack patterns, which makes them ineffective against novel, evolving threats. They also tend to produce high false positives and delayed detection in large, complex cloud environments.

What types of machine learning approaches are discussed for anomaly detection?

The paper examines supervised learning, unsupervised learning, and reinforcement learning. These approaches are used to classify threats and detect deviations from normal system behavior.

What challenges come with ML-driven security solutions in cloud environments?

Key challenges include the need for high-quality training data, computational overhead, model interpretability, and vulnerability to adversarial attacks on learning algorithms. The paper also highlights privacy concerns related to data collection and processing.

Aug 4, 2026

Upload by Levi