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crossrefFuture Internet2023-08-11Cited by 8

A Survey on Pump and Dump Detection in the Cryptocurrency Market Using Machine Learning

Mohammad Javad Rajaei, Qusay H. Mahmoud

The popularity of cryptocurrencies has skyrocketed in recent years, with blockchain technologies enabling the development of new digital assets. However, along with their advantages, such as lower transaction costs, increased security, and transactional transparency, cryptocurrencies have also become susceptible to various forms of market manipulation. The pump and dump (P&D) scheme is of significant concern among these manipulation tactics. Despite the growing awareness of P&D activities in cryptocurrency markets, a comprehensive survey is needed to explore the detection methods. This paper aims to fill this gap by reviewing the literature on P&D detection in the cryptocurrency world. This survey provides valuable insights into detecting and classifying P&D schemes in the cryptocurrency market by analyzing the selected studies, including their definitions and the taxonomies of P&D schemes, the methodologies employed, their strengths and weaknesses, and the proposed solutions. Presented here are insights that can guide future research in this field and offer practical approaches to combating P&D manipulations in cryptocurrency trading.

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crossrefFuture Internet2026-01-09Cited by 4

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The rapid integration of connectivity and automation in modern vehicles has significantly expanded the attack surface of in-vehicle networks, particularly the Controller Area Network (CAN) bus, which lacks native security mechanisms. This study investigates machine learning-based…

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crossrefFuture Internet2025-05-31Cited by 3

Hybrid Model for Novel Attack Detection Using a Cluster-Based Machine Learning Classification Approach for the Internet of Things (IoT)

Naveed Ahmed, Md Asri Ngadi, Abdulaleem Ali Almazroi, Nouf Atiahallah Alghanmi

To combat the growing danger of zero-day attacks on IoT networks, this study introduces a Cluster-Based Classification (CBC) method. Security vulnerabilities have become more apparent with the growth of IoT devices, calling for new approaches to identify unique threats quickly. T…

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crossrefFuture Internet2023-08-21Cited by 14

Detection of Man-in-the-Middle (MitM) Cyber-Attacks in Oil and Gas Process Control Networks Using Machine Learning Algorithms

Ugochukwu Onyekachi Obonna, Felix Kelechi Opara, Christian Chidiebere Mbaocha, Jude-Kennedy Chibuzo Obichere, Isdore Onyema Akwukwaegbu, Miriam Mmesoma Amaefule, et al.

Recently, the process control network (PCN) of oil and gas installation has been subjected to amorphous cyber-attacks. Examples include the denial-of-service (DoS), distributed denial-of-service (DDoS), and man-in-the-middle (MitM) attacks, and this may have largely been caused b…

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crossrefFuture Internet2023-07-26Cited by 43

A Novel Approach for Fraud Detection in Blockchain-Based Healthcare Networks Using Machine Learning

Mohammed A. Mohammed, Manel Boujelben, Mohamed Abid

Recently, the advent of blockchain (BC) has sparked a digital revolution in different fields, such as finance, healthcare, and supply chain. It is used by smart healthcare systems to provide transparency and control for personal medical records. However, BC and healthcare integra…

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crossrefFuture Internet2026-04-27Cited by 1

Enhancing Network Intrusion Detection with Quantum Machine Learning: A Comprehensive Survey of Methods, Metrics, and Applications

Antanios Kaissar, Ali Bou Nassif, Ahmed Bouridane

Quantum computing introduces new computational capabilities that can support advanced cybersecurity solutions when combined with machine learning. In recent years, quantum machine learning (QML) has emerged as a promising approach for enhancing network intrusion detection systems…

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crossrefFuture Internet2025-04-22

Using Machine Learning to Detect Vault (Anti-Forensic) Apps

Michael N. Johnstone, Wencheng Yang, Mohiuddin Ahmed

Content hiding, or vault applications (apps), are designed with a secondary, often concealed purpose, such as encrypting and storing files. While these apps may serve legitimate functions, they unequivocally present significant challenges for law enforcement. Conventional methods…

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