Thwarting Cyber Crime and Phishing Attacks with Machine Learning: A Study

Arshey, M; Viji, K. S. Angel

doi:10.1109/icaccs51430.2021.9441925

Cited by 15 publications

(4 citation statements)

References 3 publications

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“…In their comparative study, Bhowmik, Chandana, and Rudra et al [13] state that the decision tree is the most accurate model for detecting phishing attacks compared to the other models based on the naïve Bayes classifier. Arshey and Viji [14] published their work on thwarting security issues in blockchain environments with machine-learning solutions. Their work was more theoretical but had many practical applications, such as COVID-19 data storage and cloud technology.…”

Section: Traditional Machine-learning Approachesmentioning

confidence: 99%

Machine-Learning Techniques for Predicting Phishing Attacks in Blockchain Networks: A Comparative Study

et al. 2023

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Security in the blockchain has become a topic of concern because of the recent developments in the field. One of the most common cyberattacks is the so-called phishing attack, wherein the attacker tricks the miner into adding a malicious block to the chain under genuine conditions to avoid detection and potentially destroy the entire blockchain. The current attempts at detection include the consensus protocol; however, it fails when a genuine miner tries to add a new block to the blockchain. Zero-trust policies have started making the rounds in the field as they ensure the complete detection of phishing attempts; however, they are still in the process of deployment, which may take a significant amount of time. A more accurate measure of phishing detection involves machine-learning models that use specific features to automate the entire process of classifying an attempt as either a phishing attempt or a safe attempt. This paper highlights several models that may give safe results and help eradicate blockchain phishing attempts.

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Section: Traditional Machine-learning Approachesmentioning

confidence: 99%

Machine-Learning Techniques for Predicting Phishing Attacks in Blockchain Networks: A Comparative Study

et al. 2023

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“…As anticipated, the majority of technical countermeasures to phishing attacks is based on machine learning. As such, the main goal of the work discussed in [ 108 ] was to identify and propose ways in which machine learning techniques could be deployed for the detection of diverse types of cyber‐crimes, such as phishing, identify theft, hacking, distributed denial of service, email bombing, and digital stalking. Authors discussed different types of machine learning‐based implementations in cyber‐crime mitigation, including the discussion of ways in which machine learning could contribute to phishing detection, with particular reference to the detection of phishing emails via analysis of the headers and body of the emails.…”

Section: Countermeasuresmentioning

confidence: 99%

“…Authors discussed different types of machine learning-based implementations in cyber-crime mitigation, including the discussion of ways in which machine learning could contribute to phishing detection, with particular reference to the detection of phishing emails via analysis of the headers and body of the emails. The techniques proposed in [108] are effectively used in the following systems. In [86], the authors introduced a novel AI-based anomalous email detector -HOLMES -that can effectively tackle the challenge of anomalous email detection.…”

Section: Work Proposing Technical Solutionsmentioning

confidence: 99%

The COVID‐19 scamdemic: A survey of phishing attacks and their countermeasures during COVID‐19

Al‐Qahtani

Cresci

2022

IET Information Security

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The COVID-19 pandemic coincided with an equally-threatening scamdemic: a global epidemic of scams and frauds. The unprecedented cybersecurity concerns emerged during the pandemic sparked a torrent of research to investigate cyber-attacks and to propose solutions and countermeasures. Within the scamdemic, phishing was by far the most frequent type of attack. This survey paper reviews, summarises, compares and critically discusses 54 scientific studies and many reports by governmental bodies, security firms and the grey literature that investigated phishing attacks during COVID-19, or that proposed countermeasures against them. Our analysis identifies the main characteristics of the attacks and the main scientific trends for defending against them, thus highlighting current scientific challenges and promising avenues for future research and experimentation.

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“…Overall, cybercrime remains a global issue, and researchers and experts are constantly proposing new ideas and strategies to combat this evolving threat. The widespread adoption of technology and the growth of the digital era have created both opportunities and risks, and it is crucial to stay vigilant and employ effective cybersecurity measures to protect against cyber-attacks [2].…”

Section: Introductionmentioning

confidence: 99%

A Security Model for the Classification of Suspicious Data Using Machine Learning Techniques

Boussi Grace Odette

2023

IJRITCC

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Cybercrime first emerged in 1981 and gained significant attention in the 20th century. The proliferation of technology and our increasing reliance on the internet have been major factors contributing to the growth of cybercrime. Different countries face varying types and levels of cyber-attacks, with developing countries often dealing with different types of attacks compared to developed countries. The response to cybercrime is usually based on the resources and technological capabilities available in each country. For example, sophisticated attacks involving machine learning may not be common in countries with limited technological advancements. Despite the variations in technology and resources, cybercrime remains a costly issue worldwide, projected to reach around 8 trillion by 2023. Preventing and combating cybercrime has become crucial in our society. Machine learning techniques, such as convolutional neural networks (CNN), recurrent neural networks (RNN), and more, have gained popularity in the fight against cybercrime. Researchers and authors have made significant contributions in protecting and predicting cybercrime. Nowadays, many corporations implement cyber defense strategies based on machine learning to safeguard their data. In this study, we utilized five different machine learning algorithms, including CNN, LSTM, RNN, GRU, and MLP DNN, to address cybercrime. The models were trained and tested using the InSDN public dataset. Each model provided different levels of trained and test accuracy percentages.

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Thwarting Cyber Crime and Phishing Attacks with Machine Learning: A Study

Cited by 15 publications

References 3 publications

Machine-Learning Techniques for Predicting Phishing Attacks in Blockchain Networks: A Comparative Study

Machine-Learning Techniques for Predicting Phishing Attacks in Blockchain Networks: A Comparative Study

The COVID‐19 scamdemic: A survey of phishing attacks and their countermeasures during COVID‐19

A Security Model for the Classification of Suspicious Data Using Machine Learning Techniques

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