Cyber-Phishing Website Detection Using Fuzzy Rule Interpolation

Almseidin, Mohammad; Alkasassbeh, Mouhammd; Alzubi, Maen; Al‐Sawwa, Jamil

doi:10.3390/cryptography6020024

Cited by 8 publications

(5 citation statements)

References 32 publications

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“…The proposed FRI-Incircle method achieved a detection rate of 97.58% and effectively reduced false alerts, smoothing the boundaries between normal and attack traffic due to its fuzzy nature. The results were competitive compared to other methods in the literature [56].…”

Section: Of 31mentioning

confidence: 78%

Sectrabank Model to Mitigate Computer Fraud in Electronic Operations through Banking Applications on Android Devices

Melendez,

Noriega,

Tiznado

et al. 2024

Preprint

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This research aims to introduce a model, SectraBank, designed to mitigate cyber fraud attacks, specifically targeting SIM Swapping and Fake App schemes in mobile banking users. A quantitative methodology with an experimental design was meticulously employed to examine the rise of cyber fraud within mobile banking. The study involved a sample of 100 professionals with advanced knowledge in Information and Communication Technologies (ICTs). Surveys were utilized to measure awareness of threats and perceptions of security before and after the implementation of the proposed model. SectraBank leverages unique user identifiers such as fingerprint authentication and the International Mobile Equipment Identity (IMEI) number, in addition to geolocation and One Time Passwords (OTP) for validating electronic transactions in mobile banking. The effectiveness of the proposed model was notable, achieving a 96.1% efficacy rate in mitigating Fake App attacks and a 90.2% success rate in countering SIM Swapping attacks. These findings underscore the potential of SectraBank as a robust solution to enhance cybersecurity measures in the realm of mobile banking.

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Section: Of 31mentioning

confidence: 78%

Sectrabank Model to Mitigate Computer Fraud in Electronic Operations through Banking Applications on Android Devices

Melendez,

Noriega,

Tiznado

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Zero-day attacks are newly identified vulnerabilities or attack strategies that have not yet been patched. [49] argued that phishing attackers might use zero-day vulnerabilities to perform sophisticated, covert attacks. Until the vulnerabilities are found and fixed, existing detection techniques might not be able to recognise and counteract such assaults.…”

Section: Django Web Securitymentioning

confidence: 99%

Phishing Detection Using Machine Learning: A Model Development and Integration

Adeyemi Onih

2024

IJSMR

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This study aimed to develop a robust machine learning-based phishing detection system using algorithms such as K-nearest neighbour (KNN), artificial neural network (ANN), and random forest (RF). It utilised datasets from Ariyadasa et al. (2021) and UNB (2016) to discern patterns distinguishing legitimate from phishing websites. Furthermore, an objective was to integrate the optimal model into a Django-based web application, facilitating real-time phishing detection. A comprehensive literature review on phishing detection techniques was also undertaken. Datasets chosen underwent rigorous pre-processing to address missing values and imbalance. Feature selection was achieved manually and automatically using mutual information classification. Three machine learning algorithms, RF, KNN, and ANN, were explored. Their hyper-parameters were optimised using GridSearchCV. Performance results highlighted RF's accuracy at 99.78%, KNN's at 99.67%, and ANN's at 99.11%. While RF and KNN models perfectly identified legitimate websites, ANN showcased an impeccable detection of phishing websites. The RF model, with the highest accuracy, was integrated into a Django application, providing a user interface for real-time phishing detection. All models exhibited high accuracy rates, demonstrating their efficacy in phishing detection. While RF was integrated into the web application for this study, the choice between models depends on specific user or business requirements and priorities. Feedback mechanisms within the Django application further promise refinement in future recommendations. The study provides a foundational step toward enhancing web safety through effective phishing detection.

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“…Almseidin et al [18] also applied fuzzy rule interpolation for detecting phishing website attacks. Their method can handle incomplete rule bases and smooth boundaries between normal and attack traffic.…”

Section: Anomaly-basedmentioning

confidence: 99%

Botnet Detection Techniques: A Comparative Study

Alauthman

2023

Preprint

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Botnets pose a grave cybersecurity threat, enabling widescale malicious activities through networks of compromised devices. Detecting botnets is challenging given their frequent use of evasion techniques like encryption. Traditional signature-based methods fail against modern botnets capable of zero-day attacks. This paper surveys recent advances applying machine learning for botnet detection based on analysis of network traffic payloads, flows, DNS data, and hybrid feature fusion. Core machine learning models include support vector machines, neural networks, random forests, and deep learning architectures, which extract patterns to separate benign and botnet behaviors automatically. Results demonstrate machine learning's capabilities in identifying heterogeneous botnets using artefacts in network streams. However, challenges remain around limited labeled data, real-time streaming, adversarial evasion, and model interpretability. Promising directions involve semi-supervised learning, adversarial training, scalable analytics, and explainable AI to address these gaps. Beyond the technical aspects, responsible development and deployment of botnet detection systems raise ethical considerations around privacy, transparency, and accountability. With diligent cross-disciplinary collaboration, machine learning promises enhanced, generalizable, and trustworthy techniques to combat the serious threat posed by continuously evolving botnets across the digital ecosystem.

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Cyber-Phishing Website Detection Using Fuzzy Rule Interpolation

Cited by 8 publications

References 32 publications

Sectrabank Model to Mitigate Computer Fraud in Electronic Operations through Banking Applications on Android Devices

Sectrabank Model to Mitigate Computer Fraud in Electronic Operations through Banking Applications on Android Devices

Phishing Detection Using Machine Learning: A Model Development and Integration

Botnet Detection Techniques: A Comparative Study

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