An intelligent cyber security phishing detection system using deep learning techniques

Mughaid, Ala; AlZu’bi, Shadi; Hnaif, Adnan A.; Taamneh, Salah; Alnajjar, Asma; Elsoud, Esraa Abu

doi:10.1007/s10586-022-03604-4

Cited by 63 publications

(18 citation statements)

References 33 publications

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“…Multiple decision trees are used in Random Forest (RF), an ensemble learning technique, to increase classification accuracy. Each tree is trained using a separate portion of the training data, and the trees' predictions are combined to get the final prediction [57]. Due to its capacity to manage high-dimensional feature spaces and record complex decision boundaries, RF has been widely used to detect phishing.…”

Section: Theoretical Reviewmentioning

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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Section: Theoretical Reviewmentioning

confidence: 99%

Phishing Detection Using Machine Learning: A Model Development and Integration

Adeyemi Onih

2024

IJSMR

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“…In their research, Page et al 121 used nine web‐based features and six domain‐based features in their ML method for detecting phishing domains. Last few years, several works have been proposed using ML 122‐129 …”

Section: Ml‐based Solutions For Osn Platformmentioning

confidence: 99%

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

Bhattacharya

Roy

Chattopadhyay

et al. 2022

Security and Privacy

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Over the past few years, online social networks (OSNs) have become an inseparable part of people's daily lives. Instead of being passive readers, people are now enjoying their role as content contributors. OSN has permitted its users to share their information including the multimedia content. OSN users can express themselves in virtual communities by providing their opinions and interacting with others. As a consequence, the privacy and security threats in OSNs have emerged as a major concern to the research and business world. In the recent past, a number of survey works have been conducted to discuss different security and privacy threats in OSNs. However, till date, no survey work has been conducted that aims to classify and analyze various machine learning (ML)‐based solutions adapted for the security defense of OSNs. In this survey article, we present a detailed taxonomy with a classification of various works done on various security attacks in OSNs. We then review and summarize the existing state of art survey works on OSN security, and indicate the merits and limitations of these survey works. Next, we review all recent works that aim to provide ML‐based solutions toward defense of security attacks on OSNs. Finally, we discuss the future road‐map on OSN security and provide a comprehensive analysis on the open research issues with feasible measurements and possible solutions.

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“…Sustainable solutions have been employed efficiently in many applications including cybersecurity [19,20,21,22], many previous researches have been conducted for protecting users in the Cyber [23,24,25,26].…”

Section: Related Workmentioning

confidence: 99%

SCADA Authentication Identification Over The Next Generation CyberNet

Mughaid

AlZu’bi

AlZioud

et al. 2022

Preprint

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SCADA (supervisory control and data acquisition) system is an advanced control system used in industrial and service sectors such as oil and gas, water treatment, nuclear energy, and electrical power generation to control and monitor the manufacturing or servicing process and maintain the process within predetermined values in order to induce desirable outcomes. The SCADA architecture employs several protocols, including Modbus/RTU, Modbus TCP/IP, DNP3 and IEC60870. This paper will concentrate on the most widely used protocol in SCADA systems, Modbus TCP/IP, due to its widespread use, simplicity, and robustness. SCADA systems are vulnerable to cyber-attacks because Modbus/TCP lacks security measures for access control, authentication, and confidentiality. The paper contains valuable contribution by introducing an authentication technique to check the validity of messages in which the authorized users only pass this check, thereby adding a security measure to the SCADA system in terms of cyber-attacks.

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An intelligent cyber security phishing detection system using deep learning techniques

Cited by 63 publications

References 33 publications

Phishing Detection Using Machine Learning: A Model Development and Integration

Phishing Detection Using Machine Learning: A Model Development and Integration

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

SCADA Authentication Identification Over The Next Generation CyberNet

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