RETRACTED: Phishing attack detection using Machine Learning

S, Sundara Pandiyan; Selvaraj, Prabha; Burugari, Vijay Kumar; Benadit, Julian; Kanmani, P

doi:10.1016/j.measen.2022.100476

Cited by 10 publications

(4 citation statements)

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“…In this research, we proposed a multi-layer adaptive framework that uses the computer vision capability of Optical Character Recognition (OCR) to read images on live phishing sites to text, and synthesize speech from uploaded deep-fake videos, while using Random Forest, and LSTM network, along with web scrapped text at various predictions layered of the framework to significantly improve the detection rate and performance of AI-based models for phishing detection. Considering the fact that existing AI-based phishing detection techniques, frameworks, and approaches can only detect text-based [32], [33], [2], [28] or URL-based phishing [27], [32], [34], [35] sites which leads to their vulnerability and inability to detect image-based, or video-based phishing sites, the proposed framework is able to overcome limitations in existing approaches, significantly improve phishing attack detection, and successfully detect complex phishing webpages with multi-dimentional deep-fake videos, images, and texts.…”

Section: Discussionmentioning

confidence: 99%

“…To stretch and validate our multilayered adaptive framework for its effectiveness in the detection of phishing sites containing any of (Text, videos, and images), or a combination of any, or all of the 3. It is worth remembering that all existing AI or machine learning-based phishing detection techniques and frameworks can only detect text-based [32], [33], [2], [28] or URL-based [27], [32], [34], [35] phishing sites leading to their vulnerabilities to; -phishing sites with friendly URL -phishing site on hacked legitimate domain name server (DNS) -Image-only phishing site -video-only phishing site or, combination of any of them in any order. To validate both the effectiveness and adaptability of our proposed framework in overcoming such limitations, we created 4 categories of phishing sites and uploaded them to a secure server with a compromised DNS on a friendly URL; the first was a text-only phishing site, image-only phishing site, video-only phishing site, and a phishing site combining all the features.…”

Section: Framework Adaptability and Performance Evaluationmentioning

confidence: 99%

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Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework

Ige,

Kiekintveld,

Piplai

2024

Preprint

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The ever-evolving ways attacker continues to im prove their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an adaptable framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection.

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Section: Discussionmentioning

confidence: 99%

Section: Framework Adaptability and Performance Evaluationmentioning

confidence: 99%

Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework

Ige,

Kiekintveld,

Piplai

2024

Preprint

View full text Add to dashboard Cite

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“…A study by Pandiyan S et al [16] reported accuracy 85% with Light GBM. Using UCI dataset, Alnemari & Alshammari [17] compared accuracy of four models for preventing phishing attacks.…”

Section: Related Workmentioning

confidence: 96%

Machine Learning Approach for Phishing Attack Detection

Choudhary,

Mhapankar,

Bhddha

et al. 2023

JAIT

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Phishing is the easiest method for gathering sensitive information from unwary people. Phishers seek to get private data including passwords, login information, and bank account details. Cyber security experts are actively seeking for trustworthy and effective ways to identify phishing websites. In order to distinguish between legal and phishing URLs, we used machine learning (ML) technology. In this research work using ML technology extraction and analysis of both types of URLs was performed. Extreme Gradient Boosting (XGBoost), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) were used to identify phishing websites. The goal was to identify phishing URLs and determine the most effective ML technique by comparing the accuracy rates of each algorithm. In this, proposed methodology two datasets were used. The accuracy of models was calculated on Phishtank and UCI dataset using kfold, feature selection and hyperparameter tuning method. Performance measures precision, recall and F1-score and Receiver Operating Characteristics (ROC) curve were calculated. RF provided an accuracy of 98.80% and 97.87% on the Phishtank dataset and UCI respectively. Highest precision, recall, F1-score value was 99% each and AUC-ROC value was 99.89% with Phishtank dataset. Validation with other researchers showed better results with proposed methodology. Therefore this methodology can be of help to identify phishing websites.

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“…Chinnasamy et al [25] developed an ML approach for effective phishing attack detection. Based on the input features such as Uniform Resource Locator (URL) and Web Traffic, the link was classified as phishing or non-phishing.…”

Section: Praveena Et Al [23] Developed a Deep Reinforcementmentioning

confidence: 99%

Healthcare Intrusion Detection using Hybrid Correlation-based Feature Selection-Bat Optimization Algorithm with Convolutional Neural Network

Bella,

Vasundra

2024

IJACSA

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Cloud computing is popular among users in various areas such as healthcare, banking, and education due to its low-cost services alongside increased reliability and efficiency. But, security is a significant problem in cloud-based systems due to the cloud services being accessed via the Internet by a variety of users. Therefore, the patient's health information needs to be kept confidential, secure, and accurate. Moreover, any change in actual patient data potentially results in errors during the diagnosis and treatment. In this research, the hybrid Correlation-based Feature Selection-Bat Optimization Algorithm (HCFS-BOA) based on the Convolutional Neural Network (CNN) model is proposed for intrusion detection to secure the entire network in the healthcare system. Initially, the data is obtained from the CIC-IDS2017, NSL-KDD datasets, after which min-max normalization is performed to normalize the acquired data. HCFS-BOA is employed in feature selection to examine the appropriate features that not only have significant correlations with the target variable, but also contribute to the optimal performance of intrusion detection in the healthcare system. Finally, CNN classification is performed to identify and classify intrusion detection accurately and effectively in the healthcare system. The existing methods namely, SafetyMed, Hybrid Intrusion Detection System (HIDS), and Blockchain-orchestrated Deep learning method for Secure Data Transmission in IoTenabled healthcare systems (BDSDT) are employed to evaluate the efficacy of HCFS-BOA-based CNN. The proposed HCFS-BOA-based CNN achieves a better accuracy of 99.45% when compared with the existing methods: SafetyMed, HIDS, and BDSDT.

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RETRACTED: Phishing attack detection using Machine Learning

Cited by 10 publications

References 0 publications

Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework

Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework

Machine Learning Approach for Phishing Attack Detection

Healthcare Intrusion Detection using Hybrid Correlation-based Feature Selection-Bat Optimization Algorithm with Convolutional Neural Network

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