IntAnti-Phish: An Intelligent Anti-Phishing Framework Using Backpropagation Neural Network

Rahman, Sheikh Shah Mohammad Motiur; Gope, Lakshman; Islam, Takia; Alazab, Mamoun

doi:10.1007/978-3-030-57024-8_9

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…However, it needs almost 150 layers [20]. There are several guidelines for determining the number of layers, including two or fewer layers for basic datasets and additional layers for computer vision, time series, or complicated datasets [4,39,40].…”

Section: Deep Learning-based Detectionmentioning

confidence: 99%

“…The nuanced landscape of phishing techniques showcasing symmetry and asymmetry includes algorithms, domain spoofing, HTTPS phishing, SMS phishing, link handling, email phishing, and pop-ups. Attributes such as prefixes, suffixes, subdomains, IP addresses, URL lengths, '@' symbol, spear phishing, dual-slash attributes, ports, HTTPS tokens, request URLs, URL anchors, tag links, and domain age contribute to the multifaceted nature of phishing attacks [4]. Phishing perpetrators adeptly mimic legitimate websites, particularly those related to online banking and e-commerce.…”

Section: Introductionmentioning

confidence: 99%

“…While this database offers organized data categorization patterns, asymmetries arise when using categorization algorithms or machine learning for URL data, necessitating additional symmetrical URL management techniques [17]. Standard techniques like blacklisting, regular expression, and signature matching, although employed to identify phishing attempts, exhibit asymmetry by falling short in detecting unfamiliar URLs [4]. Continuous updating of database signatures to detect unexpected patterns in malicious URLs underscores the need for applying symmetrical machine learning-based research, particularly with deep learning models, for robust and symmetrical identification of malicious URLs [18].…”

Section: Introductionmentioning

confidence: 99%

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AntiPhishStack: LSTM-Based Stacked Generalization Model for Optimized Phishing URL Detection

Aslam,

Manzoor

et al. 2024

Symmetry

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The escalating reliance on revolutionary online web services has introduced heightened security risks, with persistent challenges posed by phishing despite extensive security measures. Traditional phishing systems, reliant on machine learning and manual features, struggle with evolving tactics. Recent advances in deep learning offer promising avenues for tackling novel phishing challenges and malicious URLs. This paper introduces a two-phase stack generalized model named AntiPhishStack, designed to detect phishing sites. The model leverages the learning of URLs and character-level TF-IDF features symmetrically, enhancing its ability to combat emerging phishing threats. In Phase I, features are trained on a base machine learning classifier, employing K-fold cross-validation for robust mean prediction. Phase II employs a two-layered stacked-based LSTM network with five adaptive optimizers for dynamic compilation, ensuring premier prediction on these features. Additionally, the symmetrical predictions from both phases are optimized and integrated to train a meta-XGBoost classifier, contributing to a final robust prediction. The significance of this work lies in advancing phishing detection with AntiPhishStack, operating without prior phishing-specific feature knowledge. Experimental validation on two benchmark datasets, comprising benign and phishing or malicious URLs, demonstrates the model’s exceptional performance, achieving a notable 96.04% accuracy compared to existing studies. This research adds value to the ongoing discourse on symmetry and asymmetry in information security and provides a forward-thinking solution for enhancing network security in the face of evolving cyber threats.

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Section: Deep Learning-based Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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AntiPhishStack: LSTM-Based Stacked Generalization Model for Optimized Phishing URL Detection

Aslam,

Manzoor

et al. 2024

Symmetry

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“…Detection Systems do not provide efficient detection, especially when the attack patterns of possible attacks are not equal for every attack target. For this reason, the study and deployment of new emerging technologies such as big data and Machine Learning techniques are being introduced in cybersecurity technologies[5]. The detection and prevention of possible intruders into a system is a recurrent security problem.…”

mentioning

confidence: 99%

Optimization of intrusion detection systems based on machine learning for cybersecurity by boosting feature engineering and model selection techniques

Novo¹

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AntiPhishTuner: Multi-level Approaches Focusing on Optimization by Parameters Tuning in Phishing URLs Detection

Muntasir

Rahman

Jahan

et al. 2021

Studies in Big Data

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IntAnti-Phish: An Intelligent Anti-Phishing Framework Using Backpropagation Neural Network

Cited by 7 publications

References 20 publications

AntiPhishStack: LSTM-Based Stacked Generalization Model for Optimized Phishing URL Detection

AntiPhishStack: LSTM-Based Stacked Generalization Model for Optimized Phishing URL Detection

Optimization of intrusion detection systems based on machine learning for cybersecurity by boosting feature engineering and model selection techniques

AntiPhishTuner: Multi-level Approaches Focusing on Optimization by Parameters Tuning in Phishing URLs Detection

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