Hybrid Learning Approach for E-mail Spam Detection and Classification

Shajahan, Rimitha; Lekshmy, P. L.

doi:10.1007/978-3-031-18497-0_57

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…In order to forecast whether a given URL represents a phishing link, the researchers provide a unique deep learning architecture called Texception [20]. This is an inventive method of doing so.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Phishing Detection: A Novel Hybrid Deep Learning Framework for Cybercrime Forensics

Alsubaei,

Almazroi,

Ayub

2024

IEEE Access

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Protecting against interference is essential at a time when wireless communications are essential for sending large amounts of data. Our research presents a novel deep learning technique, the ResNeXt method and embedded Gated Recurrent Unit (GRU) model (RNT), rigorously developed for realtime phishing attack detection. Focused on countering the escalating threat of phishing assaults and bolstering digital forensics, our systematic approach involves SMOTE for managing data imbalance during initial data processing. The model's discriminative capability is improved, particularly in the feature extraction process, when autoencoders and ResNet (EARN) are integrated with feature engineering. The ensemble technique of feature extraction reveals crucial data patterns. At the core of our AI categorization is the RNT model, optimized using hyperparameters through the Jaya optimization method (RNT-J). Rigorously tested on real phishing attack datasets, our AI model consistently outperforms state-of-the-art algorithms by a substantial margin of 11% to 19% while maintaining exceptional computing efficiency. Furthermore, our model achieves 98% accuracy, low false positive/false negative values, and a statistical execution time with a mean of 36.99s, median of 35.99s, minimum of 34.99s, maximum of 41.99s, and a standard deviation of 1.10s. Moreover, it demonstrates superior accuracy with SMOTE (98%) and without SMOTE (83%) compared to other algorithms. This state-of-the-art AI study, which focuses on digital forensics, offers enhanced security and optimized productivity for businesses and industries, signifying a breakthrough in the continuing battle against phishing attempts. Through strengthening protection against interference in wireless communication, our AI research strives to amplify data accessibility, resilience, and trustworthiness in the face of cybersecurity threats within the organizational context.

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Section: Related Workmentioning

confidence: 99%

Enhancing Phishing Detection: A Novel Hybrid Deep Learning Framework for Cybercrime Forensics

Alsubaei,

Almazroi,

Ayub

2024

IEEE Access

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“…Hackers may be able to access social network programs and languages that are secured, leading to security breaches. When private information is accessed without authorization due to security breaches, privacy concerns occur [3].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Mechanism of Modified Sand Cat Swarm Optimization and DCNN For Detection of Spam In Online Social Media Network

Nicholas,

Nirmalrani

2024

Preprint

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In recent years, social media has become an indispensable part of people's daily lives. The use of social media networks, particularly WhatsApp, Facebook, and email, has significantly surged worldwide due to their widespread popularity. These platforms serve as potent tools for users to share information and opinions, post photos and videos, and disseminate news. The growing popularity and importance of social media platforms attract spammers to a greater extent. Detecting social network spam is particularly challenging due to its brief content and constantly streaming nature. Many spam detection methods for social media rely on machine learning, utilizing a variety of classification and clustering algorithms. Deep learning techniques have achieved significant success globally in solving diverse problems across different domains. This research aims to develop a robust spam detection model for social media networks using deep CNN methods and to evaluate the effectiveness of the models using Modified Sand Cat Swarm (MCSO) based optimization models. N-gram and Bag of Words features are utilized for ensemble feature extraction and selection to rank and analyze the significance of features in spam detection. The performance of the proposed models is compared with other deep learning algorithms. A comparative analysis of spam detection models reveals that the random forest classifier outperforms when trained with randomly chosen data of varying sizes. A DCNN-based MSCSO successfully validates 99.02% of spam on social media networks, as confirmed by the classification. These unconventional pairings reinforce the original premise behind these classification findings.

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“…These studies mainly focused on URL identifiers, with a few exceptions in searching for email text [19]. Numerous previous studies have concentrated on email text or URL data [20][21][22][23]. It opens the door to investigating and creating a comprehensive model that combines numerous methods on different parts of phishing scams, such as attachments, URLs, senders, images, and body text to detect phishing attempts effectively.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators

Aldakheel

Zakariah

Gashgari

et al. 2023

Sensors

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Organizations and individuals worldwide are becoming increasingly vulnerable to cyberattacks as phishing continues to grow and the number of phishing websites grows. As a result, improved cyber defense necessitates more effective phishing detection (PD). In this paper, we introduce a novel method for detecting phishing sites with high accuracy. Our approach utilizes a Convolution Neural Network (CNN)-based model for precise classification that effectively distinguishes legitimate websites from phishing websites. We evaluate the performance of our model on the PhishTank dataset, which is a widely used dataset for detecting phishing websites based solely on Uniform Resource Locators (URL) features. Our approach presents a unique contribution to the field of phishing detection by achieving high accuracy rates and outperforming previous state-of-the-art models. Experiment results revealed that our proposed method performs well in terms of accuracy and its false-positive rate. We created a real data set by crawling 10,000 phishing URLs from PhishTank and 10,000 legitimate websites and then ran experiments using standard evaluation metrics on the data sets. This approach is founded on integrated and deep learning (DL). The CNN-based model can distinguish phishing websites from legitimate websites with a high degree of accuracy. When binary-categorical loss and the Adam optimizer are used, the accuracy of the k-nearest neighbors (KNN), Natural Language Processing (NLP), Recurrent Neural Network (RNN), and Random Forest (RF) models is 87%, 97.98%, 97.4% and 94.26%, respectively, in contrast to previous publications. Our model outperformed previous works due to several factors, including the use of more layers and larger training sizes, and the extraction of additional features from the PhishTank dataset. Specifically, our proposed model comprises seven layers, starting with the input layer and progressing to the seventh, which incorporates a layer with pooling, convolutional, linear 1 and 2, and linear six layers as the output layers. These design choices contribute to the high accuracy of our model, which achieved a 98.77% accuracy rate.

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Hybrid Learning Approach for E-mail Spam Detection and Classification

Cited by 3 publications

References 9 publications

Enhancing Phishing Detection: A Novel Hybrid Deep Learning Framework for Cybercrime Forensics

Enhancing Phishing Detection: A Novel Hybrid Deep Learning Framework for Cybercrime Forensics

An Efficient Mechanism of Modified Sand Cat Swarm Optimization and DCNN For Detection of Spam In Online Social Media Network

A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators

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