A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN

Alshingiti, Zainab; Alaqel, Rabeah; Al‐Muhtadi, Jalal; Qazi, Emad-ul-Haq; Saleem, Kashif; Faheem, Muhammad Hamza

doi:10.3390/electronics12010232

Cited by 77 publications

(25 citation statements)

References 32 publications

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“…Their system achieved an accuracy classification value of 93.28%. Alshingiti et al [2] proposed a deep learning-based phishing detection system, employing three distinct DL models: LSTM, CNN, and an LSTM-CNN hybrid model. The evaluation of the system revealed that the CNN model outperformed the other models in terms of accuracy, reaching an accuracy of 99.2%.…”

Section: Deep Learningmentioning

confidence: 99%

A Permutaion Importance Based feature selection method and Deep Learning Model to Detect Phishing Websites

Zaimi,

Hafidi,

Mahnane

2024

Preprint

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Phishing attacks pose a significant and escalating threat to cybersecurity in recent times. This deceptive scam aims to trick naive users, luring them into visiting harmful websites and sharing sensitive information, including credentials, credit card numbers, and passwords. Consequently, cybercriminals exploit this data for their own gain. As the sophistication and maliciousness of phishing continue to evolve, researchers are earnestly developing multiple anti-phishing solutions in the literature. Among these solutions, those based on deep learning models have gained substantial attention in recent years. This study proposes an intelligent, deep-learning-based mechanism to detect phishing URLs. The proposed system is based on the permutation importance method (PIM) to select the most relevant URL features, and the Smote-Tomek link method to solve the problem of an unbalanced dataset. In addition, four DL models—CNN, LSTM, and two hybrid models (CNN-LSTM and LSTM-CNN)—are tested to identify the more suitable detection model for the phishing field. The experimental results demonstrate the successful functioning of the proposed phishing detection mechanism. It is observed that the proposed mechanism achieved an accuracy ranging from 93.36% to 96.43% without feature selection and data balance across two variants of datasets and different DL classifiers. It also achieved an accuracy ranging from 94.12% to 96.88% with feature selection and data balance. Finally, our phishing detection mechanism is implemented as web application to enhance its usability for web users.

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Section: Deep Learningmentioning

confidence: 99%

A Permutaion Importance Based feature selection method and Deep Learning Model to Detect Phishing Websites

Zaimi,

Hafidi,

Mahnane

2024

Preprint

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“…LeCun [23] popularized CNNs, revolutionizing image processing by eliminating the need for manual feature extraction. To efficiently handle visual information, they work by processing data directly from matrices or tensors, particularly in color imaging [60]. The basic structure of a CNN can be seen in Figure 3 [60].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…To efficiently handle visual information, they work by processing data directly from matrices or tensors, particularly in color imaging [60]. The basic structure of a CNN can be seen in Figure 3 [60]. In convolutional neural networks, each point in an input image is processed using a convolution operation with a filter, or kernel, which moves across the image by a defined number of pixels known as the "stride".…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

A Hybrid CNN-LSTM Approach for Precision Deepfake Image Detection Based on Transfer Learning

Al-Dulaimi,

Kurnaz

2024

Electronics

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The detection of deepfake images and videos is a critical concern in social communication due to the widespread utilization of deepfake techniques. The prevalence of these methods poses risks to trust and authenticity across various domains, emphasizing the importance of identifying fake faces for security and preventing socio-political issues. In the digital media era, deep learning outperforms traditional image processing methods in deepfake detection, underscoring its significance. This research introduces an innovative approach for detecting deepfake images by employing transfer learning in a hybrid architecture that combines convolutional neural networks (CNNs) and long short-term memory (LSTM). The hybrid CNN-LSTM model exhibits promise in combating deep fakes by merging the spatial awareness of CNNs with the temporal context understanding of LSTMs. Demonstrating effective performance on open-source datasets like “DFDC” and “Ciplab”, the proposed method achieves an impressive precision of 98.21%, indicating its capability to accurately identify deepfake images with a limited false-positive rate. The model’s error rate is 0.26%, emphasizing the challenges and intricacies inherent in deepfake detection tasks. These findings underscore the potential of hybrid deep learning techniques for addressing the urgent issue of deepfake image detection.

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“…In recent years, 1D convolutional neural networks (1D CNNs) in particular have shown an impressive performance for text classifcation [15,16]. A considerable amount of research has also been conducted for web security based on deep learning-based approaches [17,18]. Te preprocessing stage of HTTP requests is greatly simplifed at the character level in a number of deep learning-based approaches in the literature [2,19].…”

Section: Limits Of Prior Atrsmentioning

confidence: 99%

MC-MLDCNN: Multichannel Multilayer Dilated Convolutional Neural Networks for Web Attack Detection

Moarref,

Sandıkkaya

2023

Security and Communication Networks

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The explosive growth of web-based technology has led to an increase in sophisticated and complex attacks that target web applications. To protect against this growing threat, a reliable web attack detection methodology is essential. This research aims to provide a method that can detect web attacks accurately. A character-level multichannel multilayer dilated convolutional neural network (MC-MLDCNN) is proposed to identify web attacks accurately. The model receives the full text of HTTP requests as inputs. Character-level embedding is applied to embed HTTP requests to the model. Therefore, feature extraction is carried out automatically by the model, and no additional effort is required. This approach significantly simplifies the preprocessing phase. The methodology consists of multichannel dilated convolutional neural network blocks with various kernel sizes. Each channel involves several layers with exponentially increasing dilation sizes. Through the integration of multichannel and multilayer dilated convolutional neural networks, the model can efficiently capture the temporal relation and dependence of character granularity of HTTP requests at different scales and levels. As a result, the structure enables the model to easily capture dependencies over extended and long sequences of HTTP requests and consequently identify attacks accurately. The outcomes of the experiments carried out on the CSIC 2010 dataset show that the proposed model outperforms several state-of-the-art deep learning-based models in the literature and some traditional deep learning models by identifying web attacks with a precision score of 99.65%, a recall score of 98.80%, an F1 score of 99.22%, and an accuracy score of 99.36%. A useful web attack detection system must be able to balance accurate attack identification with minimizing false positives (identifying normal requests as attacks). The success of the model in recognizing normal requests is further evaluated to guarantee increased security without sacrificing web applications’ usability and availability.

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A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN

Cited by 77 publications

References 32 publications

A Permutaion Importance Based feature selection method and Deep Learning Model to Detect Phishing Websites

A Permutaion Importance Based feature selection method and Deep Learning Model to Detect Phishing Websites

A Hybrid CNN-LSTM Approach for Precision Deepfake Image Detection Based on Transfer Learning

MC-MLDCNN: Multichannel Multilayer Dilated Convolutional Neural Networks for Web Attack Detection

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