Phishing Attack Detection Research Articles

Enhancing Phishing Detection in Semantic Web Systems Using Optimized Deep Learning Models

Phishing detection in Semantic Web systems is crucial to safeguarding users from malicious attacks. In this context, this work presents a deep learning-based phishing attack detection model using MobileBERT for feature extraction and hyperparameter optimization using covariance matrix adaptation evolution strategy (CMA-ES). The model obtained a 95% classification accuracy. Important benchmarks like accuracy, recall, and F1-score show good ability to discriminate between phishing and legitimate emails. Applying CMA-ES, which improved detection accuracy, helps to verify the model even more. MobileBERT and CMA-ES together offer Semantic Web systems a fresh, efficient method of phishing detection.

Fishing for phishy messages: predicting phishing susceptibility through the lens of cyber-routine activities theory and heuristic-systematic model

Mobile phishing has emerged as one of the most severe cybercrime threats; thus, research must examine the factors affecting people’s likelihood of becoming instant messaging phishing targets. In this study, we draw on the cyber-routine activity theory (Cyber-RAT) and heuristic-systematic model (HSM) to predict Gen-Zers’ phishing susceptibility. Based on online survey data (n = 361), the proposed research model was validated via structural equation modeling conducted with SmartPLS 4. Findings indicate that engaging in online risky behavior (social media: instant messaging, vocational, and leisure activities) increases Gen-Zers’ exposure to phishers, increasing their likelihood of becoming instant messaging phishing targets. Phishing messages with a desirable or relevant topic (high message involvement) significantly impact Gen-Zers’ phishing susceptibility. Gen-Zers’ phishing susceptibility is also influenced by phishing messages with persuasive cues. While knowledge of the phishing domain does not directly influence Gen-Zers’ susceptibility to phishing attacks, it significantly motivated them to adopt effective online security management practices on social instant messaging platforms. This paper discusses how these findings implicate online users and inform agencies to promote knowledge for understanding and detecting phishing attacks to avoid victimization.

FDN-SA: Fuzzy deep neural-stacked autoencoder-based phishing attack detection in social engineering

Phishing attacks have emerged as a major social engineering threat that affects businesses, governments, and general internet users. This work proposes a social engineering phishing detection technique based on Deep Learning (DL). Initially, website data is taken from the dataset. Then, the features of Natural Language Processing (NLP) like bag of words, n-gram, hashtags, sentence length, Term Frequency- Inverse Document Frequency of records (TF-IDF), and all caps are extracted and then web feature extraction is carried out. Later, the feature fusion is done using the Neyman similarity with Deep Belief Network (DBN). Afterwards, oversampling is used for data augmentation to enhance the number of training samples. Lastly, the detection of phishing attacks is performed by employing the proposed Fuzzy Deep Neural-Stacked Autoencoder (FDN-SA). Here, the proposed FDN-SA is developed by combining a Deep Neural Network (DNN), and Deep Stacked Autoencoder (DSA). Further, the investigation of FDN-SA is accomplished based on the accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) and is observed to compute values of 0.920, 0.925, and 0.921, respectively.

A Comprehensive Review of Phishing Attack Detection Using Machine Learning Techniques

Phishing attacks have become a significant cybersecurity concern, affecting millions of users and organizations by stealing confidential information. The rise of machine learning (ML) techniques has provided innovative ways to detect and mitigate phishing attacks. This review paper explores various ML algorithms, including Decision Trees (DT), Random Forest (RF), and Principal Component Analysis (PCA), in detecting phishing attacks. Through a review of recent studies, it is evident that ML models such as RF can achieve high accuracy, up to 97%, in phishing detection. However, challenges such as evolving phishing strategies, data imbalance, and feature extraction remain critical issues. Future research directions should focus on deep learning models and real-time detection systems to enhance the robustness and effectiveness of phishing detection mechanisms

Real-time phishing URL detection framework using knowledge distilled ELECTRA

The rise of cyber threats, particularly URL-based phishing attacks, has tarnished the digital age despite its unparalleled access to information. These attacks often deceive users into disclosing confidential information by redirecting them to fraudulent websites. Existing browser-based methods, predominantly relying on blacklist approaches, have failed to effectively detect phishing attacks. To counteract this issue, we propose a novel system that integrates a deep learning model with a user-centric Chrome browser extension to detect and alert users about potential phishing URLs instantly. Our approach introduces a Knowledge Distilled ELECTRA model for URL detection and achieves remarkable performance metrics of 99.74% accuracy and a 99.43% F1-score on a diverse dataset of 450,176 URLs. Coupled with the browser extension, our system provides real-time feedback, empowering users to make informed decisions about the websites they visit. Additionally, we incorporate a user feedback loop for continuous model enhancement. This work sets a precedent by offering a seamless, robust, and efficient solution to mitigate phishing threats for internet users.

An optimal machine learning-based algorithm for detecting phishing attacks using URL information

An optimal machine learning-based algorithm for detecting phishing attacks using URL information

Impact Analysis of Filter and Wrapper-Based Feature Selection Techniques for Webpages Phishing Attacks Identification

Phishing, which involves fraudulently gaining access to sensitive assets of unsuspecting individuals through deceptive and malicious emails, is a major global threat to internet users. The proliferation of phishing sites and their operations is occurring at an alarming rate, raising significant concerns about how to forestall them. Numerous research efforts are underway to detect phishing attempts before they can compromise important information and cause damage. Compared to conventional methods, machine learning has proven highly effective at detecting phishing attacks by analyzing different features. This study analyzed the behaviors of seven classification data mining algorithms on optimal subset features selection using Wrapper (Boruta) and Filter-based (Mutual-Information). Real-life phishing webpage datasets were used for the analysis. Ensemble classifiers such as Voting, Gradient Boosting, and Random Forest were used in the experiments. Two experiments were conducted. In the first experiment, K-Nearest Neighbor (K-NN) yielded the highest accuracy among single classifiers, with a score of 94.1%, while Random Forest (RF) ensemble achieved 96.7%. In the second experiment, using another baseline feature set, RF performed excellently under the Boruta method with an accuracy of 97.25%, while K-NN retained the highest accuracy of 95.20% among single classifiers. This study provides empirical evidence that feature selection techniques have a great impact on the performance of ML models, for both single and ensemble classifiers, in the detection of phishing attacks.

Hybrid stacked autoencoder with dwarf mongoose optimization for Phishing attack detection in internet of things environment

With the fast development of the Internet of Things (IoTs), phishing attacks are transferring to this domain because of the number of IoT devices and private data they can handle. IoT phishing suggests the malicious practice of targeting IoT devices and their users to steal sensitive data, gain unauthorized access, or compromise the safety and functionality of these devices. Phishing attacks in the framework of IoT take several procedures, comprising SMS phishing, email phishing, or even physical attacks on the devices themselves. Deep learning (DL) approaches are executed to improve the prevention and detection of IoT phishing attacks. Consequently, this article introduces a Dwarf Mongoose Optimization with deep learning for Phishing Attack Detection (DMODL-PAD) method in an IoT platform. The objective of the DMODL-PAD method is to utilize feature selection (FS) with a hyperparameter tuning strategy for automated phishing attack detection in the IoT platform. The DMODL-PAD method involves the DMO model, unlike the FS method. Afterwards, a hybrid stacked autoencoder (HSAE) model can execute the phishing attack detection process. The DMODL-PAD method uses a jellyfish search optimizer (JSO) for the hyperparameter tuning process. The experimentation validation of the DMODL-PAD technique has been examined under a benchmark database. The extensive outcomes portrayed the superior detection results of the DMODL-PAD technique over other recent methods. These results ensured that the DMODL-PAD technique could effectually detect phishing attacks from the IoT environment.

Uncovering phishing attacks using principles of persuasion analysis

With the rising of Internet in early ’90s, many fraudulent activities have migrated from physical to digital: one of them is phishing. Phishing is a deceptive practice focused on exploiting the human factor, which is the most vulnerable aspect of any security process. In this scam, social engineering techniques are extensively utilized, specifically focusing on the principles of persuasion, to deceive individuals into disclosing sensitive information or engaging in malicious actions. This research explores the use of message subjectivity for detecting phishing attacks. It does so by assessing the impact of various data representations and classifiers on automatically identifying principles of persuasion. Furthermore, it investigates how these detected principles of persuasion can be leveraged for identifying phishing attacks. The experiments conducted revealed that there is no universal solution for data representation and classifier selection to effectively detect all principles of persuasion. Instead, a tailored combination of data representation and classifiers is required for detecting each principle. The Machine Learning models created automatically detect principles of persuasion with confidence levels ranging from 0.7306 to 0.8191 for AUC-ROC. Next, principles of persuasion detected are used for phishing detection. This study also emphasizes the need for user-friendly and comprehensible models. To validate the proposal presented, several families of classifiers were tested, but among all of them, tree-based models (and Random Forest in particular) stand out as preferred option. These models achieve similar level of effectiveness as alternative methods while offering improved clarity and user-friendliness, with an AUC-ROC of 0.859842.

Unveiling the Efficacy of AI-based Algorithms in Phishing Attack Detection

Unveiling the Efficacy of AI-based Algorithms in Phishing Attack Detection

Hybrid optimization enabled squeeze net for phishing attack detection

Nowadays, phishing attacks are considered as one of the most common cyber-attacks which generally harm the security of various internet and communication systems. The phishing websites are produced with the intention of determining the user's financial information by creating cyber threats. The loss of valuable user assets occurs due to the frequent creation and circulation of fake websites all over the internet. The negative impacts of phishing websites include financial loss, loss of intellectual property, reputational harm, and interruption of daily operations. To identify and lessen these attempts, several anti-phishing strategies have been put forth during the past ten years. However, they are still inaccurate and inefficient. To overcome this problem, intelligent approaches, like Deep Learning (DL) is used, which can accurately learn the inherent characteristics of the websites and identify phishing websites. In this paper, the phishing websites are determined effectively using the Fractional Dingo Hunter Prey Optimization-SqueezeNet (FDHPO-SqueezeNet) technique. The different features, like web features, ocular features, and Natural Language Processing (NLP) features are extracted separately from the website data. Later, the optimal features are selected, fused, and augmented, and allowed for the detection of phishing websites using SqueezeNet. Finally, the phishing detection is performed using SqueezeNet, where the detection performance of the SqueezeNet is increased by training using the FDHPO technique. The investigation results revealed that the designed FDHPO-SqueezeNet technique recorded greater performance when compared with other prevailing phishing detection approaches with a maximal of 93.05 % accuracy, 94.26 % sensitivity, and 93.75 % specificity, respectively.

A cyber defense system against phishing attacks with deep learning game theory and LSTM-CNN with African vulture optimization algorithm (AVOA)

Phishing attacks pose a significant threat to online security, utilizing fake websites to steal sensitive user information. Deep learning techniques, particularly convolutional neural networks (CNNs), have emerged as promising tools for detecting phishing attacks. However, traditional CNN-based image classification methods face limitations in effectively identifying fake pages. To address this challenge, we propose an image-based coding approach for detecting phishing attacks using a CNN-LSTM hybrid model. This approach combines SMOTE, an enhanced GAN based on the Autoencoder network, and swarm intelligence algorithms to balance the dataset, select informative features, and generate grayscale images. Experiments on three benchmark datasets demonstrate that the proposed method achieves superior accuracy, precision, and sensitivity compared to other techniques, effectively identifying phishing attacks and enhancing online security.

Towards a Hybrid Security Framework for Phishing Awareness Education and Defense

The rise in generative Artificial Intelligence (AI) has led to the development of more sophisticated phishing email attacks, as well as an increase in research on using AI to aid the detection of these advanced attacks. Successful phishing email attacks severely impact businesses, as employees are usually the vulnerable targets. Defense against such attacks, therefore, requires realizing defense along both technological and human vectors. Security hardening research work along the technological vector is few and focuses mainly on the use of machine learning and natural language processing to distinguish between machine- and human-generated text. Common existing approaches to harden security along the human vector consist of third-party organized training programmes, the content of which needs to be updated over time. There is, to date, no reported approach that provides both phishing attack detection and progressive end-user training. In this paper, we present our contribution, which includes the design and development of an integrated approach that employs AI-assisted and generative AI platforms for phishing attack detection and continuous end-user education in a hybrid security framework. This framework supports scenario-customizable and evolving user education in dealing with increasingly advanced phishing email attacks. The technological design and functional details for both platforms are presented and discussed. Performance tests showed that the phishing attack detection sub-system using the Convolutional Neural Network (CNN) deep learning model architecture achieved the best overall results: above 94% accuracy, above 95% precision, and above 94% recall.

MDepthNet based phishing attack detection using integrated deep learning methodologies for cyber security enhancement

MDepthNet based phishing attack detection using integrated deep learning methodologies for cyber security enhancement

Optimizing Deep Neural Networks Using ANOVA for Web Phishing Detection

Phishing attacks are crimes committed by sending spoofed Web URLs that appear to come from a legitimate organization in order to obtain another party's sensitive information, such as usernames, passwords, and other confidential data. The stolen information is then used to commit fraud, such as identity theft and financial fraud, and can cause reputational damage to the party that is the victim of the phishing attack. This can cause great harm to the victimized individual or organization. To overcome these problems, this research uses feature selection using ANOVA and Deep Neural Networks (DNN) to detect web phishing attacks. Feature selection is used to optimize the performance of the DNN model to achieve more accurate results. Based on the results of feature selection using ANOVA, there are 52 attributes that have a significant impact on web phishing attack detection. The next step is to implement DNN to build a web phishing attack detection model. The results of testing the web phishing detection model show that in the training phase, the accuracy value increased by 17.51% for the 80:20 dataset and 18.39% for the 70:30 dataset. During the testing phase, the accuracy value increased by 17.8% for the 80:20 dataset and 18.58% for the 70:30 dataset. The resulting recognition model shows consistent and reliable results not only during training, but also during testing in situations closer to real-world conditions. Conclusively, the use of ANOVA proves effective in mitigating less relevant features and contributing to the optimization of web phishing detection models.

URL based phishing attack detection using BiLSTM-gated highway attention block convolutional neural network

URL based phishing attack detection using BiLSTM-gated highway attention block convolutional neural network

Detection of phishing addresses and pages with a data set balancing approach by generative adversarial network (GAN) and convolutional neural network (CNN) optimized with swarm intelligence

SummaryPhishing attacks have a remarkable ability to steal user information by using simple techniques. Phishing attacks steal valuable information, such as user names and passwords. The loss caused by phishing attacks is significant, and every year, millions of dollars are lost by internet users and companies through phishing attacks. Deep learning methods such as CNN neural network are one approach to detecting phishing attacks: deep learning methods, optimization for image processing, and efficient techniques used to process URL strings. A convolutional neural network is a widely used deep learning method in image processing. The limitation of CNN learning to images is one of the main challenges of this neural network for detecting phishing attacks. Imbalance in the training dataset and lack of intelligent feature selection are other challenges for CNN training in detecting phishing attacks. This manuscript presents a new approach called CGAN‐IWSO‐ResNet50 to detect phishing attacks. In the first step, the improved version of the conditional GAN is used to balance the URL samples. In the second stage, Hand‐crafted and TF‐IDF methods implement the feature extraction phase. In the feature selection stage, the WOA algorithm is used to improve the WSO algorithm's performance in feature selection. The selected features are implemented on the dataset, and legal and phishing samples are coded as RGB images. In the last step, RGB images are used to teach ResNet50 architecture. Tests in the PhishTank dataset showed that the proposed method's accuracy, sensitivity, and precision index are 99.65%, 99.12%, and 99.46%, respectively. The CGAN‐IWSO‐ResNet50 method is more accurate in detecting phishing attacks than the VGG19, AlexNet, RCNN, DNN + LSTM, and DNN + BiLSTM learning methods.

Discrete mathematical models for enhancing cybersecurity : A mathematical and statistical analysis of machine learning approaches in phishing attack detection

This paper presents a discreate mathematical modelling of cybersecurity phishing attack detection methodologies, emphasizing the crucial role of continual advancements in detection methods amidst the pervasive threat of phishing attacks in the cybersecurity landscape. Leveraging mathematical modeling and machine learning algorithms, the study employs three distinct datasets—Mendeley, URL tokenized, and a merged dataset integrating both. Multiple machine learning algorithms, including Logistic Regression, k-Nearest Neighbors, Support Vector Machines, Random Forest, Gradient Boosting Machines, Neural Networks, CatBoost, and XGBoost, are systematically applied to evaluate their efficacy. In the original Mendeley dataset, XGBoost achieves a top accuracy of 97.24%, along with CatBoost and Random Forest exceeding 97%. Post-preprocessing, CatBoost leads with an accuracy of 97.28%, showcasing superior precision, sensitivity, and F-score. Despite slight accuracy reductions post-preprocessing, models consistently achieve over 94% accuracy on the preprocessed Mendeley dataset, highlighting the substantial impact of preprocessing. Tokenized URLs exhibit comparatively lower performance, with the highest accuracy at 91.95%, emphasizing the challenges associated with this approach. The combined dataset proves optimal for most models, with XGBoost and SVM achieving the highest overall accuracy at 97.68%. SVM excels in sensitivity and specificity, while XGBoost excels in precision. The merged dataset significantly enhances accuracy, sensitivity, specificity, and precision, underscoring its pivotal role in refining predictive capabilities for identifying phishing websites. The results section provides a detailed overview of machine learning model performance on different datasets. CatBoost emerges as a standout performer on the preprocessed Mendeley dataset. The tokenized URLs offer valuable insights into associated challenges, and the combined dataset proves effective for various models. Confusion matrices, ROC curves, and Precision-Recall curves provide nuanced perspectives on model behavior, emphasizing the need for ongoing refinement and investigation into misclassification patterns to enhance model effectiveness in combating phishing threats.

A Two-Stage Hybrid Approach for Phishing Attack Detection Using URL and Content Analysis in IoT

The goal of phishing assaults is to trick users into giving up personal information by making them believe they need to act quickly on critical information. The creation of efficient solutions, such as phishing attack detection systems backed by AI, is essential for the safety of users. This research suggests a two-stage hybrid strategy that uses both URL and content analysis to identify phishing assaults. In the first step of the suggested method, URL analysis is used to determine the legitimacy of suspected phishing assaults. If the site is still live, the second check uses content analysis to determine how serious the attack is. Both analysis' findings are taken into account in the decision-making procedure. As can be seen from the experiments, the hybrid system obtains an astounding 99.06% accuracy rate. This research adds to the existing body of knowledge by providing a massive dataset of over 14 million data samples that includes both legal and phishing URLs. Furthermore, when content analysis is required for phishing URL detection, the two-stage hybrid technique significantly outperforms URL analysis alone by 70.23 %. The proposed method provides better defense against phishing attempts and is practical enough for widespread use.

CLASSIFICATION AND METHODS OF DETECTION OF PHISHING ATTACKS

The reliable operation of data transmission networks, computer systems and mobile devices is a mandatory condition for the effective functioning of the state and society, and the life of an individual. The reliability of key information systems for public use depends on many factors: cyber attacks, hardware and software failures, and various types of errors. Rapid expansion of threats to national security in the 21st century. entrusts state authorities with the tasks of their prevention, detection and neutralization. Cyber security is increasingly viewed as a fundamental problem of the state, which comprehensively affects its security, defense, economy, and almost all spheres of public life. The Internet gave a powerful impetus to the development of mass communication, trade and information exchange. However, today it is the area where many crimes are committed. The impersonal nature of digital infrastructure has made identity theft a natural and highly attractive project. Cybercriminals actively use various means of stealing information, including phishing. At the current stage and in the future, the development of both individual societies and states, as well as the world in general, will be carried out in accordance with the concept of the information society, which is connected with the use of information and telecommunication technologies in the acquisition, storage and processing of information in everyday life Phishing is a serious online security problem that involves spoofing genuine websites to trick online users and steal their confidential information. Analyzing these definitions, we can conclude that “phishing” can be considered in different ways, but the main purpose of its conduct remains unchanged—stealing data. The practical value of the results lies in the possibility of using the obtained classifier for further creation of software solutions for recognizing phishing sites. It, as well as a set of characteristics, can be implemented in anti-phishing browser extensions or other anti-phishing tools.