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Disposable email address services have gained popularity recently, offering users a way to register for online services without revealing their primary email. However, this poses challenges for organizations aiming to engage with genuine users. This paper introduces a novel approach combining natural language processing (NLP) and domain validation to identify disposable email addresses. The technique employs various machine learning methods, including Support Vector Classifier (SVC), Multinomial Naive Bayes (MNB), Gaussian Naive Bayes (GNB), Logistic Regression, XGBoost Classifier, K Neighbors Classifier, Random Forest Classifier, Linear, and Discriminant Analysis. Unlike traditional methods relying on blacklists, this technique achieves a 97% accuracy rate by effectively detecting and classifying new disposable emails. [ABSTRACT FROM AUTHOR]

In today’s interconnected digital world, email remains a primary mode of digital interaction valued for its suitability in official, academic and business communications. However, despite its utility, email faces significant challenges due to the widespread presence of spam in various forms, such as phishing, suspicious attachments and deceptive content. This issue not only affects the efficiency and security of email communication but also poses a barrier to its reliability. Therefore, it is essential to devise effective methods to tackle the escalating count of spam emails. This research work presents an intriguing methodology to combat the persistent problem of email spam. The proposed method, abbreviated as AIGSADT, is an amalgamation of the intelligent variant of the gravitational search algorithm (IGSA) and decision trees (DTs). The machine learning-based DT algorithm is individually inadequate for dealing with the large amount of constructive data on a certain attribute due to its instability and ineffectiveness. The proposed AIGSADT approach integrates the IGSA algorithm, which is effective in handling large amounts of data to detect email spam. This is achieved by constructing decision trees employing gravitational forces as the means of information transfer through mass agents. Here, the intelligent factor of the IGSA algorithm prevents the trapping of GSA agents in local optima by updating their positions based on information provided by the best and worst agents. The performance of the presented AIGSADT approach is analyzed through experiments conducted on various categories available in the Ling spam dataset. These experimental evaluations aim to analyze the significance of different pre-processing modules across different dataset categories. The comparative analysis indicates the supremacy of the proposed approach compared to state-of-the-art methodologies. [ABSTRACT FROM AUTHOR]

Mobile texting has increased social engineering assaults like phishing. Because spam, or unsolicited text messages, spread phishing attempts that steal personal information. Traditional methods of spam detection, often based on statistical models or human rule-based systems, have difficulties in keeping up with the growing complexity of spamming strategies. Gathering pertinent data from social networks is a challenging task, mostly due to the limits imposed by privacy concerns and time constraints. The inefficiency and time-consuming nature of conventional frequency-based techniques to word encoding are generally recognized. Text classification has shown promising outcomes with the use of word embeddings and deep learning techniques. The proposed approach involves integrating deep learning with the Remora optimization algorithm framework (DL–ROA) to autonomously extract intricate patterns and nuanced information from text messages. The system's capacity to adapt to new spamming strategies enhances the DL–ROA. The proposed technique improves the accuracy of detection while reducing the inefficiency and time required to create contextual word vectors based on word frequency. Spam detection is achieved by using a hybrid deep model that combines long short-term memory (LSTM) and deep convolutional neural networks (DCNN) architectures. Empirical data demonstrate that the DL–ROA technique surpasses existing deep learning models in terms of accuracy, f1-score, and recall. In addition, the DL–ROA achieved an unprecedented accuracy rate of 98.25%. [ABSTRACT FROM AUTHOR]

On the Internet, online microblogging and social networks have experienced tremendous growth. Millions of individuals share opinions on social networking platforms including Twitter, Facebook, YouTube, and Microblogging sites. Millions of individuals express their thoughts on social platforms namely Twitter, Facebook, YouTube, and microblogging sites. As user‐generated content is growing to a huge extent on the internet, the analysis, extraction identification and classification of opinion and sentiment polarity of different aspects has now become a challenging issue in sentiment analysis. In sentiment analysis, aspect level opinion mining, opinion spam review detection, extraction and representation of most relevant accessed data from the user‐generated data is a difficult process. The word vector representation using existing deep learning techniques affects the performance in aspect level opinion mining. Spammers propagate false opinion for payment exchange, which leads to degradation of the financial growth, business, and fame of the organization. To overcome the limitations aspect level opinion mining is used to recognize sentiment and polarity of aspects in the specified text. This work presents various input feature vectors of deep learning approaches aiming to improve performance in aspect level opinion mining. Next, three meta‐heuristic algorithms with k‐means clustering approach for classification of opinion spam reviews on social media transit tweets is presented. The proposed approaches are evaluated and compared with other existing approaches as well as other benchmark datasets namely Restaurant and Amazon reviews. [ABSTRACT FROM AUTHOR]

Today, identifying and preventing spam has become a challenge, particularly with the abundance of textbased content in emails, social media platforms, and websites. Although traditional spam filters are somewhat effective, they often struggle to keep up with new spam methods. The introduction of Machine Learning (ML) and Deep Learning (DL) models has greatly improved the capabilities of spam detection systems. However, the black-box nature of these models poses challenges to user trust due to their lack of transparency. To address this issue, Explainable AI (XAI) has emerged, aiming to make AI decisions more understandable to humans. This study combines XAI with ensemble learning, utilizing multiple learning algorithms to improve performance, and proposes a robust and interpretable system to detect spam effectively. Four classifiers were used for training and testing: Support Vector Machine (SVM), Logistic Regression (LR), Gradient Boost (GB), and Decision Tree (DT). To reduce overfitting, two independent spam email datasets were blended and balanced. The stacking ensemble technique, based on Random Forest (RF), was the best-performing model compared to individual classifiers, having 98% recall, 96% precision, and 97% F1-score. By leveraging XAI's interpretability, the model elucidates the reasoning behind its classifications, leading to the comprehension of hidden patterns associated with spam detection. [ABSTRACT FROM AUTHOR]

The Internet has become a vital part of our daily lives, serving as a hub for global connectivity and a facilitator for seamless communication and information exchange. However, the rise of malicious domains presents a serious challenge, undermining the reliability of the Internet and posing risks to user safety. These malicious activities exploit the Domain Name System (DNS) to deceive users, leading to harmful activities such as spreading drive-by-download malware, operating botnets, creating phishing sites, and sending spam. In response to this growing threat, the application of Machine Learning (ML) techniques has proven to be highly effective. These methods excel in quickly and accurately detecting, classifying, and analyzing such threats. This paper explores the latest developments in using transfer learning for the classification of malicious domains, with a focus on image visualization as a key methodological approach. Our proposed solution has achieved a remarkable testing accuracy rate of 98.67%, demonstrating its effectiveness in detecting and classifying malicious domains. [ABSTRACT FROM AUTHOR]

Detecting spam emails is essential to maintaining the security and integrity of email communication. Existing research has made significant progress in developing effective spam detection models, but challenges remain in improving classification performance and adaptability to evolving spamming techniques. In this study, we propose a novel spam detection model with a comprehensive feature engineering approach that combines term frequency-inverse document frequency (TF-IDF) vectorizer and word embedding features to optimize the feature space. Our contribution lies in integrating semantic-based word embeddings, leveraging pre-existing knowledge to capture the semantic meaning of words and enhance the representation of email texts. To identify the most suitable word embedding technique for our model, we evaluated GloVe, Word2Vec, and FastText. GloVe was selected for its better performance, which is the result of its pre-training on a large and diverse text corpus. Furthermore, the model was evaluated without word embeddings, which did not exhibit the same effectiveness level as our word embedding-based model. Additionally, we utilized the support vector machine as a classifier and hyperparameter tuning technique to identify our model's most effective parameter values. The proposed model was tested on two datasets. The experimental results showed that our model outperformed the other models discussed in the literature, achieving an accuracy of 99.5% on the SpamAssassin dataset, and 99.28% on the Enron-Spam dataset. [ABSTRACT FROM AUTHOR]

Email is an excellent technique for connecting users at low cost. Spam emails pose the risk of collecting a user's personal information by fooling them into clicking on a link or engaging in other fraudulent activities. Furthermore, when a spam message is delivered, the user may read the entire message before deciding it is spam and deleting it. Most approaches to email classification proposed by other authors use natural language processing (NLP) methods to analyze the content of email messages. One of the biggest shortcomings of NLP-based methods is their dependence on the language in which a message is written. To construct an effective email cyber threat intelligence (CTI) sharing framework, the privacy of a message's content must be preserved. This article proposes a novel domain-specific ontology and method for emails that require only the metadata of email messages to be shared to preserve their privacy, making them applicable to solutions for sharing email CTI. To preserve privacy, a new semantic parser was developed for the proposed email domain-specific ontology to populate email metadata and create a dataset. Machine learning algorithms were examined, and experiments were conducted to identify and classify spam messages using the newly created dataset. Feature-ranking algorithms, chi-squared, ANOVA (analysis of variance), and Kruskal–Wallis tests were used. In all experiments, the kernel naïve Bayes model demonstrated acceptable results. The highest accuracy of 92.28% and an F1 score of 95.92% for recognizing spam email messages were obtained using the proposed domain-specific ontology, the newly developed semantic parser, and the created metadata dataset. [ABSTRACT FROM AUTHOR]

Fake profile detection on social media is a critical task intended for detecting and alleviating the existence of deceptive or fraudulent user profiles. These fake profiles, frequently generated with malicious intent, could engage in different forms of spreading disinformation, online fraud, or spamming. A range of techniques is employed to solve these problems such as natural language processing (NLP), machine learning (ML), and behavioural analysis, to examine engagement patterns, user-generated content, and profile characteristics. This paper proposes an automated fake profile detection using the coyote optimization algorithm with deep learning (FPD-COADL) method on social media. This multifaceted approach scrutinizes user-generated content, engagement patterns, and profile attributes to differentiate genuine user accounts from deceptive ones, ultimately reinforcing the authenticity and trustworthiness of social networking platforms. The presented FPD-COADL method uses robust data pre-processing methods to enhance the uniformness and quality of data. Besides, the FPD-COADL method applies deep belief network (DBN) for the recognition and classification of fake accounts. Extensive experiments and evaluations on own collected social media datasets underscore the effectiveness of the approach, showcasing its potential to identify fake profiles with high scalability and precision. [ABSTRACT FROM AUTHOR]

Purpose: The ongoing evolution of the Internet and the subsequent digitalisation of financial services, along with the ever-increasing innovation of financial products, have rendered consumers more vulnerable to a wider range of fraud in the banking sector and, particularly, to consumer financial fraud (CFF). This paper aims to analyse the factors that may contribute to CFF exposure and victimisation among Spaniards, with a special focus on financial literacy. Design/methodology/approach: This paper provides a comprehensive overview of leading publications on the topic, followed by empirical analyses using regression models with a sample of 6,207 Spanish individuals drawn from the Survey of Financial Competences. Findings: Objective and subjective financial knowledge are positively correlated with CFF exposure via email but do not protect against CFF victimisation. Similarly, financial knowledge overconfidence is positively related to the former but fails to constitute a driver of the latter. Financial inclusion, measured by the number of financial products held, not only increases the risk of this exposure but also contributes to its subsequent victimisation. Originality/value: To the best of the authors' knowledge, no previous paper has analysed the relationship between CFF and financial literacy by differentiating two types of vulnerabilities to fraud (exposure and victimisation) while considering different constructs of financial literacy. Dissecting these two domains may explain why the same financial literacy construct can have different effects at both stages of financial fraud and, furthermore, how different financial literacy constructs may affect the same stage of financial fraud. [ABSTRACT FROM AUTHOR]

This study investigated the impact of AI-generated email content on email marketing deliverability, specifically its placement in spam folders. A controlled experiment was conducted with 450 participants who received AI-generated emails sent from different domains using plain text content and clear subject lines. The emails were analyzed for placement in inboxes or spam folders based on participant responses. The results revealed no significant impact of AI-generated content on email deliverability, regardless of the sender's domain or the recipient's email provider. All emails consistently reached primary inboxes, suggesting that the applied precautions (plain text, clear subject lines, and avoidance of suspicious elements) mitigated any potential spam triggers. This study shows AI-generated email content can be deliverable and personalized, challenging concerns about spam placement. Marketers can use AI to craft engaging emails that land in inboxes, not spam folders. This finding also aligns with SEO trends, where AI content isn't automatically penalized. [ABSTRACT FROM AUTHOR]

In the present study, Krill Herd (KH) is proposed as a Feature Selection tool to detect spam email problems. This works by assessing the accuracy and performance of classifiers and minimizing the number of features. Krill Herd is a relatively new technique based on the herding behavior of small crustaceans called krill. This technique has been combined with a local search algorithm called Tabu Search (TS) and has been successfully employed to identify spam emails. This method has also generated much better results than other hybrid algorithm optimization systems such as the hybrid Water Cycle Algorithm with Simulated Annealing (WCASA). To assess the effectiveness of KH algorithms, SVM classifiers, and seven benchmark email datasets were used. The findings indicate that KHTS is much more accurate in detecting spam mail (97.8%) than WCASA. [ABSTRACT FROM AUTHOR]

Spam emails and phishing attacks continue to pose significant challenges to email users worldwide, necessitating advanced techniques for their efficient detection and classification. In this paper, we address the persistent challenges of spam emails and phishing attacks by introducing a cutting-edge approach to email filtering. Our methodology revolves around harnessing the capabilities of advanced language models, particularly the state-of-the-art GPT-4 Large Language Model (LLM), along with BERT and RoBERTa Natural Language Processing (NLP) models. Through meticulous fine-tuning tailored for spam classification tasks, we aim to surpass the limitations of traditional spam detection systems, such as Convolutional Neural Networks (CNNs). Through an extensive literature review, experimentation, and evaluation, we demonstrate the effectiveness of our approach in accurately identifying spam and phishing emails while minimizing false positives. Our methodology showcases the potential of fine-tuning LLMs for specialized tasks like spam classification, offering enhanced protection against evolving spam and phishing attacks. This research contributes to the advancement of spam filtering techniques and lays the groundwork for robust email security systems in the face of increasingly sophisticated threats. [ABSTRACT FROM AUTHOR]

The impact of financial fraud is widespread, from everyday life to the financial industry, and it reduces industry confidence and destabilizes the country's economy. Therefore, it is important to develop an intelligent financial fraud detection system for early warning and prevention. This study proposes a double-weight latent Dirichlet allocation (DW-LDA) to extract the keywords from financial fraud data, and then we use five intelligent classifiers to build an intelligent text fraud detection model. In addition, the financial fraud dataset usually contains more non-fraud cases than fraud cases, which is an imbalanced dataset; hence, this study uses a synthesized minority oversampling technique (SMOTE) and random undersampling to handle imbalanced datasets. In verification, this study collected the Enron email and MD&A datasets to compare the performances of the related topic models and weighted LDA (TFIDF+LDA and PMI + LDA) with the proposed DW-LDA after SMOTE handling. In evaluating model performance, we use accuracy, recall, precision, F-score, and AUC as evaluation metrics, and the results show that the proposed DW-LDA (TFIDF+PMI + LDA) has a better performance than the listing topic models. For visual information representation, we use visual graphs to show the important results, such as the word cloud of the fraudulent email and keywords. The research results and the built intelligent text fraud detection model can be provided to investors and stakeholders for reference. [ABSTRACT FROM AUTHOR]

Phishing email attacks are among the most common and most harmful cybersecurity attacks. With the emergence of generative AI, phishing attacks can be based on emails generated automatically, making it more difficult to detect them. That is, instead of a single email format sent to a large number of recipients, generative AI can be used to send each potential victim a different email, making it more difficult for cybersecurity systems to identify the scam email before it reaches the recipient. Here, we describe a corpus of AI-generated phishing emails. We also use different machine learning tools to test the ability of automatic text analysis to identify AI-generated phishing emails. The results are encouraging, and show that machine learning tools can identify an AI-generated phishing email with high accuracy compared to regular emails or human-generated scam emails. By applying descriptive analytics, the specific differences between AI-generated emails and manually crafted scam emails are profiled and show that AI-generated emails are different in their style from human-generated phishing email scams. Therefore, automatic identification tools can be used as a warning for the user. The paper also describes the corpus of AI-generated phishing emails that are made open to the public and can be used for consequent studies. While the ability of machine learning to detect AI-generated phishing emails is encouraging, AI-generated phishing emails are different from regular phishing emails, and therefore, it is important to train machine learning systems also with AI-generated emails in order to repel future phishing attacks that are powered by generative AI. [ABSTRACT FROM AUTHOR]

Email spam is more than just annoying. It is costly, both to the individual and to society. This Comment examines these costs through the lens of one type of spam: political fundraising emails. It also proposes two types of solutions. The first is “front-end” regulation, which could include a do-not-email registry or an outright ban on unsolicited bulk email. This type of regulation would address the spam problem by decreasing the overall amount. The second type of solution is “back-end” regulation, meaning subject-line labeling and a requirement that spammers stick to one sender name. This would tackle the problem by decreasing the costs of receiving each spam email. This Comment weighs the advantages and disadvantages of each approach, ultimately concluding that Congress should pursue back-end regulation first because it is more likely to survive First Amendment scrutiny. [ABSTRACT FROM AUTHOR]

The rapid spread and daily usage of social networks have made them vulnerable to spammers. Therefore, detecting and eliminating spam and spammers has become more than necessary to reduce the risks that it poses to users' security. In order to achieve this goal, it is crucial to determine the exact features that help identify and classify whether a user is spam or not. This paper proposes a wrapper-based method for selecting the most important features. It is based on combining two recent metaheuristic algorithms, the Henry Gas Solubility Optimization Algorithm (HGSO) and the Equilibrium Optimizer Algorithm (EO), with the goal of choosing a small and most influential subset of features that give good performance and help in the spammer profile detection process. For the purpose of showing the ability of the proposed method to achieve the desired goals, several comparisons are conducted on a modified Social Honeypot dataset. The first comparison is made between HGSOEO and the two algorithms (HGSO and EO) that were used to develop the proposed algorithm to prove the power of hybridization. The two next comparisons are made against some classical filter- and wrapper-based feature selection methods. The last comparison is carried out against some well-known metaheuristic algorithms for feature selection. Experiments and analysis of the results show that the proposed model is more accurate than the algorithms and methods that we compared it to. [ABSTRACT FROM AUTHOR]

In recent years, the widespread adoption of Machine Learning (ML) at the core of complex IT systems has driven researchers to investigate the security and reliability of ML techniques. A very specific kind of threats concerns the adversary mechanisms through which an attacker could induce a classification algorithm to provide the desired output. Such strategies, known as Adversarial Machine Learning (AML), have a twofold purpose: to calculate a perturbation to be applied to the classifier's input such that the outcome is subverted, while maintaining the underlying intent of the original data. Although any manipulation that accomplishes these goals is theoretically acceptable, in real scenarios perturbations must correspond to a set of permissible manipulations of the input, which is rarely considered in the literature. In this article, we present AdverSPAM, an AML technique designed to fool the spam account detection system of an Online Social Network (OSN). The proposed black-box evasion attack is formulated as an optimization problem that computes the adversarial sample while maintaining two important properties of the feature space, namely statistical correlation and semantic dependency. Although being demonstrated in an OSN security scenario, such an approach might be applied in other context where the aim is to perturb data described by mutually related features. Experiments conducted on a public dataset show the effectiveness of AdverSPAM compared to five state-of-the-art competitors, even in the presence of adversarial defense mechanisms. [ABSTRACT FROM AUTHOR]

Product reviews are becoming a more popular tool for businesses and individuals when making judgements about purchases. Spammers create synthesized reviews to either promote certain items or denigrate those of rivals to make money. As a result, in recent years, both the business and research sectors have paid close attention to the detection of false opinion spam. Customers' decision-making is severely harmed by false opinion spam in service or product evaluations. It's becoming difficult to identify false opinion spam. Accordingly, the article proposed to detect deceptive opinion spam based on a hybrid deep learning technique. Initially, the model was tested using deceptive reviews gathered from several online forums. To identify deceptive reviews, many researchers at the moment create models based on a single text attribute. On the contrary, deceptive reviewers will decisively copy the wording style of legitimate evaluations while submitting reviews. These text-feature-based techniques may or may not be successful. As a result, the research suggested an ensemble multiple-feature selection technique of the Extra tree classifier to extract information based on a variety of features, including text, behaviour, and deceptive scoring features. In addition, a data resampling approach is used that integrates the Borderline-SMOTE algorithm to reduce the effects of the high dimensional imbalanced class category distribution. For detecting deceptive reviews, the article developed a hybrid technique of Bidirectional Long Short-Term Memory (Bi-LSTM) with a Capsule Neural Network to detect the positive and negative false opinions spam. The model optimizes the dynamic routing algorithm and changes the structure of the conventional capsule network without sacrificing classification performance, leading to high model accuracy. The model performance is evaluated using Python software. The study assesses the suggested model using data from two distinct domains (hotel and restaurant) as a standard benchmark. The experimental results demonstrate the advantage of neural models with higher accuracy of 99% respectively, showing that the suggested neural model greatly outperforms the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

The growing popularity of social media has engendered the social problem of spam proliferation through this medium. New spam types that evade existing spam detection systems are being developed continually, necessitating corresponding countermeasures. This study proposes an anomaly detection-based framework to detect new Twitter spam, which works by modeling the characteristics of non-spam tweets and using anomaly detection to classify tweets deviating from this model as anomalies. However, because modeling varied non-spam tweets is challenging, the technique's spam detection and false positive (FP) rates are low and high, respectively. To overcome this shortcoming, anomaly detection is performed on known spam tweets pre-detected using a trained decision tree while modeling normal tweets. A one-class support vector machine and an autoencoder with high detection rates are used for anomaly detection. The proposed framework exhibits superior detection rates for unknown spam compared to conventional techniques, while maintaining equivalent or improved detection and FP rates for known spam. Furthermore, the framework can be adapted to changes in spam conditions by adjusting the costs of detection errors. [ABSTRACT FROM AUTHOR]

Social networks are widely used platforms for sharing various information and content, including text, images, and videos.The main challenge in social networking today is the verification of data credibility and the identification of genuine social media profiles. Cybercriminals take advantage of this by utilizing fake profiles to disseminate false information. However, current research mainly concentrates on spam filtering and analyzing malicious behavior separately, disregarding the interrelated nature of these issues. We propose a more desirable global, hybrid solution that encompasses both malicious profile detection and spam detection to mitigate the spread of spam effectively. This paper offers a deep learning-based method for detecting malicious profiles and spam tweets. For the profiles to interact with them as legitimate profiles, we first provide the detection of fake profiles using an automated honeypot. Next, we detect those who make interactions as malicious profiles, and finally, we collect their shared content to find spam tweets using a convolution neural network algorithm. We suggest using collaborative filtering and content filtering algorithms from recommender systems to define accounts similar to harmful profiles and spam similar to spam material picked up by convolution neural networks. We get a highly compelling and intriguing outcome with higher accuracy (99.23%) and lesser loss than typical learning algorithms. [ABSTRACT FROM AUTHOR]

Electronic Electronic messages, i.e. e-mails, are a communication tool frequently used by individuals or organizations. While e-mail is extremely practical to use, it is necessary to consider its vulnerabilities. Spam e-mails are unsolicited messages created to promote a product or service, often sent frequently. It is very important to classify incoming e-mails in order to protect against malware that can be transmitted via e-mail and to reduce possible unwanted consequences. Spam email classification is the process of identifying and distinguishing spam emails from legitimate emails. This classification can be done through various methods such as keyword filtering, machine learning algorithms and image recognition. The goal of spam email classification is to prevent unwanted and potentially harmful emails from reaching the user's inbox. In this study, Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms are used to classify spam emails and the results are compared. Algorithms with different approaches were used to determine the best solution for the problem. 5558 spam and non-spam e-mails were analyzed and the performance of the algorithms was reported in terms of accuracy, precision, sensitivity and F1-Score metrics. The most successful result was obtained with the RF algorithm with an accuracy of 98.83%. In this study, high success was achieved by classifying spam emails with machine learning algorithms. In addition, it has been proved by experimental studies that better results are obtained than similar studies in the literature. [ABSTRACT FROM AUTHOR]

Spam messages affect the normal communication of mobile phone users and public security. Fake base station (FBS) is the main channel for sending spam messages. Accurate spatiotemporal prediction of spam messages from FBS is important for implementing short-term spam prevention measures by the police. However, existing models used for spam messages modeling often focus on capturing spatial dependencies based on distance, thus failing to fully exploit spatial dependencies hidden in the data. Meanwhile, they encounter difficulties in mitigating the impact of excessive zero data. Therefore, based on the crowdsourced spam message data, a temporal graph-convolutional-network model (FBS-TGCN) is developed for the spatiotemporal prediction of spam messages from FBS. This model includes graph convolutional network (GCN) and gated temporal convolutional network (TCN) to capture the spatial and temporal dependencies alternatively. We further combine a weighted adjacency matrix and a self-adaptive adjacency matrix to capture both distance-based spatial dependencies and hidden spatial dependencies. A weighted loss function is used to mitigate the influence of excessive zero data. Experimental results using the spam dataset in Beijing demonstrate the effectiveness of the proposed model when compared to the baseline models, especially in the prediction of spam messages quantity and high spamming areas. [ABSTRACT FROM AUTHOR]

The Internet and social media networks usage has increased nowadays and become a prominent medium of communicating. Email is one of the professional reliable methods of communication. Automatic classifications of spam emails have become an area of interest. In order to detect spam emails, this study utilizes a dataset, including spam and non-spam emails. Various techniques are applied to obtain higher accuracy using machine learning techniques. NLP is also utilized for improvising accuracy using embeddings. For that, this work utilizes the BERT model, to achieve satisfactory detection of spam emails. Further, the results are compared with state-of-the-art methods, including, KNN, LSTM and Bi-LSTM. The results obtained by Bi-LSTM and LSTM were 97.94% and 86.02%, respectively. The presented methodology is promising in detecting spam emails due to the higher accuracy achieved. [ABSTRACT FROM AUTHOR]

Background: Spams are commonly known as unwanted commercial or deceptive emails, which strategically target specific individuals or businesses to promote products or mislead recipients. However, with the implementation of advanced technologies such as machine learning and natural language processing, computers can be trained to discern and categorize these emails as spam or legitimate (ham) messages. Despite considerable efforts in spam filtering, the effective identification and mitigation of spam emails remain an ongoing challenge. Methods: This research places particular emphasis on scrutinizing email headers and extracting crucial data, such as HOP count and IP address, using a Python script that serves as a forensic or investigative tool for analyzing and extracting information from email files. Additionally, it assesses various vectorization techniques to gauge the efficacy of machine-learning approaches for spam classification. The work encompasses a range of supervised learning algorithms, including Logistic Regression, Decision Trees, Naive Bayes, and Natural Language Processing (NLP) methods, such as Bidirectional Encoder representation of transformers (BERT). Two vectorization methods, count vectorization and tf-idf vectorization, are compared. The evaluation metrics employed included accuracy, training time, CPU and wall times, precision, recall, f1 score, and support. Conclusion: The performance of the Decision Trees is particularly noteworthy, achieving a flawless 100% accuracy rate. The trained model is seamlessly integrated into both an Android application and a website, enabling realtime spam detection and classification. [ABSTRACT FROM AUTHOR]

Learning-based classifiers are found to be vulnerable to attacks by adversarial samples. Some works suggested that ensemble classifiers tend to be more robust than single classifiers against evasion attacks. However, recent studies have shown that this is not necessarily the case under more realistic settings of black-box attacks. In this paper, we propose a novel ensemble approach to improve the robustness of classifiers against evasion attacks by using diversified feature selection and a stochastic aggregation strategy. Our proposed scheme includes three stages. Firstly, the adversarial feature selection algorithm is used to select a feature each time that can trade-offbetween classification accuracy and robustness, and add it to the feature vector bank. Secondly, each feature vector in the bank is used to train a base classifier and is added to the base classifier bank. Finally, m classifiers from the classifier bank are randomly selected for decision-making. In this way, it can cause each classifier in the base classifier bank to have good performance in terms of classification accuracy and robustness, and it also makes it difficult to estimate the gradients of the ensemble accurately. Thus, the robustness of classifiers can be improved without reducing the classification accuracy. Experiments performed using both Linear and Kernel SVMs on genuine datasets for spam filtering, malware detection, and handwritten digit recognition demonstrate that our proposed approach significantly improves the classifiers' robustness against evasion attacks. [ABSTRACT FROM AUTHOR]

As social media platforms continue their exponential growth, so do the threats targeting their security. Detecting disguised spam messages poses an immense challenge owing to the constant evolution of tactics. This research investigates advanced artificial intelligence techniques to significantly enhance multiplatform spam classification on Twitter and YouTube. The deep neural networks we use are state-of-the-art. They are recurrent neural network architectures with long- and short-term memory cells that are powered by both static and contextualized word embeddings. Extensive comparative experiments precede rigorous hyperparameter tuning on the datasets. Results reveal a profound impact of tailored, platform-specific AI techniques in combating sophisticated and perpetually evolving threats. The key innovation lies in tailoring deep learning (DL) architectures to leverage both intrinsic platform contexts and extrinsic contextual embeddings for strengthened generalization. The results include consistent accuracy improvements of more than 10–15% in multisource datasets, unlocking actionable guidelines on optimal components of neural models, and embedding strategies for cross-platform defense systems. Contextualized embeddings like BERT and ELMo consistently outperform their noncontextualized counterparts. The standalone ELMo model with logistic regression emerges as the top performer, attaining exceptional accuracy scores of 90% on Twitter and 94% on YouTube data. This signifies the immense potential of contextualized language representations in capturing subtle semantic signals vital for identifying disguised spam. As emerging adversarial attacks exploit human vulnerabilities, advancing defense strategies through enhanced neural language understanding is imperative. We recommend that social media companies and academic researchers build on contextualized language models to strengthen social media security. This research approach demonstrates the immense potential of personalized, platform-specific DL techniques to combat the continuously evolving threats that threaten social media security. [ABSTRACT FROM AUTHOR]

Text classification involves organizing textual information into predefined classes, a task which is particularly useful in domains like sentiment analysis, spam detection, and content labeling. In India, where a massive amount of information is generated daily through newspapers and social media, Hindi is one of the most widely used and spoken languages. However, there is limited research on Hindi text classification and, particularly, regarding Hindi news classification. This paper presents a research study to classify Hindi news articles published in Hindi-language newspapers in India by using and comparing various Machine Learning (ML) and Deep Learning (DL) algorithms. To prepare the textual news data for classification, pre-processing and feature engineering techniques, such as count vectorizer, Tf-Idf vectorizer and Doc2Vec, were used and applied to convert texts into vectors. This pre-processing step on the textual data was very challenging due to the presence of multimodal words, conjunctions, punctuation, and special characters in Hindi texts. The study considered Hindi news headlines from predetermined categories (Science, Sports, Entertainment and Business) and, among the different ML and DL models tested and evaluated, Linear Regression with Doc2Vec vectorizer and SGD classifier with Tf-Idf vectorizer produced best accuracies of 97.04% and 96.59%, respectively. The best performing DL model was found to be the Bi-LSTM with an accuracy of approximately 97% on the testing data. [ABSTRACT FROM AUTHOR]

In recent years, the prevalence of imbalanced datasets has posed significant challenges to traditional machine learning models. This imbalance is especially pronounced in fields such as spam detection, where malicious or unwanted messages are typically outnumbered by legitimate ones. Although various techniques have been developed to address this disparity, most conventional methods either undersample the majority class or oversample the minority class, potentially leading to information loss or overftting. In this study, we propose a novel approach using Generative Adversarial Networks (GANs) to generate synthetic samples, thus enhancing the representation of the minority class. By leveraging the powerful BERT embeddings to capture the intricate textual nuances, our model strives to produce synthetic spam messages that are not only realistic but also diverse. Initial results indicate that our GAN-augmented model offers a noticeable improvement in detecting spam messages compared to traditional techniques. This advancement not only holds potential for spam detection but also suggests broader applicability in addressing dataset imbalance across various domains. [ABSTRACT FROM AUTHOR]

Cybersecurity threats, particularly spam SMS, are increasingly sophisticated, demanding more advanced detection systems. Traditional spam detection methods fall short due to their ineffectiveness against novel threats and lack of transparency. This paper investigates the role of Explainable Artificial Intelligence (XAI) in spam detection, emphasizing the interpretability of AI-driven systems through SHapley Additive explanations (SHAP). We propose a hybrid model combining BERT with Random Forest (RF) and Artificial Neural Networks (ANN) for spam detection, and employ SHAP values to elucidate the decision-making process. The study demonstrates that our XAI approach not only improves the accuracy of spam detection but also enhances the transparency and trustworthiness of the predictions. These findings suggest that the incorporation of XAI into spam detection models is not only beneficial but necessary for future cybersecurity measures. Our research invites further exploration into other XAI techniques and their applications in real-world scenarios. [ABSTRACT FROM AUTHOR]

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The classification of Short Message Service (SMS) is a crucial task that requires distinguishing undesirable and harmful messages from legitimate ones. These spam messages may include malicious links that reveal users' personal data to some sharp practices. Many Machine Learning (ML) and deep learning algorithms prevail for spam detection. However, a large number of features present in the message drop the classifier's performance, which can be untangled through feature engineering. This paper proposes an ensemble feature selection algorithm assessed over a high-dimensional imbalanced dataset that resembles the actual data prevailing in the surroundings. The proposed work employs wrapper-based Stratified K-fold Cross-Validated (SKCV) Recursive Feature Elimination with Cross-Validation (RFECV) over hyperparameters tuned Support Vector Classifier (SVC) and random forest algorithms. This study contributes an efficient feature selection algorithm that produces a pruned feature subset substituting the whole dataset. To establish the potential of the proposed approach, its performance is evaluated against five feature selection techniques, including Mutual Information (MI), Low Variance (LV), Particle Swarm Optimization (PSO), Chi-square, and Recursive Feature Elimination (RFE). All obtained feature subsets are analyzed with three ML models Logistic Regression (LR), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB). This research also compares the proposed framework results with Long Short Term Memory (LSTM) neural network to validate its competence. [ABSTRACT FROM AUTHOR]

Online reviews have become available for consumers' reference to make purchase decisions, but a large number of spam reviews have damaged e-commerce reputations. Previous research has addressed review spam detection with classification models using textual features, behavior features, and relational features. However, the fine-grained aspect features related to the product attributes in online reviews have been overlooked and have not yet been thoroughly studied. Therefore, this study proposes a review spam detection model based on a list of novel aspect features. The basic idea is that since spam reviews are usually written by users without real experience, the product aspects depicted in spam reviews will be different from those in genuine reviews. First, we use the Bi-LSTM model to automatically extract massive aspect words, which are then clustered into different aspect categories by the K-means algorithm. Further, we propose nine novel aspect features to train a machine learning model for review spam detection. Experimental results on two labeled Yelp datasets show that the proposed aspect features can significantly improve the accuracy of review spam detection by about 16.11% to 38.86% compared with textual and behavior features. [ABSTRACT FROM AUTHOR]

The botnet attack is one of the coordinated attack types that can infect Internet of Things (IoT) devices and cause them to malfunction. Botnets can steal sensitive information from IoT devices and control them to launch another attack, such as a Distributed Denial-of-Service (DDoS) attack or email spam. This attack is commonly detected using a network-based Intrusion Detection System (NIDS) that monitors the network device's activity. However, IoT network is dynamic and IoT devices have many types with different configurations and vendors in IoT environments. Therefore, this research proposes an Intrusion Detection System (IDS) by ensemble-ing traffic from heterogeneous IoT devices. This research proposes Deep Neural Network (DNN) to create a training model from each heterogeneous IoT device. After that, each training model from each heterogeneous IoT device is used to predict the traffic. The prediction results from each training model are averaged using the ensemble averaging method to determine the final result. This research used the N-BaIoT dataset to validate the proposed IDS model. Based on experimental results, ensemble averaging DNN can detect botnet attacks in heterogeneous IoT devices with an average accuracy of 97.21, precision of 91.41, recall of 87.31, and F1-score 88.48. [ABSTRACT FROM AUTHOR]

Online questionnaires that use crowdsourcing platforms to recruit participants have become commonplace, due to their ease of use and low costs. Artificial intelligence (AI)-based large language models (LLMs) have made it easy for bad actors to automatically fill in online forms, including generating meaningful text for open-ended tasks. These technological advances threaten the data quality for studies that use online questionnaires. This study tested whether text generated by an AI for the purpose of an online study can be detected by both humans and automatic AI detection systems. While humans were able to correctly identify the authorship of such text above chance level (76% accuracy), their performance was still below what would be required to ensure satisfactory data quality. Researchers currently have to rely on a lack of interest among bad actors to successfully use open-ended responses as a useful tool for ensuring data quality. Automatic AI detection systems are currently completely unusable. If AI submissions of responses become too prevalent, then the costs associated with detecting fraudulent submissions will outweigh the benefits of online questionnaires. Individual attention checks will no longer be a sufficient tool to ensure good data quality. This problem can only be systematically addressed by crowdsourcing platforms. They cannot rely on automatic AI detection systems and it is unclear how they can ensure data quality for their paying clients. [ABSTRACT FROM AUTHOR]

Spam emails pose a substantial cybersecurity danger, necessitating accurate classification to reduce unwanted messages and mitigate risks. This study focuses on enhancing spam email classification accuracy using stacking ensemble machine learning techniques. We trained and tested five classifiers: logistic regression, decision tree, K-nearest neighbors (KNN), Gaussian naive Bayes and AdaBoost. To address overfitting, two distinct datasets of spam emails were aggregated and balanced. Evaluating individual classifiers based on recall, precision and F1 score metrics revealed AdaBoost as the top performer. Considering evolving spam technology and new message types challenging traditional approaches, we propose a stacking method. By combining predictions from multiple base models, the stacking method aims to improve classification accuracy. The results demonstrate superior performance of the stacking method with the highest accuracy (98.8%), recall (98.8%) and F1 score (98.9%) among tested methods. Additional experiments validated our approach by varying dataset sizes and testing different classifier combinations. Our study presents an innovative combination of classifiers that significantly improves accuracy, contributing to the growing body of research on stacking techniques. Moreover, we compare classifier performances using a unique combination of two datasets, highlighting the potential of ensemble techniques, specifically stacking, in enhancing spam email classification accuracy. The implications extend beyond spam classification systems, offering insights applicable to other classification tasks. Continued research on emerging spam techniques is vital to ensure long-term effectiveness. [ABSTRACT FROM AUTHOR]

The email has become an online communication tool and an important part of daily life. Spam mails take up a lot of space and bandwidth, and spam filtering algorithms have flaws that cause them to mistake legitimate emails for spam (false positives). These issues are becoming a bigger difficulty for the online world. This work proposes the use of a sandpiper optimization (SPO) algorithm, which is applied for the feature selection process which minimizes the training complexity and maximizes the classification accuracy and Radial Bias Neural Network (RBNN) for classifying emails as genuine email and spam email. The Enron email dataset and Spam Assassin datasets were used. The outcomes show that the rotation forest algorithm after feature selection with SPO accurately classifies the emails as genuine email and spam email with 3.88%, 5.75%, and 6.16% higher accuracy for Genuine Email, 2.31%, 8.47%, and 7.23% higher accuracy for spam email compared with existing Universal Spam Detection using Transfer Learning of the BERT Model (USD-TL-BERT), Hybrid Learning Approach for E-mail Spam Detection and Classification (HLA-ESDC), and the Email Spam Detection Using Hierarchical Attention Hybrid Deep Learning Method (ESD-HAHD), respectively. This demonstrates that the proposed method significantly outperforms existing methods. [ABSTRACT FROM AUTHOR]

With the tremendous proliferation of social media in day-to-day life, social content has become one of the potential information sources. Ensuring social trust is crucial to averting the negative impact on viral marketing, expertise retrieval, and recommendation systems, leading to ascertainment of credibility of social media users. A unified framework is proposed for evaluation of the user credibility through analysis of three fundamental credibility-related factors from social media and e-commerce sites, namely, probability of being a promoter, a spam bot, and/or a spammer. The factors are estimated using deep learning baseline model and/or further analysed using Fuzzy Inference System for making a decision on Tweeter credibility. The framework is demonstrated on Twitter and Amazon benchmark datasets for evaluating its efficacy in identifying credible users. The proposed system outperformed the baseline model and state-of-the-art techniques providing an accuracy of 97% in detecting the credible user. [ABSTRACT FROM AUTHOR]

Traditional methods of automatic generation of recruitment text usually rely on a large number of data annotation and complex statistical algorithms, but these methods have certain limitations. In order to further optimize the processing power of neural network and improve its computational efficiency and stability, this paper aims to solve the shortcomings of convergence and local vulnerability in existing neural network algorithms, and make general improvements to them. Through optical detection and improved neural network algorithm, a more efficient method of automatic generation of artificial intelligence recruitment text is developed. Candidates' resumes are pre-processed with light detection technology to extract key information and filter out spam text. Then the processed text is input into the improved neural network model for training to improve the quality of recruitment text generation. The experimental results show that the proposed method has achieved significant improvement in the accuracy and readability of the automatic generation of recruitment texts, which can provide high quality recruitment texts for human resources departments, improve recruitment efficiency and reduce labor costs. This study provides new ideas and methods for further developing the application of artificial intelligence in the field of recruitment. [ABSTRACT FROM AUTHOR]