Accuracy Of Spam Detection Research Articles

Analyzing Privacy and Security in Cloud Computing Environments

With the rapid surge of spam across the Internet and its various forms, effectively identifying and combating spam has become an urgent priority. Cloud computing offers significant advantages in terms of storage and processing capabilities, making it a viable solution for analysing vast amounts of email data. To address the dynamic nature of spam and its life cycle, an anti-spam system with feedback reassessment is proposed. This system incorporates a text filtering approach based on active learning, involving four key stages: training, filtering, feedback, and re-filtering. Compared to traditional systems, the feedback-enabled filtering system demonstrates improved keyword filtering. To further enhance the accuracy of spam detection and minimize misjudgements in legitimate emails, leveraging weighted decision-making based on email header information is recommended. Additionally, for emails with sparse content, employing title weighting in the filtering process proves to be both feasible and effective, particularly in identifying spam with minimal text content. Given the advancements of cloud-based filtering methods over traditional algorithms, leveraging cloud computing holds promise in effectively combating the escalating volume of spam. As such, this paper delves into an in-depth exploration of spam identification within cloud computing environments, focusing on text filtering systems. This study is informed by a comprehensive analysis of existing anti-spam technologies, aiming to contribute to the ongoing efforts in mitigating spam proliferation online.

Efficient Email Spam Classification with N-gram Features and Ensemble Learning

In this paper, we present an innovative approach to enhancing email spam classification using N-gram features, TF-IDF weighting, SMOTE oversampling, and ensemble learning techniques such as Decision Trees, Random Forests, and Ensemble Extra Trees. Our methodology involves preprocessing the dataset to extract N-gram features, applying TF-IDF weighting to highlight important terms, and addressing class imbalance through SMOTE. We then train and evaluate multiple classification models and find that the Ensemble Extra Trees algorithm outperforms others in terms of accuracy, precision, recall, and F1-score. Our experiments on benchmark datasets confirm the efficacy of our approach, showcasing significant improvements in spam detection accuracy and highlighting the potential of ensemble learning for email spam classification. This research contributes to the advancement of spam filtering technologies, providing a robust and efficient solution for accurately identifying and categorizing spam emails.

An enhanced mechanism for detection of spam emails by deep learning technique with bio-inspired algorithm

This study aims to create a highly effective system for classifying email spam, with the key objective of improving performance and accuracy in classification. Rigorous pre-processing techniques, including lemmatization and tokenization, are applied to refine the dataset. The impact of optimal feature selection on deep learning algorithm stability with varying training datasets is investigated and to improve classifier performance, pre-stages such as feature extraction and selection using bag of words are utilized. The hybrid model is further enhanced by combining feature extraction and classification algorithms with optimization. The inclusion of multiple features, such as n-gram features, addresses the 'Curse of Dimensionality,' and optimal feature selection is employed to improve the spam detection process. The proposed system undergoes three main modifications: multiple feature extraction, feature selection, and an enhanced hybrid model, all aimed at improving spam detection accuracy. The e-mail classification phase involves mapping between training and testing sets, with deep learning algorithms utilized for feature extraction and classification.The recently introduced Sand Cat Swarm Optimization algorithm is employed to refine weights during each epoch, enhancing accuracy and minimizing loss. The proposed system extends its capabilities to identify phishing attacks within the network, offering a comprehensive approach to email security.

Towards Transparent Cybersecurity: The Role of Explainable AI in Mitigating Spam Threats

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.

A Novel Approach for Arabic SMS Spam Detection Using Hybrid Deep Learning Techniques

Spam detection in SMS communication is a crucial task for maintaining the quality of messaging services and protecting users from unwanted and potentially harmful messages. Arabic SMS spam detection poses unique challenges due to the rich morphology and complex structure of the Arabic language, which can significantly impact the performance of traditional text classification methods. To address these challenges, this paper presents a novel approach for Arabic SMS spam detection using a hybrid deep learning model that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. The proposed model leverages the strengths of CNNs in capturing local features and patterns in the text and the capability of Bi-LSTM networks to understand long-term dependencies and contextual information. This hybrid architecture is designed to effectively handle the complexities of the Arabic language and improve the accuracy of spam detection. The model was evaluated on a dataset of Arabic SMS messages, consisting of both ham (non-spam) and spam messages. The dataset underwent preprocessing steps, including text cleaning, tokenization, and padding, to prepare it for training the deep learning model. The hybrid model was trained using the Adam optimizer and evaluated using accuracy, precision, recall, and F1 score metrics. Early stopping was implemented to prevent overfitting during the training process. The results demonstrate that the hybrid model achieved high performance, with an accuracy of 0.9699, precision of 0.9739, recall of 0.9675, and an F1 score of 0.9707. These metrics indicate the model's effectiveness in accurately detecting spam in Arabic SMS messages. Additionally, the paper provides visualizations of the confusion matrix, ROC curve, and training-validation loss graph to illustrate the model's performance. The implications of this research are significant for the field of Arabic text classification and spam detection. The proposed hybrid model offers a robust solution for accurately classifying Arabic SMS messages, which can be integrated into messaging platforms to enhance spam detection capabilities and improve user experience. Future work could explore data augmentation techniques, transfer learning, and advanced hybrid architectures to further enhance the model's performance and applicability.

Website Forum Diskusi Dengan Filter Spam Menggunakan Metode Clustering K-Medoids dan Metode Stemming Tala

In online learning, there are known terms of synchronous learning and asynchronous learning. Some common asynchronous learning activities are interacting with the Learning Management System (LMS), communicating using email, posting on discussion forums and reading articles. In this study, a discussion forum will be designed to support the online learning process. In discussion forums, there are often threads that are opened containing messages that are not useful or not related to the topic being discussed. These messages are often referred to as spam. To solve this problem, the K-Medoids clustering algorithm can be applied. Meanwhile, to carry out the stemming process, the Tala method will be used. The result of this research is a discussion forum website application that provides a spam filter feature so that discussion forums can be clean from spam. The website also supports the process of adding and storing new spam data lists, so that users can enter new spam words to improve spam detection accuracy.

Detecting Spam Email with Machine Learning Optimized with Harris Hawks optimizer (HHO) Algorithm

Email spam has been a big issue in recent years. As the percentage of internet users grows, so does the number of spam emails. Technologies are being used for illegitimate and immoral activities, such as phishing and robbery. As a consequence, it is essential to identify fraudulent spammers by employing machine learning techniques. This paper presents a novel spam classification technique that integrates the Harris Hawks optimizer (HHO) algorithm with the k-Nearest Neighbors algorithm (k-NN). The Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm motivated by Harris’ Hawks’ cooperative relations and surprises pounce pursue technique in nature. According to empirical results on the dataset, the suggested model can handle high-dimensional data (Spambase). The suggested model’s spam detection accuracy is compared to the numerous algorithms, including the Binary Dragonfly Algorithm (BDA), Equilibrium Optimizer (EO), Teaching-Learning-based Optimization, Seagull Optimization Algorithm (SO), and Marine Predators Algorithm (MPA). We found that the proposed technique trumps the other spam detection techniques investigated in this study in terms of classification accuracy. The experimental results showed that the proposed technique accuracy reaches %94.3.

A semantic-based classification approach for an enhanced spam detection

In this paper, we explore the use of a text semantic analysis to improve the accuracy of spam detection. We propose a method based on two semantic level analysis. In the first level, we categorize emails by specific domains (e.g., Health, Education, Finance, etc.) to enable a separate conceptual view for spams in each domain. In the second level, we combine a set of manually-specified and automatically-extracted semantic features for spam detection in each domain. These features are meant to summarize the email content into compact topics discriminating spam from non-spam emails in an efficient way. We show that the proposed method enables a better spam detection compared to existing methods based on bag-of-words (BoW) and semantic content, and leads to more interpretable results.

Using Reduced Set of Features to Detect Spam in Twitter Data with Decision Tree and KNN Classifier Algorithms

In social media, the users share their ideas, opinions to their neighbours and friends. Spammers send spam information to the genuine users to mislead them. This spam data is a very serious problem in social media sites. To detect spam messages in social media various spam detection methodologies are developed by researchers. The researchers used more number of features to construct the models. Generally the original dataset contains many irrelevant and redundant features. Such large amount of features reduces the spam detection accuracy. To improve the spam detection accuracy in social media networks, we have to reduce the meaningless attributes from high dimensional social media dataset. In order to reduce dimensionality of dataset, we have used one of the dimensionality reduction approach, called principal component analysis (PCA). After reducing the dimensionality of the dataset, the dataset samples are classified using Decision Tree Induction classifier algorithm and K Nearest Neighbour algorithm. In our proposed work these algorithms are used to check data samples are spam samples or ham samples. In this methodology, we have used Twitter dataset for testing proposed approach. Experimental results shows that KNN classifier outperforms compared to Decision tree classifier.

Improved email spam detection model based on support vector machines

Email has become extremely popular among people nowadays. In fact, it has been reported to be the cheapest, popular and fastest means of communication in recent times. Despite the huge benefits of emails, unfortunately its usage has been bedeviled with the huge presence of unsolicited and sometimes fraudulent emails which must be promptly detected and isolated through what is popularly referred to as spam detection system. Spam detection is highly needed to protect email users and prevents several negative usages to which emails have been subjected to of recent. Unfortunately, due to the adaptive nature of unsolicited emails through the use of mailing tools, the effectiveness of the spam detecting tools has often been limited and sometimes rendered ineffective, hence the need for better spam detection tools to achieve better spam detection accuracy. Several spam detection models have been proposed and tested in the literature, but still the reported accuracy indicated that there is still need for more work in this direction in order to achieve better accuracy. In this work, support vector machines-based model is proposed for spam detection while paying attention to appropriately search for the optimal parameters to achieve better performance. Experimental results show that the proposed model outperformed all the earlier proposed models on the same popular dataset employed in this work. Accuracy of 95.87 and 94.06% was obtained for training and testing sets, respectively. The 94.06% testing accuracy represents an improvement of 3.11% over the best reported model in the literature that had an accuracy of 91.22% on the same dataset used in this work.

Statistical Features-Based Real-Time Detection of Drifted Twitter Spam

Twitter spam has become a critical problem nowadays. Recent works focus on applying machine learning techniques for Twitter spam detection, which make use of the statistical features of tweets. In our labeled tweets data set, however, we observe that the statistical properties of spam tweets vary over time, and thus, the performance of existing machine learning-based classifiers decreases. This issue is referred to as “Twitter Spam Drift”. In order to tackle this problem, we first carry out a deep analysis on the statistical features of one million spam tweets and one million non-spam tweets, and then propose a novel Lfun scheme. The proposed scheme can discover “changed” spam tweets from unlabeled tweets and incorporate them into classifier's training process. A number of experiments are performed to evaluate the proposed scheme. The results show that our proposed Lfun scheme can significantly improve the spam detection accuracy in real-world scenarios.

Feature engineering for detecting spammers on Twitter: Modelling and analysis

Twitter is a social networking website that has gained a lot of popularity around the world in the last decade. This popularity made Twitter a common target for spammers and malicious users to spread unwanted advertisements, viruses and phishing attacks. In this article, we review the latest research works to determine the most effective features that were investigated for spam detection in the literature. These features are collected to build a comprehensive data set that can be used to develop more robust and accurate spammer detection models. The new data set is tested using popular classifiers (Naive Bayes, support vector machines, multilayer perceptron neural networks, Decision Trees, Random forests and k-Nearest Neighbour). The prediction performance of these classifiers is evaluated and compared based on different evaluation metrics. Moreover, a further analysis is carried out to identify the features that have higher impact on the accuracy of spam detection. Three different techniques are used and compared for this analysis: change of mean square error (CoM), information gain (IG) and Relief-F method. Top five features identified by each technique are used again to build the detection models. Experimental results show that most of the developed classifiers obtained high evaluation results based on the comprehensive data set constructed in this work. Experiments also reveal the important role of some features like the reputation of the account, average length of the tweet, average mention per tweet, age of the account, and the average time between posts in the process of identifying spammers in the social network.

SPADE: a social-spam analytics and detection framework

Social media such as Facebook, MySpace, and Twitter have become increasingly important for attracting millions of users. Consequently, spammers are increasing using such networks for propagating spam. Although existing filtering techniques such as collaborative filters and behavioral analysis filters are able to significantly reduce spam, each social network needs to build its own independent spam filter and support a spam team to keep spam prevention techniques current. To alleviate those problems, we propose a framework for spam analytics and detection which can be used across all social network sites. Specifically, the proposed framework SPADE has numerous benefits including (1) new spam detected on one social network can quickly be identified across social networks; (2) accuracy of spam detection will be improved through cross-domain classification and associative classification; (3) other techniques (such as blacklists and message shingling) can be integrated and centralized; (4) new social networks can plug into the system easily, preventing spam at an early stage. In SPADE, we present a uniform schema model to allow cross-social network integration. In this paper, we define the user, message, and web page model. Moreover, we provide an experimental study of real datasets from social networks to demonstrate the flexibility and feasibility of our framework. We extensively evaluated two major classification approaches in SPADE: cross-domain classification and associative classification. In cross-domain classification, SPADE achieved over 0.92 F-measure and over 91 % detection accuracy on web page model using Naive Bayes classifier. In associative classification, SPADE also achieved 0.89 F-measure on message model and 0.87 F-measure on user profile model, respectively. Both detection accuracies are beyond 85 %. Based on those results, our SPADE has been demonstrated to be a competitive spam detection solution to social media.

K-Means Clustering Scheme for Enhanced Spam Detection

In recent years, the problems of increasing spam mail on the internet are becomes a serious issue and difficult to detect. Furthermore, several e-mail classifications methods have been proposed and their performance is achieved. Although, Naive Bayes classifiers (NB) has been widely used in e-mail classification and is very simple and efficient, yet the problem of improving the accuracy and reducing misclassification rate still exists. Therefore, many researches are being carried out. These studies propose a hybrid scheme for e-mail classification based on Naive Bayes and K-means clustering to obtain better accuracy and reduce the misclassification rate of spam detection. The experiment of the proposed scheme was carried out using spam base benchmark dataset to evaluate the feasibility of the proposed method. The result of this hybrid led to enhance Naive Bayes classifiers and subsequently increase the accuracy of spam detection and reducing the misclassification rate. In addition, experimental results on spam base datasets show that the enhanced Naive Bayes (KNavie) significantly outperforms Naive Bayes and many other recent spam detection methods.

Сlassification methodology e-mails using neural networks

The effectiveness of email security is largely dependent on the accuracy of spam detection in leaves and roots. Existing recognition methods based on statistical analysis of text information, which significantly limits their ability to detect new types of spam and leaks. To overcome this shortcoming proposed classification methodology sheets based on semantic analysis using an electronic neural networks. A used as input parameters of the neural network in the frequency of meeting the letter of informative words in canonical form. It is shown that the best type of neural network model is Kohonen map, the main advantage of which is a high-speed training and the possibility of easy visualization of classification. This allows you to quickly react to new spam and howl leaks and conduct a final classification of letters by the user. The experiments confirmed the possibility of increasing the reliability of detection of 20-30%

Improved Spam Detection using DBSCAN and Advanced Digest Algorithm

E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. Nowadays, detecting and filtering are still the most feasible ways of fighting spam emails. There are many reasonably successful spam email filters in operation. The identification of spam plays an important role in current anti-spam mechanism. For improving the accuracy of spam detection, an improved Filtering technique is presented which is based on the Improved Digest algorithm and DBSCAN clustering algorithm. Using this technique, mails are represented using improved digest algorithm and then clustered using DBSCAN clustering algorithm. All similar emails which always categorized as spam are identified and clustered together where good mails that don’t look similar like other mails are not clustered. This method greatly improves the filtering accuracy against latest proposed algorithms by 30 % and improves the resistance of spam detection against increased obfuscation effort by spammers, while keeping miss-detection of good emails at a similar level of older filtering methods.

Using GMDH-based networks for improved spam detection and email feature analysis

Unsolicited or spam email has recently become a major threat that can negatively impact the usability of electronic mail. Spam substantially wastes time and money for business users and network administrators, consumes network bandwidth and storage space, and slows down email servers. In addition, it provides a medium for distributing harmful code and/or offensive content. In this paper, we explore the application of the GMDH (Group Method of Data Handling) based inductive learning approach in detecting spam messages by automatically identifying content features that effectively distinguish spam from legitimate emails. We study the performance for various network model complexities using spambase, a publicly available benchmark dataset. Results reveal that classification accuracies of 91.7% can be achieved using only 10 out of the available 57 attributes, selected through abductive learning as the most effective feature subset (i.e. 82.5% data reduction). We also show how to improve classification performance using abductive network ensembles (committees) trained on different subsets of the training data. Comparison with other techniques such as neural networks and naïve Bayesian classifiers shows that the GMDH-based learning approach can provide better spam detection accuracy with false-positive rates as low as 4.3% and yet requires shorter training time.

Targeting spam control on middleboxes: Spam detection based on layer-3 e-mail content classification

This paper proposes a spam detection technique, at the packet level (layer 3), based on classification of e-mail contents. Our proposal targets spam control implementations on middleboxes. E-mails are first pre-classified (pre-detected) for spam on a per-packet basis, without the need for reassembly. This, in turn, allows fast e-mail class estimation (spam detection) at receiving e-mail servers to support more effective spam handling on both inbound and outbound (relayed) e-mails. In this paper, the naïve Bayes classification technique is adapted to support both pre-classification and fast e-mail class estimation, on a per-packet basis. We focus on evaluating the accuracy of spam detection at layer 3, considering the constraints on processing byte-streams over the network, including packet re-ordering, fragmentation, overlapped bytes, and different packet sizes. Results show that the proposed layer-3 classification technique gives less than 0.5% false positive, which approximately equals the performance attained at layer 7. This shows that classifying e-mails at the packet level could differentiate non-spam from spam with high confidence for a viable spam control implementation on middleboxes.