Papers Spam detection
“Spam detection” 태그가 달린 논문 123편 · 필터 해제
Integration of AI in Cybersecurity: Current Trends with a Focused Look at Intrusion Detection Applications
Artificial Intelligence (AI) is widely adopted today for its ability to detect patterns, automate tasks, and reduce time and cost across various applications. Its integration into Cybersecurity has garnered significant a…
Intrusion DetectionFederated LearningSpam detectionReal-time and Zero-footprint Bag of Synthetic Syllables Algorithm for E-mail Spam Detection Using Subject Line and Short Text Fields
Contemporary e-mail services have high availability expectations from the customers and are resource-strained because of the high-volume throughput and spam attacks. Deep Machine Learning architectures, which are resourc…
Spam detectionDetecting LLM-Generated Spam Reviews by Integrating Language Model Embeddings and Graph Neural Network
The rise of large language models (LLMs) has enabled the generation of highly persuasive spam reviews that closely mimic human writing. These reviews pose significant challenges for existing detection systems and threate…
Graph Neural NetworkNode ClassificationFeature EngineeringSpam detectionLeveraging Big Data Frameworks for Spam Detection in Amazon Reviews
In this digital era, online shopping is common practice in our daily lives. Product reviews significantly influence consumer buying behavior and help establish buyer trust. However, the prevalence of fraudulent reviews u…
Spam detectionGCC-Spam: Spam Detection via GAN, Contrastive Learning, and Character Similarity Networks
The exponential growth of spam text on the Internet necessitates robust detection mechanisms to mitigate risks such as information leakage and social instability. This work addresses two principal challenges: adversarial…
Contrastive LearningSpam detectionText DetectionText-ADBench: Text Anomaly Detection Benchmark based on LLMs Embedding
Text anomaly detection is a critical task in natural language processing (NLP), with applications spanning fraud detection, misinformation identification, spam detection and content moderation, etc. Despite significant a…
Anomaly DetectionFraud DetectionSpam detectionCyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced Datasets
Cybersecurity has become essential worldwide and at all levels, concerning individuals, institutions, and governments. A basic principle in cybersecurity is to be always alert. Therefore, automation is imperative in proc…
Anomaly DetectionBinary ClassificationFraud DetectionIntrusion Detection+2Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models
Email spam detection is a critical task in modern communication systems, essential for maintaining productivity, security, and user experience. Traditional machine learning and deep learning approaches, while effective i…
Spam detectionTraditional Spam DetectionZero-Shot LearningA Comprehensive Analysis of Adversarial Attacks against Spam Filters
Deep learning has revolutionized email filtering, which is critical to protect users from cyber threats such as spam, malware, and phishing. However, the increasing sophistication of adversarial attacks poses a significa…
Deep LearningSentenceSpam detectionQuickCharNet: An Efficient URL Classification Framework for Enhanced Search Engine Optimization
The uniform resource locator (URL) conveys essential information about a page’s topic, authority, and security, which significantly influences its ranking in search engine results. However, many existing URL classificati…
ClassificationEfficient Neural NetworkMarketingSpam detection+1Judgment of Thoughts: Courtroom of the Binary Logical Reasoning in Large Language Models
This paper proposes a novel prompt engineering technique called Judgment of Thought (JoT) that is specifically tailored for binary logical reasoning tasks. JoT employs three roles$\unicode{x2014}$lawyer, prosecutor, and …
Fake News DetectionLanguage ModelingLanguage ModellingLarge Language Model+4Different Victims, Same Layout: Email Visual Similarity Detection for Enhanced Email Protection
In the pursuit of an effective spam detection system, the focus has often been on identifying known spam patterns either through rule-based detection systems or machine learning (ML) solutions that rely on keywords. Howe…
Spam detectionConditional Semi-Supervised Data Augmentation for Spam Message Detection with Low Resource Data
Several machine learning schemes have attempted to perform the detection of spam messages. However, those schemes mostly require a huge amount of labeled data. The existing techniques addressing the lack of data availabi…
Data AugmentationSpam detectionOnline detection and infographic explanation of spam reviews with data drift adaptation
Spam reviews are a pervasive problem on online platforms due to its significant impact on reputation. However, research into spam detection in data streams is scarce. Another concern lies in their need for transparency. …
Drift DetectionSpam detectionSMS Spam Detection and Classification to Combat Abuse in Telephone Networks Using Natural Language Processing
In the modern era, mobile phones have become ubiquitous, and Short Message Service (SMS) has grown to become a multi-million-dollar service due to the widespread adoption of mobile devices and the millions of people who …
BlockingSpam detectionGenshin: General Shield for Natural Language Processing with Large Language Models
Large language models (LLMs) like ChatGPT, Gemini, or LLaMA have been trending recently, demonstrating considerable advancement and generalizability power in countless domains. However, LLMs create an even bigger black b…
Spam detectionA Robust Autoencoder Ensemble-Based Approach for Anomaly Detection in Text
Anomaly detection (AD) is a fast growing and popular domain among established applications like vision and time series. We observe a rich literature for these applications, but anomaly detection in text is only starting …
Anomaly DetectionBenchmarkingSentiment AnalysisSpam detectionExplainableDetector: Exploring Transformer-based Language Modeling Approach for SMS Spam Detection with Explainability Analysis
SMS, or short messaging service, is a widely used and cost-effective communication medium that has sadly turned into a haven for unwanted messages, commonly known as SMS spam. With the rapid adoption of smartphones and I…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Language ModelingLanguage Modelling+2Hierarchical mixture of discriminative Generalized Dirichlet classifiers
This paper presents a discriminative classifier for compositional data. This classifier is based on the posterior distribution of the Generalized Dirichlet which is the discriminative counterpart of Generalized Dirichlet…
Mixture-of-ExpertsSpam detectionSpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam Detection
In this study, we introduce SpamDam, a SMS spam detection framework designed to overcome key challenges in detecting and understanding SMS spam, such as the lack of public SMS spam datasets, increasing privacy concerns o…
Adversarial RobustnessBackdoor AttackFederated LearningPrivacy Preserving+1