Spam detection
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Benchmarks
Most implemented
GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction
Grouped Pointwise Convolutions Reduce Parameters in Convolutional Neural Networks
Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text
Weight Poisoning Attacks on Pre-trained Models
Cost-Aware Robust Tree Ensembles for Security Applications
GANs for Semi-Supervised Opinion Spam Detection
Papers
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 detection