Wu et al., 2021 - Google Patents
A novel real-time anti-spam frameworkWu et al., 2021
View PDF- Document ID
- 4455050866058470752
- Author
- Wu D
- Shi W
- Ma X
- Publication year
- Publication venue
- ACM Transactions on Internet Technology (TOIT)
External Links
Snippet
As one of the most pervasive current modes of communication, email needs to be fast and reliable. However, spammers and attackers use it as a primary channel to conduct illegal activities. Although many approaches have been developed and evaluated for spam …
- 238000001514 detection method 0 abstract description 39
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
- G06Q10/107—Computer aided management of electronic mail
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30861—Retrieval from the Internet, e.g. browsers
- G06F17/30864—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems
- G06F17/30867—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems with filtering and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
- H04L63/1425—Traffic logging, e.g. anomaly detection
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
- H04L63/1416—Event detection, e.g. attack signature detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
- G06F21/55—Detecting local intrusion or implementing counter-measures
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/54—Store-and-forward switching systems
- H04L12/58—Message switching systems, e.g. electronic mail systems
- H04L12/585—Message switching systems, e.g. electronic mail systems with filtering and selective blocking capabilities
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L51/00—Arrangements for user-to-user messaging in packet-switching networks, e.g. e-mail or instant messages
- H04L51/12—Arrangements for user-to-user messaging in packet-switching networks, e.g. e-mail or instant messages with filtering and selective blocking capabilities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/54—Store-and-forward switching systems
- H04L12/58—Message switching systems, e.g. electronic mail systems
- H04L12/5885—Message switching systems, e.g. electronic mail systems with provisions for tracking the progress of a message
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Aljabri et al. | Machine learning-based social media bot detection: a comprehensive literature review | |
| Dada et al. | Machine learning for email spam filtering: review, approaches and open research problems | |
| Bazzaz Abkenar et al. | A hybrid classification method for Twitter spam detection based on differential evolution and random forest | |
| Somesha et al. | Classification of phishing email using word embedding and machine learning techniques | |
| Jose et al. | Detecting spammers on social network through clustering technique | |
| Maqsood et al. | An intelligent framework based on deep learning for SMS and e‐mail spam detection | |
| Abkenar et al. | Twitter spam detection: A systematic review | |
| Li et al. | An empirical study of supervised email classification in Internet of Things: practical performance and key influencing factors | |
| Fatima et al. | An optimized approach for detection and classification of spam email’s using ensemble methods | |
| Agarwal et al. | A novel approach for spam detection using natural language processing with AMALS models | |
| Benabbou et al. | Fake accounts detection system based on bidirectional gated recurrent unit neural network | |
| Wu et al. | A novel real-time anti-spam framework | |
| Gupta et al. | A comprehensive comparative study of machine learning classifiers for spam filtering | |
| Alkhdour et al. | A new technique for detecting email spam risks using LSTM-particle swarm optimization algorithms | |
| Alhawamleh | Advanced spam filtering in electronic mail using hybrid the mini batch k-means normalized mutual information feature elimination with elephant herding optimization technique | |
| Chien et al. | Email Feature Classification and Analysis of Phishing Email Detection Using Machine Learning Techniques | |
| Kaushal et al. | Fairness-driven federated learning-based spam email detection using clustering techniques | |
| Sharma et al. | A survey of email spam filtering methods | |
| Larabi | Improving spam email detection using deep recurrent neural network | |
| Elakkiya et al. | Stratified hyperparameters optimization of feed-forward neural network for social network spam detection (SON2S) E. Elakkiya, S. Selvakumar | |
| Bhattacharyya et al. | Machine Learning-Based Detection and Categorization of Malicious Accounts on Social Media | |
| AlShaikh et al. | Supervised methods of machine learning for email classification: a literature survey | |
| Sharma et al. | SOCIAL MEDIA SPAM DETECTION USING DIFFERENT TEXT FEATURE SELECTION TECHNIQUE AND MACHINE LEARNING. | |
| Borse et al. | State of the art on Twitter spam detection | |
| Ponnuchamy et al. | Comparative analysis of eminent algorithms for detecting ham and spam contents in osn |