Sajedi et al., 2016 - Google Patents

Sms spam filtering using machine learning techniques: A survey

Sajedi et al., 2016

Document ID: 7496686590088141620
Author: Sajedi H; Parast G; Akbari F
Publication year: 2016
Publication venue: Machine Learning Research

External Links

Cited by

Snippet

Objective: To report a review of various machine learning and hybrid algorithms for detecting SMS spam messages and comparing them according to accuracy criterion. Data sources: Original articles written in English found in Sciencedirect. com, Google-scholar. com …

Continue reading at www.academia.edu (PDF) (other versions)

238000000034 method 0 title abstract description 29

Classifications

- 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
- G06F17/30705—Clustering or classification
- G06F17/30707—Clustering or classification into predefined classes
- 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
- G06F17/30705—Clustering or classification
- G06F17/3071—Clustering or classification including class or cluster creation or modification
- 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
- 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
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- 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
- 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
- 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
- G06Q30/00—Commerce, e.g. shopping or e-commerce
- G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
- 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
- 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
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- 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

Similar Documents

Publication	Publication Date	Title
Abdulhamid et al.	2017	A review on mobile SMS spam filtering techniques
Sajedi et al.	2016	Sms spam filtering using machine learning techniques: A survey
Sonowal et al.	2018	SmiDCA: an anti-smishing model with machine learning approach
Sjarif et al.	2019	SMS spam message detection using term frequency-inverse document frequency and random forest algorithm
Venkatraman et al.	2020	Spam e-mail classification for the Internet of Things environment using semantic similarity approach
El-Alfy et al.	2016	Spam filtering framework for multimodal mobile communication based on dendritic cell algorithm
Ismail et al.	2022	Efficient E‐Mail Spam Detection Strategy Using Genetic Decision Tree Processing with NLP Features
Ramanathan et al.	2012	phishGILLNET—phishing detection methodology using probabilistic latent semantic analysis, AdaBoost, and co-training
US8424091B1 (en)	2013-04-16	Automatic local detection of computer security threats
US10848448B2 (en)	2020-11-24	Spam filtering in multimodal mobile communication
Somesha et al.	2022	Classification of phishing email using word embedding and machine learning techniques
Maqsood et al.	2023	An intelligent framework based on deep learning for SMS and e‐mail spam detection
Al-Hasan et al.	2015	Dendritic cell algorithm for mobile phone spam filtering
Saka et al.	2022	Context-based clustering to mitigate phishing attacks
Gautam et al.	2023	Email-based cyberstalking detection on textual data using multi-model soft voting technique of machine learning approach
Kaya et al.	2016	A novel approach for spam email detection based on shifted binary patterns
Iyengar et al.	2017	Integrated SPAM detection for multilingual emails
Alanazi et al.	2024	A hybrid NLP and domain validation technique for disposable email detection
Nivedha et al.	2022	Detection of email spam using Natural Language Processing based Random Forest approach
Brabec et al.	2023	A modular and adaptive system for business email compromise detection
Gad et al.	2015	Email filtering based on supervised learning and mutual information feature selection
Lee et al.	2017	An online subject-based spam filter using natural language features
Mujtaba et al.	2018	Detection of suspicious terrorist emails using text classification: A review
Adebayo et al.	2015	Static Code Analysis of permission-based features for android malware classification using apriori algorithm with Particle Swarm Optimization
Santos et al.	2011	Spam filtering through anomaly detection