Mbah, 2017 - Google Patents
A phishing e-mail detection approach using machine learning techniquesMbah, 2017
View PDF- Document ID
- 9366845527360645907
- Author
- Mbah K
- Publication year
External Links
Snippet
According to APWG reports of 2014 and 2015, the number of unique Phishing e-mail reports received from consumers has increased tremendously from 68270 e-mails in October 2014 to 106421 e-mails in September 2015. This significant increase is a proof of the existence of …
- 238000000034 method 0 title abstract description 67
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
- G06F17/30861—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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Jain et al. | A survey of phishing attack techniques, defence mechanisms and open research challenges | |
| Kumar et al. | Phishing website classification and detection using machine learning | |
| Gupta et al. | Fighting against phishing attacks: state of the art and future challenges | |
| Mahajan et al. | Phishing website detection using machine learning algorithms | |
| Jain et al. | A novel approach to protect against phishing attacks at client side using auto-updated white-list | |
| Goenka et al. | A comprehensive survey of phishing: Mediums, intended targets, attack and defence techniques and a novel taxonomy | |
| Khonji et al. | Phishing detection: a literature survey | |
| US9942250B2 (en) | Network appliance for dynamic protection from risky network activities | |
| Buber et al. | Feature selections for the machine learning based detection of phishing websites | |
| US8763116B1 (en) | Detecting fraudulent activity by analysis of information requests | |
| Patil et al. | Survey on malicious web pages detection techniques | |
| Bhardwaj et al. | Privacy-aware detection framework to mitigate new-age phishing attacks | |
| Basnet et al. | Learning to detect phishing URLs | |
| Kalpakis et al. | OSINT and the Dark Web | |
| Kumar Birthriya et al. | A comprehensive survey of phishing email detection and protection techniques | |
| Gupta et al. | Emerging phishing trends and effectiveness of the anti-phishing landing page | |
| Besel et al. | Full cycle analysis of a large-scale botnet attack on Twitter | |
| Binsaeed et al. | Detecting spam in twitter microblogging services: A novel machine learning approach based on domain popularity | |
| Mbah | A phishing e-mail detection approach using machine learning techniques | |
| Swarnalatha et al. | Real-time threat intelligence-block phising attacks | |
| Alnajim et al. | An approach to the implementation of the anti-phishing tool for phishing websites detection | |
| Paturi et al. | Detection of phishing attacks using visual similarity model | |
| Thaker et al. | Detecting phishing websites using data mining | |
| Marchal | DNS and semantic analysis for phishing detection | |
| Gautam et al. | Phishing prevention techniques: past, present and future |