Predicting Spam Messages Using Back Propagation Neural Network

Author:

Jain Ankit KumarORCID,Goel Diksha,Agarwal Sanjli,Singh Yukta,Bajaj Gaurav

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Science Applications

Reference25 articles.

1. Goel, D., & Jain, A. K. (2018). Mobile phishing attacks and defence mechanisms: state of art and open research challenges. Computers & Security,73, 519–544.

2. Gudkova, D. (2018). Spam and Phishing in 2017. Securelist—Kaspersky Lab’s Cyber threat Research and Reports, 15 Feb, 2018, http://securelist.com/spam-and-phishing-in-2017/83833/ . Retrived April, 2018.

3. Crowe, J. (2017). Must-Know Phishing Statistics 2017. Barkly Endpoint Security Blog. http://blog.barkly.com/phishing-statistics-2017 . Retrived April 2018.

4. Goel, D., & Jain, A. K. (2017). Smishing-classifier: A novel framework for detection of Smishing attack in mobile environment. In International conference on next generation computing technologies (pp. 502–512). Singapore: Springer.

5. Almeida, T. A., Silva, T. P., Santos, I., & Hidalgo, J. M. G. (2016). Text normalization and semantic indexing to enhance Instant Messaging and SMS spam filtering. Knowledge-Based Systems,108, 25–32.

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