Enhancing the Naive Bayes Spam Filter Through Intelligent Text Modification Detection
| Title | Enhancing the Naive Bayes Spam Filter Through Intelligent Text Modification Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Peng, W., Huang, L., Jia, J., Ingram, E. |
| Conference Name | 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) |
| Date Published | aug |
| Keywords | Bayes methods, Bayes Spam Filter, Bayesian Poisoning, computer security, e-mail filters, Email, email communication, feature extraction, Human Behavior, intelligent text modification detection, Internet based information exchange venues, learning (artificial intelligence), machine learning, machine learning algorithm, machine learning algorithms, Metrics, naïve Bayes classifier, Naive Bayes Spam Filter, pattern classification, pubcrawl, Python algorithm, Scalability, social networks, spam, spam classification, spam detection, spam emails, spam filter, spam score, Spamassassin, Testing, text analysis, text classification, text modifications, unsolicited e-mail, Unsolicited electronic mail |
| Abstract | Spam emails have been a chronic issue in computer security. They are very costly economically and extremely dangerous for computers and networks. Despite of the emergence of social networks and other Internet based information exchange venues, dependence on email communication has increased over the years and this dependence has resulted in an urgent need to improve spam filters. Although many spam filters have been created to help prevent these spam emails from entering a user's inbox, there is a lack or research focusing on text modifications. Currently, Naive Bayes is one of the most popular methods of spam classification because of its simplicity and efficiency. Naive Bayes is also very accurate; however, it is unable to correctly classify emails when they contain leetspeak or diacritics. Thus, in this proposes, we implemented a novel algorithm for enhancing the accuracy of the Naive Bayes Spam Filter so that it can detect text modifications and correctly classify the email as spam or ham. Our Python algorithm combines semantic based, keyword based, and machine learning algorithms to increase the accuracy of Naive Bayes compared to Spamassassin by over two hundred percent. Additionally, we have discovered a relationship between the length of the email and the spam score, indicating that Bayesian Poisoning, a controversial topic, is actually a real phenomenon and utilized by spammers. |
| URL | https://ieeexplore.ieee.org/document/8455990 |
| DOI | 10.1109/TrustCom/BigDataSE.2018.00122 |
| Citation Key | peng_enhancing_2018 |
- spam filter
- pubcrawl
- Python algorithm
- Scalability
- social networks
- spam
- spam classification
- spam detection
- spam emails
- pattern classification
- spam score
- Spamassassin
- testing
- text analysis
- text classification
- text modifications
- unsolicited e-mail
- Unsolicited electronic mail
- intelligent text modification detection
- Bayes Spam Filter
- Bayesian Poisoning
- computer security
- e-mail filters
- email communication
- feature extraction
- Human behavior
- Bayes methods
- Internet based information exchange venues
- learning (artificial intelligence)
- machine learning
- machine learning algorithm
- machine learning algorithms
- Metrics
- naïve Bayes classifier
- Naive Bayes Spam Filter