Presentation2.pptx
- 1. VISVESVARAYA TECHNOLOGICAL UNIVERSITY KALPATARU INSTITUTE OF TECHNOLOGY DEPARTMENT OF computer SCIENCE AND ENGINEERING Internship Seminar on “SPAM DETECTION” Submitted by : Under the guidance of : RISHI S PROF. SANJAY KUMAR N V 1KI18CS058 Associate professor, Dept. of CSE.
- 2. ABSTRACT • E-mail spamming is the use of electronic messaging systems to send an unsolicited message(spam),especially advertising, as well as sending messages repeatedly on the same site. • Spam e-mail are message randomly sent to multiple addresses by all sorts of groups. • The sites attempt to steal your personal, electronic and financial information. • Bayesian Spam Detection/ Filtering is used to detect spam in an email.
- 3. INTRODUCTION • Major approaches adopted towards spam filtering include text analysis. • Spam e-mails are messages randomly sent to multiple addresses by all sorts of groups. • Spam e-mails can be defined as: 1. Anonymity 2. Mass Mailings 3. Unsolicited
- 4. SCOPE OF THE PROJECT • It provides sensitivity to the client and adapts well to the future spam techniques. • It considers a complete message instead of single words with respect to its organization. • It increases Security and Control. • It reduces IT Administration Costs. • It also reduce Network Resource Costs.
- 5. FLOW CHART
- 6. SPAM DETECTION • Whenever you submit details about your email or contact number on any platform, it has become easy for those platforms to market their products by advertising them by sending emails or by sending messages directly to your contact number. • This results in lots of spam alerts and notifications in your inbox. • This is where the task of spam detection comes in. • Spam detection means detecting spam messages or emails by understanding text content so that you can only receive notifications about messages or emails that are very important to you.
- 7. SPAM DETECTION • If spam messages are found, they are automatically transferred to a spam folder and you are never notified of such alerts. • This helps to improve the user experience, as many spam alerts can bother many users.
- 8. OBJECTIVE The objective of identification of Spam e-mails are: • To give knowledge to the user about the fake e-mails and relevant e-mails. • To classify that mail spam or not.
- 9. METHEDOLOGY NAVIE BAYES CLASSIFIER • Simple probabilistic classifier that calculates a set of probabilities by counting the frequency and combination of values in a given dataset. • Represent as a vector of feature values. • It is very useful to classify the e-mails properly. • The precision and recallof this method is known to be very effective.
- 10. IMPLEMENTATION • We have calculated the feature vector by using a dictionary and extracted the probabilities of word that appear to be spam or non- spam. • Using that feature vector naïve bayes algorithm works by comparing the trained data to test the data. • Dataset is a collection of data or related information that is composed for separate elements. • In this research, two datasets are be used to evaluate the performance of naive bayes algorithm to filter e-mail spam.
- 11. CONCLUSION • We are able to classify the emails as spam or non-spam.With high number of emails if people using the system it will be difficult to handle all possible mails. • Spam filter is capable of filtering mails according to the domain names listed in black list only. • At this stage is not able to filter the spams on the basis of the its contents or som other criteria.
- 12. REFERENCES 1.H. Faris, A. M. Al-Zoubi, A. A. Heidari et al., “An intelligent system for spam detection and identification of the most relevant features based on evolutionary random weight networks,” Information Fusion, vol. 48, pp. 67– 83, 2019.View at: Publisher Site | Google Scholar 2.E. Blanzieri and A. Bryl, “A survey of learning-based techniques of email spam filtering,” Artificial Intelligence Review, vol. 29, no. 1, pp. 63–92, 2008.View at: Publisher Site | Google Scholar 3.A. Alghoul, S. Al Ajrami, G. Al Jarousha, G. Harb, and S. S. Abu-Naser, “Email classification using artificial neural network,” International Journal for Academic Development, vol. 2, 2018.View at: Google Scholar 4.N. Udayakumar, S. Anandaselvi, and T. Subbulakshmi, “Dynamic malware analysis using machine learning algorithm,” in Proceedings of the 2017 International Conference on Intelligent Sustainable Systems (ICISS), IEEE, Palladam, India, December 2017.View at: Google Scholar
- 13. Thank you