Spam Spyder (Spam Detection using MI & AI)

Anuja, Kathwate and Dnyaneshwari, Chafle and Sharda, Moharle and Mansi, Singh and Prof. Rina, Shirpurkar (2024) Spam Spyder (Spam Detection using MI & AI). International Journal of Trend in Scientific Research and Development, 8 (5). pp. 999-1007. ISSN 2456-6470

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Abstract

Spam emails and messages are a major problem for both users and organizations in the digital era. With the help of machine learning techniques and the Spyder Integrated Development Environment (IDE), this project seeks to create a reliable spam detection system. Classifying messages as either'spam' or 'ham' (non-spam) with high accuracy is the main goal. The project starts with gathering and preparing a dataset comprising tagged spam and non-spam message instances. Text normalization, tokenization, and feature extraction are important preprocessing tasks. Text input is transformed into numerical features appropriate for machine learning models using methods like word embeddings and Term Frequency-Inverse Document Frequency. Using sophisticated data filtering algorithms and web-crawling techniques, Spam Spyder is a system that finds and analyzes spam content on the internet. The proliferation of uninvited and destructive messages, popularly known as "spam," has become a serious concern for online platforms, businesses, and users due to the exponential growth of digital communication and user-generated content. In order to combat this, Spam Spyder automates the process of identifying spam on websites, social media networks, and other online platforms. To detect spammy content, the system combines machine learning, natural language processing (NLP), and pattern recognition. Through website crawling and scanning for specified spam traits (such dubious links, misleading wording, or excessive keyword repetition), Spam Spyder is able to identify and classify. Now a days communication plays a major role in every thing be it professional or personal. Email communication service is being used extensively because of its free use services, low-cost operations, accessibility, and popularity. This security flaw is being exploited by some businesses and ill-motivated persons for advertising, phishing, malicious purposes, and finally fraud. This produces a kind of email category called SPAM. Spam refers to any email that contains an advertisement, unrelated and frequent emails. These emails are increasing day by day in numbers. Studies show that around 55 percent of all emails are some kind of spam. A lot of effort is being put into this by service providers. Moreover, the spam detection of service provider scan ever be aggressive with classification because it may cause potential information loss to in case of a misclassification.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	26 Oct 2024 09:13
Last Modified:	26 Oct 2024 09:13
URI:	http://eprints.umsida.ac.id/id/eprint/14424

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