FRAUD SMS OR EMAIL DETECTION AND CLASSIFICATION USING MACHINE LEARNING

Registration ID: IJNRD_224557

Published ID: IJNRD2407034

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LAKSHMIKANTH REDDY P, P Satish Kumar, Mannuru malleswari

FRAUD SMS & EMAIL DETECTION, CLASSIFICATIONS USING MACHINE LEARNING, TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY

With the increasing digitalization of society, communication via electronic channels such as email, text messages, and social media has become ubiquitous. However, alongside the convenience and connectivity that digital platforms offer, there is a rising threat from cybercriminals who exploit these mediums to perpetrate spam and fraudulent activities. Spam, defined as unsolicited and often deceptive messages, poses significant risks to users by attempting to lure them into revealing sensitive information or engaging in malicious actions. This paper proposes a machine learning-based approach to mitigate the impact of spam and protect individuals from falling victim to cyber fraud. The methodology leverages machine learning algorithms, specifically employing the term frequency-inverse document frequency (TF-IDF) vectorizer and the Naïve Bayes classifier. These algorithms are well-suited for text classification tasks, where the goal is to distinguish between legitimate messages and spam. The TF-IDF vectorizer transforms textual data into numerical representations based on the frequency of terms within documents, while the Naïve Bayes classifier applies probabilistic principles to classify messages as either spam or non-spam based on these representations. To validate the effectiveness of the proposed approach, a comprehensive dataset of labeled messages was compiled and uploaded to Kaggle for model training and evaluation. The dataset includes a diverse range of textual content that simulates real-world communication patterns susceptible to spam attacks. Through rigorous experimentation and iterative model refinement, the system achieved impressive performance metrics, demonstrating a 95% accuracy rate and a 100% precision rate in identifying spam messages. Furthermore, the implementation of the model involves creating a user-friendly interface hosted on a local web server, developed using the PyCharm IDE. This interface allows users to conveniently input suspicious messages for immediate classification. If a message is flagged as potential spam, users are alerted to exercise caution, thereby empowering them to make informed decisions about the messages they receive. Beyond technical implementation, the paper discusses the broader implications of combating cyber fraud through machine learning. Despite advancements in security measures, cybercriminals continue to exploit vulnerabilities in digital communication channels, highlighting the critical need for proactive defense mechanisms. By integrating machine learning into everyday cybersecurity practices, individuals and organizations can augment their defenses against evolving spam and phishing tactics. Moreover, the study emphasizes the importance of public awareness andeducation in recognizing and mitigating cyber threats. While technological solutions provide robust defense mechanisms, they must be complemented by user vigilance and informed cybersecurity practices. Initiatives aimed at raising awareness about phishing scams and fraudulent communications can empower users to identify red flags and take preventive actions, thereby reducing the success rate of cybercriminals. In conclusion, this research underscores the efficacy of machine learning in enhancing cybersecurity efforts against spam and cyber fraud. By harnessing the power of data-driven algorithms, coupled with user education and awareness, individuals can better safeguard their digital identities and financial assets in an increasingly interconnected world. The findings contribute to the ongoing discourse on cybersecurity strategies, advocating for a holistic approach that combines technological innovation with human vigilance to combat emerging cyber threats effectively.

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LAKSHMIKANTH REDDY P, P Satish Kumar, & Mannuru malleswari (July-2024). FRAUD SMS OR EMAIL DETECTION AND CLASSIFICATION USING MACHINE LEARNING. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(7), a329-a338. https://ijnrd.org/papers/IJNRD2407034.pdf

Volume 9 Issue 7, July-2024

Pages : a329-a338

Paper Reg. ID: IJNRD_224557

Published Paper Id: IJNRD2407034

Research Area: Computer Engineering 

Author Type: Indian Author

Country: Cuddapah, Andra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2407034.pdf

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