Event Details
SPAM Email Detection using Voting with Machine Learning Algorithms
Presenter: Amir Khatibzadeh
Supervisor:
Date: Tue, April 11, 2023
Time: 11:00:00 - 11:45:00
Place: ZOOM - Please see below.
ABSTRACT
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ABSTRACT
Spam detection is crucial in modern communications, including email and social media. Despite the existence of services to detect spam, spammers have devised creative techniques and approaches to bypass these services and deliver spam messages to their targets. Finding better methods to detect spam is an ongoing battle between spammers and detection services. Machine learning (ML) techniques have been shown to be effective in detecting anomalies and spam. A key metric is the rate of detecting spam messages correctly. However, a low rate of marking legitimate messages or emails as spam (false positive) is essential. In this thesis, a combination of ML models and a voting mechanism is used to obtain a high spam detection rate with a low false positive rate.