Spam detection by using machine learning based binary classifier

Abstract

<span lang="EN-US">Because <span lang="EN-US">of its ease of use and speed compared to other communication applications, email is the most commonly used communication application worldwide. However, a major drawback is its inability to detect whether mail content is either spam or ham. There is currently an increasing number of cases of stealing personal information or phishing activities via email. This project will discuss how machine learning can help in spam detection. Machine learning is an artificial intelligence application that provides the ability to automatically learn and improve data without being explicitly programmed. A binary classifier will be used to classify the text into two different categories: spam and ham. This research shows the machine learning algorithm in the Azure-based platform predicts the score more accurately compared to the machine learning algorithm in visual studio, hybrid analysis and JoeSandbox cloud.</span></span>

Applied Improved Canny Edge Detection for Diagnosis Medical Images of Human Brain Tumors

“…Preprocessing, display, enhancement, in addition to information extraction, represent the 3 general processes which all kinds of the data have to go through in the case of utilizing digital approaches. Pixels are the picture elements that make up digital images [3] [4]. Pixels are usually arranged in an ordered rectangular array.…”

Deteksi Email Spam menggunakan Algoritma Convolutional Neural Network (CNN)

“…PENDAHULUAN Indonesia saat ini menduduki peringkat ke-8 di dunia dalam hal volume pengiriman spam [1]. Email, sebagai salah satu aplikasi komunikasi yang paling luas digunakan di seluruh dunia, memiliki kelemahan utama yaitu ketidakmampuannya dalam membedakan secara efisien antara isi email yang merupakan spam dan yang bukan spam (ham) [2]. Kejahatan siber yang termasuk pengiriman malware melalui email menjadi salah satu tantangan serius dalam ranah ini.…”

“…Penelitian terdahulu memanfaatkan algoritma N-Gram dan Naive Bayes melalui REST API dan berhasil mencapai tingkat akurasi deteksi hingga 98% [1]. Penelitian lain yang menggunakan algoritma Binary Classifier berhasil mencapai akurasi hingga 99% [2]. Penelitian lain juga mengevaluasi penggunaan algoritma K-NN dan SVM untuk tujuan yang sama [3], [4].…”