Review on Effective Email Classification for Spam and Non Spam Detection on Various Machine Learning Techniques

Some time email receiver or user receives a email which he does not intended to receive or accept, these kind of emails are nothing but spam emails. In other words the unsolicited bulk email is nothing but the spam. Numbers of emails users are increasing day by day, email users communicate around the world using email and internet. Now days a large volumes of spam emails are causing serious problem for Internet service and Internet users. This affects or degrades user search experience, which assists propagation of virus in network or grid, this will increases load on traffic in the network. It also wastes valuable time of user, user’s energy for appropriate emails among the spam emails. To avoiding such spam there are so many traditional anti spam techniques includes, rule based system, White list and DNS black holes, IP blacklist, Heuristic based filter, Bayesian based filters. All these techniques are based on links of the mail or content of the email. In this paper, we conferred our study on various existing techniques on spam detection and finding the effective, accurate, and reliable spam detection technique. DOI: 10.17762/ijritcc2321-8169.150315

Identifying spam e-mail messages using an intelligence algorithm

During the past few years, there have been growing interests in using email for delivering various types of messages such as social, financial, etc. There are also people who use email messages to promote products and services or even to do criminal activities called Spam email. These unwanted messages are sent to different target population for different purposes and there is a growing interest to develop methods to filter such email messages. This paper presents a method to filter Spam email messages based on the keyword pattern. In this article, a multi-agent filter trade based on the Bayes rule, which has benefit of using the users’ interest, keywords and investigation the message content according to its topic, has been used. Then Nested Neural Network has been used to detect the spam messages. To check the authenticity of this proposed method, we test it for a couple of email messages, so that it could determine spams and hams from each other, effectively. The result shows the superiority of this method over the previous ones including filters with Multi-Layer Perceptron that detect spams

Penanganan Fitur Kontinyu dengan Feature Discretization Berbasis Expectation Maximization Clustering untuk Klasifikasi Spam Email Menggunakan Algoritma ID3

Pemanfaatan jaringan internet saat ini berkembang begitu pesatnya, salah satunya adalah pengiriman surat elektronik atau email. Akhir-akhir ini ramai diperbincangkan adanya spam email. Spam email adalah email yang tidak diminta dan tidak diinginkan dari orang asing yang dikirim dalam jumlah besar ke mailing list, biasanya beberapa dengan sifat komersial. Adanya spam ini mengurangi produktivitas karyawan karena harus meluangkan waktu untuk menghapus pesan spam. Untuk mengatasi permasalahan tersebut dibutuhkan sebuah filter email yang akan mendeteksi keberadaan spam sehingga tidak dimunculkan pada inbox mail. Banyak peneliti yang mencoba untuk membuat filter email dengan berbagai macam metode, tetapi belum ada yang menghasilkan akurasi maksimal. Pada penelitian ini akan dilakukan klasifikasi dengan menggunakan algoritma Decision Tree Iterative Dicotomizer 3 (ID3) karena ID3 merupakan algoritma yang paling banyak digunakan di pohon keputusan, terkenal dengan kecepatan tinggi dalam klasifikasi, kemampuan belajar yang kuat dan konstruksi mudah. Tetapi ID3 tidak dapat menangani fitur kontinyu sehingga proses klasifikasi tidak bisa dilakukan. Pada penelitian ini, feature discretization berbasis Expectation Maximization (EM) Clustering digunakan untuk merubah fitur kontinyu menjadi fitur diskrit, sehingga proses klasifikasi spam email bisa dilakukan. Hasil eksperimen menunjukkan ID3 dapat melakukan klasifikasi spam email dengan akurasi 91,96% jika menggunakan data training 90%. Terjadi peningkatan sebesar 28,05% dibandingkan dengan klasifikasi ID3 menggunakan binning

Efficient and Trustworthy Review/Opinion Spam Detection

The most common mode for consumers to express their level of satisfaction with their purchases is through online ratings, which we can refer as Online Review System. Network analysis has recently gained a lot of attention because of the arrival and the increasing attractiveness of social sites, such as blogs, social networking applications, micro blogging, or customer review sites. The reviews are used by potential customers to find opinions of existing users before purchasing the products. Online review systems plays an important part in affecting consumers' actions and decision making, and therefore attracting many spammers to insert fake feedback or reviews in order to manipulate review content and ratings. Malicious users misuse the review website and post untrustworthy, low quality, or sometimes fake opinions, which are referred as Spam Reviews. In this study, we aim at providing an efficient method to identify spam reviews and to filter out the spam content with the dataset of gsmarena.com. Experiments on the dataset collected from gsmarena.com show that the proposed system achieves higher accuracy than the standard na?ve bayes

Hybrid approach for spam email detection

On this era, email is a convenient way to enable the user to communicate everywhere in the world which it has the internet. It is because of the economic and fast method of communication. The email message can send to the single user or distribute to the group. Majority of the users does not know the life exclusive of e-mail. For this issue, it becomes an email as the medium of communication of a malicious person. This project aimed at Spam Email. This project concentrated on a hybrid approach namely Neural Network (NN) and Particle Swarm Optimization (PSO) designed to detect the spam emails. The comparisons between the hybrid approach for NN_PSO with GA algorithm and NN classifiers to show the best performance for spam detection. The Spambase used contains 1813 as spams (39.40%) and 2788 as non-spam (60.6%) implemented on these algorithms. The comparisons performance criteria based on accuracy, false positive, false negative, precision, recall and f-measure. The feature selection used by applying GA algorithm to reducing the redundant and irrelevant features. The performance of F-Measure shows that the hybrid NN_PSO, GA_NN and NN are 94.10%, 92.60% and 91.39% respectively. The results recommended using the hybrid of NN_PSO with GA algorithm for the best performance for spam email detection