Malay SMS Spam Detection Framework using Naïve Bayes Technique
Cik Feresa Mohd Foozy1, Shamala Palaniappan2, Sofia Najwa Ramli3, Chuah Chai Wen4, MF Abdollah5, Rabiah Ahmad6
1Cik Feresa Mohd Foozy, Applied Computing Technology, Department of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, Johor, Malaysia.
2Shamala Palaniappan, Applied Computing Technology, Department of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, Johor, Malaysia.
3Sofia Najwa Ramli, Applied Computing Technology, Department of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, Johor, Malaysia.
4Chuah Chai Wen, Information Security Interest Group, Department of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, Johor, Malaysia.
5MF Abdollah, Department of Information & Communication Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka, Malaysia.
6Rabiah Ahmad, Department of Information & Communication Technology, University Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka, Malaysia.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript Published on 19 June 2019 | PP: 331-335 | Volume-8 Issue-8S June 2019 | Retrieval Number: H10560688S19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Short Message Service (SMS) Spam is one form of mobile device attack that can affect mobile user’s security and privacy. This is because such attack applies social engineering method to trick the user for information gathering. This study proposed an SMS Spam detection framework specifically for Malay language by using Naïve Bayes. There are several solutions to detect SMS Spam, but machine learning is one of the most effective technique to detect spam attack. In addition, the existing detection framework using machine learning technique is not effective for Malay language SMS. This is because the features used are not based on Malay language to detect the SMS content as Spam or not Spam. This framework consists of several processes such as Data Collection, Pre-processing, three types of Features Selection, Classification and Detection. Based on the result, it shows that the classification derives acceptable accuracy which is over 90%.
Keywords: Attack, Detection, Naïve Bayes, Spam.
Scope of the Article: Computer Science and Its Applications