A Stepping Stone Perspective to Detection of Network Threats: Spam Detection
Mohd Nizam Omar1, Ali Yusny Daud2, Osman Ghazali3

1Mohd Nizam Omar, Inter Networks Research Laboratory, School of Computing, University Utara Malaysia, UUM Sintok, Kedah, Malaysia.

2Ali Yusny Daud, Inter Networks Research Laboratory, School of Computing, University Utara Malaysia, UUM Sintok, Kedah, Malaysia.

3Osman Ghazali, Inter Networks Research Laboratory, School of Computing, University Utara Malaysia, UUM Sintok, Kedah, Malaysia.

Manuscript received on 03 February 2019 | Revised Manuscript received on 10 February 2019 | Manuscript Published on 22 March 2019 | PP: 126-131 | Volume-8 Issue-5S April 2019 | Retrieval Number: ES3405018319/19©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: This paper examines one of the novel applications of the concept of stepping stone detection to address network threats known as spam. Previous research has been identified in several applications such as spam, backdoors, intrusions of proxy servers and denial of service attacks as a possible solution that can be solved by using a stepping stone perspective against network threats. In this paper, an experiment has been conducted as to proof two formulas that generated to solve spam problem. Through the control environment and the development of special prototype to detect spam, the result shows that both formulas in detecting spam attack can be used to detect spam successfully. The successful result of the experiment proofs that one of the identified application really works in the real experiment testbed. By producing another solution to detect spam in this research hopefully can contribute another solution to detect a spam problem.

Keywords: Generated to Solve Spam Problem. Through the Control Environment and the Development of Special Prototype to Detect Spam.
Scope of the Article: Parallel Computing on GPU