Machine Learning Framework To Analyze Against Spear Phishing
J. Vijaya Chandra1, Narasimham Challa2, Sai Kiran Pasupuletti3
1J.Vijaya Chandra*, Research Scholar, Dept of CSE, KLEF (Deemed to be University), Guntur, Andhrapradesh, India.
2Dr. Narasimham Challa, Dean IQAC and Professor, Dept of CSE, Vignan’s Institute of Technology and Science, Vishakapatnam, A.P., India.
3Dr.Sai Kiran Pasupuleti, Professor, Dept of CSE, KLEF(Deemed to be University), Guntur, Andhra Pradesh, India.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 3605-36011 | Volume-8 Issue-12, October 2019. | Retrieval Number: L3802081219/2019©BEIESP | DOI: 10.35940/ijitee.L3802.1081219
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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: The objective of this paper is to design and implement machine learning based ensemble algorithm on dataset to fit into the models that can be understood and executed by machines. In this paper we discussed different algorithms and machine learning concepts that can be implemented on the datasets, we taken email spam filter dataset for experiment and analysis, as the Advanced persistent threat the latest threat is intruded using the emails and major intrusion is done through spam emails. Machine learning uses different datamining techniques and mechanisms and accepts the input-data and gives the output as the statistical analysis. We implemented different email classification algorithms on the datasets based on spam and ham emails where spear phishing methods are identified and implemented different classification and regression methods to get the accurate results. In this paper for the better results in spite of existing algorithms we introduced the ensemble methods such as boosting, bagging, stacking and voting for much accuracy and higher level of classification and combining different algorithm. This paper will measure different machine learning algorithms performance on spam email filtering on the huge datasets. The framework provides implementation of learning algorithms that you can apply to larger datasets. An obvious approach to making decisions more reliable is to combine the output of different models. We even compared the existing algorithms and proposed algorithm; comparison tables are drawn along with the statistical analysis, data and graphical analysis is given.
Keywords: Advanced Persistent Threat, Spear Phishing, Email Classification, Machine Learning, Data Mining.
Scope of the Article: Classification