ANALISA CLUSTERING PHISING DENGAN K-MEANS DALAM MENINGKATKAN KEAMANAN KOMPUTER


Suhardi Rustam(1*);

(1) Universitas Ichsan Gorontalo
(*) Corresponding Author

  

Abstract


Almost the crime in cyber is a condition of criminal activity using computers or computer networks as tools and also as a target. Fraud in academic websites the most at risk. The action of Phishing is on the rise. Recorded globally, the number of fraudulent mode phishing 42% of the mode in addition to phishing which is stated in the website Anti-Phishing Working Group (APWG) in its monthly report, noting there 12.845 e-mail new and unique as well as 2.560 a fake site that is used as a means of phishing, in Addition to increase the quantity, the quality of the attacks is also increasing, the need for the work done by the network administrator in improving surveillance in monitoring activity on the network, in the action of data theft will perform the action of manipulating someone with the appearance of a particular web site. In this study a set of datasets will be clustering using k-means, K-Means algorithm will classify the dataset, resulted in the identification of phishing that is accurate and certifiable. With the results of this research iteration=10, the K-Fold=2 the of the Bouldin Davis index = 0.119.


Keywords


Clustering; Phishing; K-Means Clustering; Computer Security; Data Mining

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 3569 times
PDF view: 2120 times
     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v10i2.309.175-181
  

Cite

References


Musriha, Gilang R. 2014. Pengaruh Intensitas Pemakaian Internet Terhadap Penggunaan Internet untuk Berbelanja Online yang Dimoderasi oleh Consumer Innovativeness Di Surabaya

Salim, Tomy. 2017. Data Mining Mengidentifikasi Website Phising Menggunakan Algoritma C.45. Jurnal TAM (Technology Acceptance Model) Volume 8

Arora, P., Deepali, D., dan Varshney, S. 2015. Analysis of K-Means and KMedoids Algorithm For Big Data. International Conference on Information Security & Privacy (ICISP2015), (hal. 507-512). Nagpur,India.

Agusta Yudi.2007. K-Means – Penerapan, Permasalahan dan Metode Terkait. Jurnal Sistem dan Informatika Vol. 3

Bates, A. & Kalita, J. 2016. Counting clusters in twitter posts. Proceedings of the 2nd International Conference on Information Technology for Competitive Strategies, pp. 85

Hasibuan, A Zaenal .2007. Metodelogi Penelitian Pada Bidang Ilmu Komputer dan Teknologi Informasi. 20 Juli 2018


Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 Suhardi Rustam

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.