Data Mining dengan Segmentasi Pengguna pada Keamanan Sistem File

Agus Pamuji

Abstract


Salah satu sumber daya yang menjadi pertimbangan kritis adalah sistem file. Hampir semuanya terlibat dalam menghubungkan pengguna dengan sistem file. Manajemen pengguna, file dan konfigurasi akan menjadi fokus permasalahan jika dikaitkan dengan keamanan. Pengguna pada sistem file dianggap memiliki identitas ketika terhubung dengan sistem. Disamping itu, atribut izin dan hak yang ada pada pengguna sebagai pelengkap identitas.  Saat ini terjadi peningkatan aktiftas dalam sistem file sehingga menjadi lebih kompleks . Sistem yang kompleks  dan pengguna yang belum terkelola dengan baik maka berpotensi ancaman keamanan file. Dalam studi ini, telah dilakukan penelusuran dan investigasi pada aktivitas  dengan log riwayat aktivitas  pengguna dalam sistem file khususnya pendekatan data mining . Metode klustering ditujukan untuk menganalisis dengan menghasilkan luaran pengetahuan berupa kluster. Pembentukan kluster ditunjang dengan teknik K-Means. Hasil pengelompokan menjadi segmentasi terhadap pengguna pada sistem file.  Hasil akhir merepresentasikan adanya 5 kluster pada teknik K-Means.  Model dengan teknik K-Means terbukti menjadi model yang efektif dibuktikan dengan nilai akurasi pada metode Davies Bouldin Index (DBI). Tambahan pengukuran lain adalah dengan F- Measures untuk meninjau hasil akurasi penempatan kluster pada kasus dengan teknik K-Means. Dengan demikian, metode klustering dengan teknik K-Means merupakan metode yang dianggap handal ketika mensegmentasikan data pengguna terkait dengan aktivitas pada sistem file.


Keywords


Data Mining; Klustering; K-Means; Sistem File; Keamanan File

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References


J. Umarani, S. Manikandan, D. Centre, and T. Nadu, “Implementation of Data mining Concepts in R Programming 1,” Int. J. Trendy Res. Eng. Technol., vol. 4, no. 1, pp. 1–7, 2020.

X. Wang and F. Liu, “Data-driven relay selection for physical-layer security: A decision tree approach,” IEEE Access, vol. 8, no. 1, pp. 12105–12116, 2020, doi: 10.1109/ACCESS.2020.2965963.

S. Mohammad, “Overview on Data mining With Cloud,” J. Emerg. Technol. Innov. Res., vol. 4, no. 12, pp. 519–523, 2021.

I. Kholod, A. Shorov, and S. Gorlatch, “Efficient distribution and processing of data for parallelizing data mining in mobile clouds,” J. Wirel. Mob. Networks, Ubiquitous Comput. Dependable Appl., vol. 11, no. 1, pp. 2–17, 2020, doi: 10.22667/JOWUA.2020.03.31.002.

E. Bertino, M. Kantarcioglu, C. G. Akcora, S. Samtani, S. Mittal, and M. Gupta, “AI for Security and Security for AI,” CODASPY 2021 - Proc. 11th ACM Conf. Data Appl. Secur. Priv., pp. 333–334, 2021, doi: 10.1145/3422337.3450357.

M. A. P. Chamikara, P. Bertok, D. Liu, S. Camtepe, and I. Khalil, “Efficient privacy preservation of big data for accurate data mining ,” Inf. Sci. (Ny)., vol. 527, no. xxxx, pp. 420–443, 2020, doi: 10.1016/j.ins.2019.05.053.

N. Bhandari and P. Pahwa, Comparative analysis of privacy-preserving data mining techniques, vol. 56. Springer Singapore, 2019.

P. Rupprecht et al., “A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging,” Nat. Neurosci., vol. 24, no. 9, pp. 1324–1337, 2021, doi: 10.1038/s41593-021-00895-5.

K. N. Durai, R. Subha, and A. Haldorai, “A Novel Method to Detect and Prevent SQLIA Using Ontology to Cloud Web Security,” Wirel. Pers. Commun., vol. 117, no. 4, pp. 2995–3014, 2021, doi: 10.1007/s11277-020-07243-z.

M. Jha, Smart Intelligent Computing and Applications, vol. 104. Springer Singapore, 2019.

Y. Feng, S. Zhao, and H. Liu, “Analysis of Network Coverage Optimization Based on Feedback K-Means Clustering and Artificial Fish Swarm Algorithm,” in IEEE Access, 2020, vol. 8, pp. 42864–42876, doi: 10.1109/ACCESS.2020.2970208.

D. Bhayani, “Identification of Security Breaches in Log Records using Data mining Techniques,” Int. J. Pure Appl. Math., vol. 119, no. 15, pp. 743–756, 2018.

D. Iordache, “Database – Web Interface Vulnerabilities,” Strateg. XXI - Secur. Def. Fac., vol. 17, no. 1, pp. 279–287, 2021, doi: 10.53477/2668-2001-21-35.

C. Gao, X. Zhang, and H. Liu, “Data and knowledge-driven named entity recognition for cyber security,” Cybersecurity, vol. 4, no. 1, 2021, doi: 10.1186/s42400-021-00072-y.

R. T. H. Hasan and S. Y. Ameen, “Security Enhancement of IoT and Fog Computing Via Blockchain Applications,” J. Soft Comput. Data Min., vol. 2, no. 2, pp. 26–38, 2021, doi: 10.30880/jscdm.2021.02.02.003.

R. A. Laksono, K. R. Sungkono, R. Sarno, and C. S. Wahyuni, “Sentiment analysis of restaurant customer reviews on tripadvisor using naïve bayes,” Proc. 2019 Int. Conf. Inf. Commun. Technol. Syst. ICTS 2019, pp. 49–54, 2019, doi: 10.1109/ICTS.2019.8850982.

C. Oktarina, K. A. Notodiputro, and I. Indahwati, “Comparison of K-Means Clustering Method and K-Medoids on Twitter Data,” Indones. J. Stat. Its Appl., vol. 4, no. 1, pp. 189–202, 2020, doi: 10.29244/ijsa.v4i1.599.

J. Clark and F. Provost, “Unsupervised dimensionality reduction versus supervised regularization for classification from sparse data,” Data Min. Knowl. Discov., vol. 33, no. 4, pp. 871–916, 2019, doi: 10.1007/s10618-019-00616-4.

M. R. Anwar, R. Panjaitan, and R. Supriati, “Implementation Of Database Auditing By Synchronization DBMS,” Int. J. Cyber IT Serv. Manag., vol. 1, no. 2 SE-Articles, pp. 197–205, 2021, [Online]. Available: https://iiast-journal.org/ijcitsm/index.php/IJCITSM/article/view/53.

W. Fu and P. O. Perry, “Estimating the Number of Clusters Using Cross-Validation,” J. Comput. Graph. Stat., vol. 29, no. 1, pp. 162–173, 2020, doi: 10.1080/10618600.2019.1647846.

T. Javid, M. K. Gupta, and A. Gupta, “A hybrid-security model for privacy-enhanced distributed data mining ,” J. King Saud Univ. - Comput. Inf. Sci., no. xxxx, 2020, doi: 10.1016/j.jksuci.2020.06.010.

W. Yang, H. Long, L. Ma, and H. Sun, “Research on clustering method based on weighted distance density and k-means,” in Procedia Computer Science, 2020, vol. 166, pp. 507–511, doi: 10.1016/j.procs.2020.02.056.

B. Jumadi Dehotman Sitompul, O. Salim Sitompul, and P. Sihombing, “Enhancement Clustering Evaluation Result of Davies-Bouldin Index with Determining Initial Centroid of K-Means Algorithm,” J. Phys. Conf. Ser., vol. 1235, no. 1, pp. 1–7, 2019, doi: 10.1088/1742-6596/1235/1/012015.

S. Ramos et al., “Data mining techniques for electricity customer characterization,” Procedia Comput. Sci., vol. 186, no. 3, pp. 475–488, 2021, doi: 10.1016/j.procs.2021.04.168.

R. C. Sharma, K. Hara, and H. Hirayama, “A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data,” Scientifica (Cairo)., vol. 2017, 2017, doi: 10.1155/2017/9806479.

A. Khobzaoui, M. Benhamouda, and M. Fahsi, “Data mining Contribution to Intrusion Detection Systems Improvement,” in ACM International Conference Proceeding Series, 2020, pp. 1–8, doi: 10.1145/3447568.3448514.

X. N. Bui, H. Nguyen, Y. Choi, T. Nguyen-Thoi, J. Zhou, and J. Dou, “Prediction of slope failure in open-pit mines using a novel hybrid artificial intelligence model based on decision tree and evolution algorithm,” Sci. Rep., vol. 10, no. 1, pp. 1–17, 2020, doi: 10.1038/s41598-020-66904-y.

L. Vanfretti and V. S. N. Arava, “Decision tree-based classification of multiple operating conditions for power system voltage stability assessment,” Int. J. Electr. Power Energy Syst., vol. 123, no. 3, pp. 1–10, 2020, doi: 10.1016/j.ijepes.2020.106251.

M. Rouzbahman et al., “Data mining Methods for Optimizing Feature Extraction and Model Selection,” in PervasiveHealth: Pervasive Computing Technologies for Healthcare, 2020, pp. 1–8, doi: 10.1145/3406601.3406602.




DOI: http://dx.doi.org/10.26418/jp.v8i1.52233

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