SISTEM DETEKSI INTRUSI PADA JARINGAN DENGAN MENGGUNAKAN METODE K-NEAREST NEIGHBOR DAN TEORI DEMPSTER SHAFER

Akhmad Alimudin, Waskitho Wibisono, Diana Purwitasari

Abstract


Penelitian mengenai Intrusion Detection System(IDS) telah banyak dilakukan untuk mendapatkan hasil deteksi intrusi yang baik dan akurat dari IDS. Untuk membangun sistem IDS, salah satu komponen utamanya adalah komponen yang mampu melakukan proses klasifikasi terhadap data log paket jaringan. Pada penelitian ini, kami akan melakukan penggunaan metode KNN dan Dempster Shafer (KNN-DS) untuk diterapkan pada Sistem Deteksi Intrusi pada jaringan. Data yang akan digunakan pada penelitian kali ini adalah data KDDCUP 99 yang merupakan data tcpdump dari DARPA98 yang sudah dilakukan preprocessing. Dengan menggunakan KNN-DS ini akan didapatkan hasil yang lebih maksimal untuk mendukung nilai kebenaran dari output yang dihasilkan oleh KNN.


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References


A. O. Boudraa, e. a. (2004). Dempster-Shafer’s Basic Probability Assignment Based on Fuzzy Membership Functions. Electronic Letters on Computer Vision and Image Analysis , vol. 4, 1-9.

A. Padovitz, e. a. (2006). A Unifying Model for Representing and Reasoning About Context under Uncertainty. 11th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems,IPMU, Paris, France , 1983 - 1989.

Adel N. Toosi, M. K. (2006). Network Intrusion Detection Based on Neuro-Fuzzy Classification. In Proceeding of IEEE International Conference on Computing and Informatics .

Adnan M.A. Brifcani, A. S. (2011). Intrusion Detection and Attack Classifier Based on Three Techniques:A Comparative Study. In Eng. & Tech. Journal’2011 .

Alief Habibiy, I. U. (2009). Rancang Bangun Perangkat Lunak Klasifikasi Intrusi pada Jaringan Menggunakan Metode Support Vector Machine(SVM). Seminar Proyek Akhir PENS .

Bambang Wijanarko, E. M. (2009). Algoritma Fuzzy Sebagai Metode Pendeteksi Pola Serangan Pada Jaringan Berbasis Snort IDS. Seminar Proyek Akhir PENS .

Berbers, D. P. (2006). Quality Extensions and Uncertainty Handling for Context Ontologies. Workshop on Context and Ontologies: Theory, Practice and Applications, Riva del Garda, Italy , 62-64.

Bloch, I. (1996). Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account. Pattern Recognition Letters, vol. 17 , 905 - 919.

Denoeux, T. (1995). A k-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory. IEEE Transaction on Systems, Man and Cybernetics , vol. 25, 804-813.

Dong Yu, D. A. (2005). Alert confidence fusion in intrusion detection systems with extended Dempster- Shafer theory. In Proceedings of ACM Southeast Regional Conference (2)'2005 , 142-147.

Golshani, E. C.-R. (1990). Uncertain reasoning using the Dempster-Shafer method: an application in forecasting and marketing management. Expert Systems , vol. 7, 9-18.

H. Wu, e. a. (2003). Sensor Fusion Using Dempster- Shafer Theory II: Static Weighting and Kalman Filter-like Dynamic Weighting. IEEE Instrumentation and Measurement Technology Conferencev (IMTC 2003), Colorado, USA .

Hadjiefthymiades, C. A. (1153 - 1168). Enhancing Situation-Aware Systems Through Imprecise Reasoning. IEEE Transactions On Mobile Computing, vol. 7 , 2008.

JC Burgess, C. (1998). A Tutorial on Support Vector Machines for Pattern Recognition. Bell Laboratories, Lucent Technologies .

KDD Cup 1999 Intrusion detection dataset. (1999). Retrieved from http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html

luthfi, E. T. (2007). Fuzzy C-Means untuk Clustering Data(Studi Kasus:Data Performance Mengajar Dosen). Seminar Nasional Teknologi .

M. C. Florea, e. a. (2007). Dempster-Shafer Evidence Theory Through The Years: Limitations, Practical Examples, Variants Under Conflict and a New Adaptive Combination Rule. Advances and Challenges in Multisensor Data and Information Processing, ed: IOS Press , 148-156.

Novi Anisyah, Z. S. (2011). Aplikasi Mobile untuk Metode K-Nearest Neighbor pada Intrusion Detection System Berbasis Snort. Seminar Proyek Akhir PENS .

P. D. Haghighi, e. a. (2009). Situation-Aware Adaptive Processing (SAAP) of Data Streams. Pervasive Computing, Innovations in Intelligent Multimedia and Applications, ed: Springer-Verlag , 318-356.

Padovitz, A. (2006). Context Management and Reasoning about Situations in Pervasive Computing. Ph.D Thesis, Caulfield School of Information Technology, Monash University .

Qi Chen, U. A. (2006). Anomaly Detection Using the Dempster-Shafer Method. In Proceedings of DMIN’2006 , 232-240.

S. L. H´egarat-Mascle, e. a. (1997). Application of Dempster–Shafer Evidence Theory to Unsupervised Classification in Multisource Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing , vol. 35, 1028-1037.

Sentz, K. (2010, 3 9). Combination of Evidence in Dempster-Shafer Theory. Retrieved from www.sandia.gov: http://www.sandia.gov/epistemic/Reports/SAND2002- 0835.pdf

Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press .

V. Venkatachalam, S. S. (2008). Clustering and Sample Selection to Enhance the Performance of the Lamstar Intrusion Detection System. In I.J of Simulation’2008 , 13-20.

Yaxin Bi, D. A. (2004). Combining Multiple Classifiers Using Dempster's Rule of Combination for Text Categorization. In Proceedings of MDAI’2004 , 127- 138 .


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