Comparisons on Intrusion Detection and Prevention Systems in Distributed Databases

Yıl 2019, Cilt: 7 Sayı: 4, 446 - 455, 30.10.2019

Cigdem Bakir , Veli Hakkoymaz Banu Diri Mehmet Güçlü

https://doi.org/10.17694/bajece.605134

Cited By: 2

Öz

With the use of distributed systems,
different users can instantly access data from different locations and perform
some operations on the data. However, the unauthorized access of multiple users
to the system from different points at the same time can lead to dangerous
results in terms of data security and confidentiality of the data. This study
is based on intrusion detection and prevention systems built on distributed
databases and classifies the methods used to analyze and evaluate successes
comparatively. It is observed that the artificial immunity algorithm we have
described in artificial intelligence techniques, which is one of the methods
classified as three categories, gives more successful results compared to the
other techniques mentioned in the data mining and statistical methods. 

Anahtar Kelimeler

Artifical intelligence method, Data mining, Distributed database, Intrusion detection system, Intrusion prevention system, Statistical method.

Kaynakça

• [1] P. Ramasubramanian, A. Kannan, “Multi-Agent based Quickprop Neural Network Short-term Forecasting Framework for Database Intrusion Prediction System”, CiteSeerX, 2014.[2] P.Romasubramanian, A. Kannan, A. “A genetic-algorithm based neural network short-term farecasting framework for database intrusion prediction system”, Soft Computing, Vol., 8, pp. 699-714, 2006.[3] K.Haslum, A. Abraham, “Disp: A framework for distributed intrusion prediction and prevention using hidden markov models and online fuzzy risk assessment”, 3rd International Symposium on Information Assurance and Security, pp.183-190, 2007.[4] H.Deng, Q. Zeng, Q, “SVM-baseed detection system for wireless ad hoca networks”, Vehicular Technology Conference, Vol.3, pp. 2147-2151, 2003.[5] F. Jemili, M. Zaghdoud, “Hybrid Intrusion Detection and Prediction multiAgent System, HIDPAS”, (IJCSIS) International Journal of Computer Science and Information Security, Vol.5, 1, pp. 62-71, 2009.[6] W. Hu, G. Jun, “Online Adaboost-Based Parameterized Methods for Dynamic Distributed network Intrusion Detection”, IEEE Transactions on CyberNetics, Vol.44, 3, pp. 66-82, 2014.[7] A. Abraham, C. Grosan, C. Martiv-Vide, “Evolutionary design of intrusion detection programs”, Int. Journal of Network Security, Vol. 4, pp. 328-339, 2007.[8] Ş.Sağıroğlu, E.N. Yolaçan, U. Yavanoğlu, “Zeki Saldırı Tespit Sistemi Tasarımı ve Gerçekleştirilmesi”, Ankara, 2011.[9] M.Z.Yıldırım, A.Çavuşoğlu, B. Şen, İ. Budak, İ, “Yapay Sinir Ağları ile Ağ Üzerinde Saldırı Tespiti ve Paralel Optimizasyonu”, XVI. Akademik Bilişim, Mersin, 2011.[10] Y.Liao, V.R.Vemuri., “Use of K-Nearest Neighbor classifier for intrusion detection”, Elsevier Computers&Security, Vol. 21, 5, pp.439-448, 2002.[11] M.Jianliang, “The Application on Intrusion Detection based on K-Means Cluster Algorithm”, International Forum on Information Technology and Applications, pp. 150-152, 2009.[12] K.M. Faraoun, A.Boukelif, “Neural Networks learning improvement using the K-Means clustering algorithm to detect network intrusions”, International Journal of Computer and Information Engineering, Vol. 1, 10, pp. 3138-3145, 2007.[13] G.U.Nadiammai, M.Hemalathen, “An evaluation of clustering technique over intrusion detection system”, ICACCI '12 Proceedings of the International Conference on Advances in Computing, Communications and Informatics, pp. 1054-1060, 2002.[14] K. Law, F.Kwok, “IDS False Alarm Filtering using KNN Classifier, Springer Information Security Applications Lecture Notes in Computer Science”, pp.114-121, 2004.[15] A. Adetunmbi, “Network Intrusion Based on Rough set and k-Nearest Neighbour”, International Journal of Computing ICT Research, Vol. 2, 1, 2008.[16] A.Aburonman, M. Reaz, “A novel SVM-kNN-PSO ensemble method for intrusion detection system”, Elseiver Applied Soft Computing, Vol.38, pp. 360-372, 2016.[17] M. Chen, P. Chang, J. Wu, “A population-based incremental learning approach with artificial immune system for network intrusion detection”, Elseiver Engineering Applications of Artificial Intelligence, 51, pp. 171-181, 2016.[18] A. Peddabachigari, A. Abraham, “Intrusion detection systems using decision trees and support vector machines”, International Journal of Applied Science and Computations, pp.1-16, 2004.[19] N. Rachburee, N. Punlumjeak, “Big Data Analytics: Feature Selection and Machine Learning for Intrusion Detection on Microsoft Azure Platform”, Journal of Telecommunication Electronic and Computer Engineering, Vol. 9, 1-4, pp. 1-5, 2017.[20] A. Sung, S. Mukkamala, “Identifying import features for Intrusion Detection using Support Vector Machines and Neural Networks”, Proceedings of the 2003 Symposium Applications and the Internet (Saint’03), 2003.[21] S. Mukkamala, G. Janoski, “ Intrusion Detection using Neural Networks and Support Vector Machines”, IJCNN’02 Proceedings of the 2002 International Joint Conference on, Vol. 2, pp. 1702-1707, 2002.[22] A. Tajbakhsh, M. Rahmati, “Intrusion detection using fuzzy assocation rules”, Elsevier Applied Soft Computing, Vol. 9, pp. 462-469, 2009.[23] Y. Hu, B. Panda, “ A data mining approach for Database Intrusion Detection”, ACM Symposium on Applied Computing, pp. 711-716, 2004.[24] R. Noum, Z. Al-Sultani, “ Learning Vector Quantization (LVQ) and k-Nearest Neighbor for Intrusion Classification”, World of Computer Science and Information Technology Journal (WCSIT), Vol. 2, 3, pp. 105-109, 2012.[25] B. Hamman, D. Hoffman, “Learning vector Quantization for (dis-)-similarities”, Elsevier Neurocomputing, Vol. 131, pp. 43-51, 2014.[26] E. Soleiman, A. Fetarat, “Using Learning Vector Quantization (LVQ) in Intrsuion Detection Systems”, International Journal of Innovative Research in Advanced Engineering (IJIRAE), Vol. 1, 10, 2014.[27] Y. Degang, C. Guo, C, “Learning Vector Quantization Neural Network Method for Network Intrusion Detection”, Wuhan University Journal of Natural Sciences, Vol. 12, 1, pp. 147-150, 2007.[28] L.R. Rabier, “A tutorial on Hidden Markov Models and Selected applications speech recognition”, Ready in Speech Recognition, pp. 267-296, 1990.[29] D. Deshmukh, T. Ghorpade, P. Padiya, “ Improving Classification Using Preprocessing and Machine Learning Algorithms on NSL-KDD Dataset”, 2015 International Conference on Communication, Information & Computing Technology (ICCICT), 2015.[30] R. Shanmugavadivu, N. Nagarajan, “Network Intrusion Detection System using Fuzzy Logic”, Indian Journal of Computer Science and Engineering (IJCSE), Vol. 2, 1, pp. 101-111, 2014.[31] D.S. Mukherjee, N. Sharma, “Intrusion Detection using Naive Bayes Classifier with Feature Reduction”, Elsevier Procedia Technology, Vol. 4, pp. 119-128, 2012.[32] S. Sharma, “An Improved Network Intrusion Detection Technique based on k-means clustering via Naive Bayes Classification”, IEEE-International Conference on Advances In Engineering, Science and Management (ICAESM-2012), pp. 417-422, 2012.[33] M. Panda, M. Patra, “Network Intrusion Detection using Naive Bayes”, IJCSNS International Journal of Computer Science and Network Security, Vol. 7, 12, pp. 258-263, 2007.[34] A. El-Semany, “A Framework for Hybrid Fuzzy Logic Intrusion Detection Systems”, IEEE International Conference on Fuzzy Systems, pp. 325-330, 2005.[35] J.Tian, “Intrusion detection combining Multiple Decision Trees by Fuzzy Logic”, Proceedings of the sixth International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT’05), 2005.[36] S. Janakiraman, V. Vasudevan, “An Intelligent Distributed Intrusion Detection System using Genetic Algorithm”, JCIT Journal of Convergence Information Technology, Vol. 4, 1, 2009.[37] M. Hassan, “Network Intrusion Detection System Using Genetic Algorithm and Fuzzy Logic”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 1, 7, pp. 435-1445, 2013.[38] W. Chen, S.H. Hsu, “Application of SVM and ANN for intrusion detection”, Elsevier Computers&Operations Research, 32, 2617-2634, 2005.[39] X. Tong, Z.Wang, “A research using hybrid RBF/Elman neural networks for intrusion detection system secure model”, Elsevier Computer Physics Communications, Vol. 180, pp.1795-1801, 2009.[40] D. Mahit, "Using Artifical Neural Network Classification and Invertion of Intrusion in Classification and Intrusion Detection System, International Journal of Innovative in Computer and Communication Engineering, Vol. 3, 2, pp. 1102-1108, 2015.[41] L.Castro, J. Timmis, “Artificial immune systems as a novel soft computing paradigm”, Soft computing, Springer, Vol. 7, 8, pp. 526–544, 2003.[42] J.Shen, J. Wang, “Network Intrusion Detection by Artificial Immune System”, IEEE Power and Energy General Meeting, pp.1-8, 2011.[43] C. Bakir, V. Hakkoymaz, “ Veritabanı Güvenliğinde Saldırı Tahmini ve Tespiti için Kullanıcıların Sınıflandırılması”, ISCTurkey2015 8.Uluslararası Bilgi Güvenliği ve Kriptoloji Konferansı (VIII. Int’l Conference on Information Security and Cryptology), pp. 28-33, 2015.