Comparisons on Intrusion Detection and Prevention Systems in Distributed Databases

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.

[1]  Arputharaj Kannan,et al.  A genetic-algorithm based neural network short-term forecasting framework for database intrusion prediction system , 2006, Soft Comput..

[2]  Mamun Bin Ibne Reaz,et al.  A novel SVM-kNN-PSO ensemble method for intrusion detection system , 2016, Appl. Soft Comput..

[3]  Lam For Kwok,et al.  IDS False Alarm Filtering Using KNN Classifier , 2004, WISA.

[4]  Jonathan Timmis,et al.  Artificial immune systems as a novel soft computing paradigm , 2003, Soft Comput..

[5]  Sheng-Hsun Hsu,et al.  Application of SVM and ANN for intrusion detection , 2005, Comput. Oper. Res..

[6]  Reyadh Shaker Naoum,et al.  Learning Vector Quantization ( LVQ ) and k-Nearest Neighbor for Intrusion Classification , 2012 .

[7]  Yi Hu,et al.  A data mining approach for database intrusion detection , 2004, SAC '04.

[8]  Mohamed Ben Ahmed,et al.  Hybrid Intrusion Detection and Prediction multiAgent System HIDPAS , 2009, ArXiv.

[9]  Kamel Mohamed Faraoun,et al.  Neural Networks Learning Improvement using the K-Means Clustering Algorithm to Detect Network Intrusions , 2007 .

[10]  R. Shanmugavadivu.  NETWORK INTRUSION DETECTION SYSTEM USING FUZZY LOGIC , 2011 .

[11]  Wattana Punlumjeak,et al.  Big Data Analytics: Feature Selection and Machine Learning for Intrusion Detection on Microsoft Azure Platform , 2017 .

[12]  Pei-Chann Chang,et al.  A population-based incremental learning approach with artificial immune system for network intrusion detection , 2016, Eng. Appl. Artif. Intell..

[13]  Carlos Martín-Vide,et al.  Evolutionary Design of Intrusion Detection Programs , 2007, Int. J. Netw. Secur..

[14]  Neelam Sharma,et al.  INTRUSION DETECTION USING NAIVE BAYES CLASSIFIER WITH FEATURE REDUCTION , 2012 .

[15]  S. Janakiraman,et al.  An Intelligent Distributed Intrusion Detection System using Genetic Algorithm , 2009, J. Convergence Inf. Technol..

[16]  Jidong Wang,et al.  Network intrusion detection by artificial immune system , 2011, IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society.

[17]  B Kharde Gayatri,et al.  Using Artificial Neural Network Classification and Invention of Intrusion in Network Intrusion Detection System , 2015 .

[18]  Mauricio Papa,et al.  A Framework for Hybrid Fuzzy Logic Intrusion Detection Systems , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..

[19]  Manas Ranjan Patra,et al.  NETWORK INTRUSION DETECTION USING NAÏVE BAYES , 2007 .

[20]  A. Abraham,et al.  Intrusion Detection Systems Using Decision Trees and Support Vector Machines , 2004 .

[21]  Frank-Michael Schleif,et al.  Learning vector quantization for (dis-)similarities , 2014, Neurocomputing.

[22]  Abdolreza Mirzaei,et al.  Intrusion detection using fuzzy association rules , 2009, Appl. Soft Comput..

[23]  S. K. Sharma,et al.  An improved network intrusion detection technique based on k-means clustering via Naïve bayes classification , 2012, IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012).

[24]  Andrew H. Sung,et al.  Intrusion detection using neural networks and support vector machines , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[25]  M. M. Hassan.  Network Intrusion Detection System Using Genetic Algorithm and Fuzzy Logic , 2013 .

[26]  A.H. Sung,et al.  Identifying important features for intrusion detection using support vector machines and neural networks , 2003, 2003 Symposium on Applications and the Internet, 2003. Proceedings..

[28]  Ajith Abraham,et al.  DIPS: A Framework for Distributed Intrusion Prediction and Prevention Using Hidden Markov Models and Online Fuzzy Risk Assessment , 2007 .

[29]  Puja Padiya,et al.  Improving classification using preprocessing and machine learning algorithms on NSL-KDD dataset , 2015, 2015 International Conference on Communication, Information & Computing Technology (ICCICT).

[30]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[31]  Zhu Wang,et al.  A research using hybrid RBF/Elman neural networks for intrusion detection system secure model , 2009, Comput. Phys. Commun..

[32]  Jun Gao,et al.  Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection , 2014, IEEE Transactions on Cybernetics.