Survey on Intrusion Detection Using Data Mining Methods
The present emerging information growth has made numerous challenges in the data mining. Data mining is the procedure of removing valid, before known, and comprehensive data sets for the future decision making. With the better technology through WWW, the streaming information comes into the picture with its challenges. The data, which alters with time and updates its value, is known as streaming information. As most of the data is streaming in nature, there are numerous challenges we need to face in the security perspective sense. Intrusion detection system (IDS) works within the detecting supposition of the intruders to protect the respective method. The research in mining of data stream and system for intrusion detection has gained high attraction because of the system’s safety measure significance. Algorithms, frameworks and systems that address security issues have been developed over a few years. A present approach of intrusion detection desires detection rate and high accuracy as well as low false alarm rate. We, in brief, outline and compare a tremendous amount of intrusion detection ways, strategies and systems. In addition, we also talk about tools, which can be used by way of network defenders and data sets.
Cite this Article
Manish Arya, Sanjiv Sharma. Survey on Intrusion Detection Using Data Mining Methods. Journal of Advanced Database Management & Systems. 2016; 3(2): 33–43p.
Jiawei Han, Micheline Kamber, Jian Pei. Data Mining: Concepts and Techniques. 3rd Edn. Morgan Kaufmann; 2011. (1st ed., 2000–2001) (2nd ed., 2006).
Babcock B, Datar M, Motwani R. Load Shedding Techniques for Data Stream Systems. (shortpaper), Proc. of the 2003 Workshop on Management and Processing of Data Streams. Jun 2003. Jacobs IS, Bean CP. Fine Particles, Thin Films and Exchange Anisotropy in Magnetism. Vol. III. Rado GT, Suhl H, editors. New York: Academic; 1963; 271–350p.
Bifet, Albert. Mining Big Data in Real Time. Informatica 37. 2013; 15–20p.
Babcock B, Babu S, Datar M, et al. Models and Issues in Data Stream Systems. Proceedings of PODS. 2002.
Zhen-Ya Zhang, Hong-Mei Cheng, et al. From Data Mining to
Opportunity/Symptoms Found. Computer Science. 2007.
Dai Yingxia, Lian Yi-Feng, Wang Hang. Security and Intrusion Detection Systems [M]. Beijing: Tsinghua University Press; 2002.
Manish Kumar, Hanumanthapaa M. Intrusion Detection System using Stream Data Mining and Drift Detection Method. 4th ICCCNT’2013, Tiruchengode, India. Jul 4–6, 2013.
Heady R, Luger G, Maccabe A, et al. The Architecture of a Network Level Intrusion Detection System. Technical Report, Computer Science Department, University of New Mexico. Aug 1990.
Nadianmai GV, Hemalathain M. Effective Approach toward Intrusion Detection System using Data Mining Techniques. Cairo University, Elsevier, Egyptian Informatics Journal. 2014; 37–50p.
Feng Wenying, Zhang Qinglei, Hu Gomgzhu, et al. Mining Network Data for Intrusion Detection through Combining SVMs with Ant Colony Networks. Elsevier, Future Gener Comput Syst. 2014; 37: 127–140p.
Mohammad Muamer N, Sulaiman Norrozila, Muhsin Osama Abdulkarim. A Novel Intrusion Detection System by using Intelligent Data Mining in Weka Environment. Elsevier, Procedia Comput Sci. 2011; 3: 1237–1242p.
Al-Saedi Karim, Manickam Selvakuma R, Ramadass Sureswaran, et al. Research Proposal: An Intrusion Detection system Alert Reduction and Assessment Framework Based on Data Mining. Journal of Computer Science. 2013; 9(4): 421–426p. ISSN: 1549-3636.
Agarwal Basan, Mittal Namita. Hybrid Approach for Detection of Anomaly Network Traffic using Data Mining Techniques. Elsevier, Procedia Technology. 2012; 6: 996–1003p.
Orfila Augustin, Carbo Javier, Ribagorda Arturo. Autonomous Decision on Intrusion Detection with Trained BDI Agents. Elsevier, Comput Commun. 2008; 31: 1803–1813p.
Pietraszek Tadeusz, Tanner Axel. Data Mining and machine Learning-Towards Reducing False Positives in Intrusion Detection. Elsevier, Information Security Technical Report. 2005; 10: 169–183p.
Xiang Cheng, Yong Png Chin Menz, Lim Swee. Design of Multiple-Level Hybrid Classifier for Intrusion Detection System Using Bayesian Clustering and Decision Trees. Elsevier, Pattern Recogn Lett. 2008; 29: 918–924p.
Li Xiao-Bai. A Scalable Decision Tree System and its Application in Pattern Recognition and Intrusion Detection. Elsevier, Decis Support Syst. 2005; 41: 112–130p.
Chen Chia-Mei, Chen Ya-Lin Lin, Hsao-Chung. An Efficient Network Intrusion Detection. Elsevier, Comput Commun. 2010; 33: 477–484p.
Yu Jaehak, Kang Hyunjoong, Park DaeHeon, et al. An In-Depth Analysis on Traffic Flooding Attacks Detection and System Using Data Mining Techniques. Elsevier, J Syst Architect. 2013; 59: 1005–1012p.
Li Yang, Guo Li. An Active Learning Based TCM-KNN Algorithm for Supervised Network Intrusion Detection. Elsevier, Computer Security. 2007; 26: 459–467p.
Kim Mi-Yeon Lee, Dong Hoon. Data-Mining Based SQL Injection Attack Detection using Internal Query Trees. Elsevier, Expert Syst Appl. 2014; 41: 5416–5430p.
Chen Wun-Hwa, Hsu Sheng-Hsun, Shen Hwang-Pin. Application of SVM and ANN for Intrusion Detection. Elsevier, Comput Oper Res. 2005; 32: 2617–2634p.
Koc Levent, Mazzuchi Thomas A, Sarkani Shahram. A Network Intrusion Detection System Based on a Hidden Naive Bayes Multiclass Classifier. Elsevier, Expert Syst Appl. 2012; 39: 13492–13500p.
Su Ming-Yang, Yu Gwo-Jong, Lin Chun-Yuen. A Real-Time Network Intrusion Detection System for Large-Scale Attacks Based on an Incremental Mining Approach. Elsevier, Computer Security. 2009; 28: 301–309p.
Ozyer Tansel, Alhajj Reda, Barker Ken. Intrusion Detection by Integrating Boosting Genetic Fuzzy Classifier and Data Mining Criteria for Rule Prescreening. J Netw Comput Appl. 2007; 30: 99–113p.
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