Abstract

Intrusion detection is the procedure for determining intrusions in the network. This paper explores the methodology in the direction of intrusion detection system. It explores the possibility of enhancement and propounding the advantages. This study helps in exploring the method analytically, methodically and experimentally. This paper lists the gaps and the advantages, so that future framework can be design to enhance the efficiency. It also provides the detail discussion based on the attributes and parameters variations. Finally future suggestions have been listed.

Keyword

Data mining, Evolutionary algorithms, Intrusion detection, Network system.

Cite this article

Refference

[1][1]Jianliang M, Haikun S, Ling B. The application on intrusion detection based on k-means cluster algorithm. In international forum on information technology and applications 2009 (pp. 150-2). IEEE.

[2][2]Sharma N, Gaur B. An approach for efficient intrusion detection for KDD dataset: a survey. International Journal of Advanced Technology and Engineering Exploration. 2016; 3(18):72-6.

[3][3]Mohamed MH, Waguih HM. A proposed academic advisor model based on data mining classification techniques. International Journal of Advanced Computer Research. 2018; 8(36):129-36.

[4][4]Tian L, Jianwen W. Research on network intrusion detection system based on improved k-means clustering algorithm. In international forum on computer science-technology and applications 2009 (pp. 76-9). IEEE.

[5][5]Conteh NY, Schmick PJ. Cybersecurity: risks, vulnerabilities and countermeasures to prevent social engineering attacks. International Journal of Advanced Computer Research. 2016; 6(23):31-8.

[6][6]Farhaoui Y. How to secure web servers by the intrusion prevention system (IPS). International Journal of Advanced Computer Research. 2016; 6(23):65-71.

[7][7]Irandegani M, Bagherizadeh M. Designing an asynchronous multi-channel media access control protocol based on service quality for wireless sensor networks. International Journal of Advanced Computer Research. 2017; 7(32):190-9.

[8][8]Devaraju S, Ramakrishnan S. Performance analysis of intrusion detection system using various neural network classifiers. International conference on recent trends in information technology (ICRTIT) 2011 (pp. 1033-8).

[9][9]Brugger ST. Data mining methods for network intrusion detection. University of California at Davis. 2004.

[10][10]Sirisha GN, Shashi M. Subspace clustering for high dimensional datasets. International Journal of Advanced Computer Research. 2016; 6(26):177-84.

[11][11]Murugavalli S, Jainulabudeen SA, Kumar GS, Anuradha D. Enhancing security against hard AI problems in user authentication using CAPTCHA as graphical passwords. International Journal of Advanced Computer Research. 2016; 6(24):93-9.

[12][12]Lee W, Stolfo SJ. Data mining approaches for intrusion detection. In USENIX security symposium 1998 (pp. 79-93).

[13][13]Nalavade K, Meshram BB. Mining association rules to evade network intrusion in network audit data. International Journal of Advanced Computer Research. 2014; 4(15):560-7.

[14][14]Naoum R, Aziz S, Alabsi F. An enhancement of the replacement steady state genetic algorithm for intrusion detection. International Journal of Advanced Computer Research. 2014; 4(15):487-94.

[15][15]Lee W, Stolfo SJ, Mok KW. A data mining framework for building intrusion detection models. In proceedings of the symposium on security and privacy 1999 (pp. 120-32). IEEE.

[16][16]Tiwari R, Sinhal A. Block based text data partition with RC4 encryption for text data security. International Journal of Advanced Computer Research. 2016; 6(24):107-13.

[17][17]Sperotto A, Schaffrath G, Sadre R, Morariu C, Pras A, Stiller B. An overview of IP flow-based intrusion detection. IEEE Communications Surveys and Tutorials. 2010; 12(3):343-56.

[18][18]Li Z, Li Y, Xu L. Anomaly intrusion detection method based on k-means clustering algorithm with particle swarm optimization. In international conference on information technology, computer engineering and management sciences 2011 (pp. 157-61). IEEE.

[19][19]Manimaran A, Durairaj M. The conjectural framework for detecting DDoS attack using enhanced entropy based threshold technique (EEB-TT) in cloud environment. International Journal of Advanced Computer Research. 2016; 6(27):230-7.

[20][20]Yin-huan LI. Design of intrusion detection model based on data mining technology. In international conference on industrial control and electronics engineering 2012 (pp. 571-4). IEEE.

[21][21]Prasenna P, Kumar RK, Ramana AR, Devanbu A. Network programming and mining classifier for intrusion detection using probability classification. In international conference on pattern recognition, informatics and medical engineering 2012 (pp. 204-9). IEEE.

[22][22]Han LI. Using a dynamic K-means algorithm to detect anomaly activities. In seventh international conference on computational intelligence and security 2011 (pp. 1049-52). IEEE.

[23][23]Benaicha SE, Saoudi L, Guermeche SE, Lounis O. Intrusion detection system using genetic algorithm. In science and information conference (SAI) 2014 (pp. 564-8). IEEE.

[24][24]Thaseen IS, Kumar CA. Intrusion detection model using fusion of PCA and optimized SVM. In international conference on contemporary computing and informatics 2014 (pp. 879-84). IEEE.

[25][25]Wagh SK, Kolhe SR. Effective intrusion detection system using semi-supervised learning. In international conference on data mining and intelligent computing 2014 (pp. 1-5). IEEE.

[26][26]Sayar AA, Pawar SN, Mane V. A review of intrusion detection system in computer network. International Journal of Computer Science and Mobile Computing. 2014; 3(2):700-3.

[27][27]Bahl S, Sharma SK. Improving classification accuracy of intrusion detection system using feature subset selection. In international conference on advanced computing & communication technologies 2015 (pp. 431-6). IEEE.

[28][28]Yan C. Intelligent intrusion detection based on soft computing. In international conference on measuring technology and mechatronics automation 2015 (pp. 577-80). IEEE.

[29][29]Haidar GA, Boustany C. High perception intrusion detection system using neural networks. In international conference on complex, intelligent, and software intensive systems 2015 (pp. 497-501). IEEE.

[30][30]Kumar GR, Mangathayaru N, Narsimha G, Reddy GS. Evolutionary approach for intrusion detection. In international conference on engineering & MIS 2017 (pp. 1-6). IEEE.

[31][31]Ding S, Wang G. Research on intrusion detection technology based on deep learning. In international conference on computer and communications 2017 (pp. 1474-8). IEEE.

[32][32]Xiaofeng Z, Xiaohong H. Research on intrusion detection based on improved combination of K-means and multi-level SVM. In international conference on communication technology 2017 (pp. 2042-5). IEEE.

[33][33]Potteti S, Parati N. Intrusion detection system using hybrid fuzzy genetic algorithm. In international conference on trends in electronics and informatics 2017 (pp. 613-8). IEEE.

[34][34]Balasaraswathi VR, Sugumaran M, Hamid Y. Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms. Journal of Communications and Information Networks. 2017; 2(4):107-19.

[35][35]Shah V, Aggarwal AK, Chaubey N. Performance improvement of intrusion detection with fusion of multiple sensors. Complex & Intelligent Systems. 2017; 3(1):33-9.

[36][36]Almiani M, Ghazleh AA, Al-Rahayfeh A, Razaque A. Intelligent intrusion detection system using clustered self-organized map. In international conference on software defined systems 2018 (pp. 138-44). IEEE.

[37][37]Anwer HM, Farouk M, Abdel-Hamid A. A framework for efficient network anomaly intrusion detection with features selection. In international conference on information and communication systems 2018 (pp. 157-62). IEEE.

[38][38]Pamukov ME, Poulkov VK. Multiple negative selection algorithm: improving detection error rates in IoT intrusion detection systems. In international conference on intelligent data acquisition and advanced computing systems: technology and applications 2017 (pp. 543-7). IEEE.

[39][39]Desai AS, Gaikwad DP. Real time hybrid intrusion detection system using signature matching algorithm and fuzzy-GA. In international conference on advances in electronics, communication and computer technology 2016 (pp. 291-4). IEEE.

[40][40]Maske SA, Parvat TJ. Advanced anomaly intrusion detection technique for host based system using system call patterns. In international conference on inventive computation technologies 2016 (pp. 1-4). IEEE.

[41][41]Garg A, Maheshwari P. Performance analysis of snort-based intrusion detection system. In international conference on advanced computing and communication systems 2016 (pp. 1-5). IEEE.

[42][42]Mehmood T, Rais HB. Machine learning algorithms in context of intrusion detection. In international conference on computer and information sciences 2016 (pp. 369-73). IEEE.

[43][43]Gupta D, Singhal S, Malik S, Singh A. Network intrusion detection system using various data mining techniques. In international conference on research advances in integrated navigation systems 2016 (pp. 1-6). IEEE.