Abstract
Mining the frequent pattern deals with the finding patterns in large set of data, subsequences and substructures that occur in a database frequently. Likewise, We can use Frequent pattern mining for MANET nodes in order to identify the paths which are participated in frequent data transaction among the various Mobile adhoc network nodes. The network data stream is a long and continuous sequence of data sets transmitted over the network. The OCA (Online Combinatorial Approximation) algorithm is used in the data stream for mining online data. The processing time of OCA was much less and accuracy of its approximate result was quite high like other traditional mining methods. The Data Path Combinatorial Approximation (DPCA) algorithm deals with a frequent pathset mining over the MANET data flow. The pathset is generation of path from the set of paths on any node which are provided paths to various other nodes participating in the data transmission. The mining algorithm is based on Approximate Inclusion–Exclusion technique. Without continual path scanning, approximate counts are calculated for the pathsets. Skip and complete technique and group count technique were combined together and integrated into the DPCA algorithm to improve the MANET performance in terms of identifying fool around (misbehaving) nodes.








Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on very large data bases (VLDB ’94). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 487–499
Aranganathan A, Suriyakala CD (2019) An efficient secure detection and prevention of malevolent nodes with lightweight surprise check scheme using trusted mobile agents in mobile ad-hoc networks. J Ambient Intell Human Comput 10:3493–3503. https://doi.org/10.1007/s12652-018-1069-8
Deng Z-H, Lv S-L (2014) Fast mining frequent itemsets using nodesets. Expert Syst Appl 41:4505–4512. https://doi.org/10.1016/j.eswa.2014.01.025
Feng T, Chang Y (2011) Combinatorial constructions for optimal two-dimensional optical orthogonal codes with λ = 2. IEEE Trans Inf Theory 57(10):6796–6819. https://doi.org/10.1109/TIT.2011.2165805
Garofalakis M, Gehrke J, Rastogi R (2002) Querying and mining data streams: you only get one look a tutorial. In: Proceedings of the 2002 ACM SIGMOD international conference on Management of data (SIGMOD '02). Association for Computing Machinery, New York, p 635. https://doi.org/10.1145/564691.564794
Gomathy V, Padhy N, Samanta D et al (2020) Malicious node detection using heterogeneous cluster based secure routing protocol (HCBS) in wireless adhoc sensor networks. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01797-3
Gurung S, Chauhan S (2018) A novel approach for mitigating route request flooding attack in MANET. Wire Netw 24:2899–2914. https://doi.org/10.1007/s11276-017-1515-0
Jabas A, Garimella R, Sirandas R (2008) MANET mining: Mining step association rules. 2008 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, 589–594
Jana C, Pal M (2018) Application of bipolar intuitionistic fuzzy soft sets in decision making problem. Int J Fuzzy Syst Appl 7(3):32–55. https://doi.org/10.4018/IJFSA.2018070103
Jana C, Muhiuddin G, Pal M (2019) Multiple-attribute decision making problems based on SVTNH methods. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01568-9
Jana C, Pal M, Wang J (2019) Bipolar fuzzy Dombi aggregation operators and its application in multiple-attribute decision-making process. J Ambient Intell Human Comput 10:3533–3549. https://doi.org/10.1007/s12652-018-1076-9
Jana C, Senapati T, Pal M et al (2020) Different types of cubic ideals in BCI-algebras based on fuzzy points. Afr Mat 31:367–381. https://doi.org/10.1007/s13370-019-00728-6
Jea K-F, Li C-W (2009) Discovering frequent itemsets over transactional data streams through an efficient and stable approximate approach. Expert Syst Appl 36:12323–12331. https://doi.org/10.1016/j.eswa.2009.04.053
Jea K-F, Chang M-Y, Lin K-C (2004) An efficient and flexible algorithm for online mining of large itemsets. Inf Process Lett 92(6):311–316
Karthikeyan K, Ramesh S, Kirubakaran N et al (2020) Advanced resilient data consigning mechanism for mobile adhoc networks. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01820-7
Kessl R (2016) Probabilistic Static Load-Balancing of Parallel Mining of Frequent Sequences. IEEE Trans Knowl Data Eng 28(5):1299–1311. https://doi.org/10.1109/TKDE.2016.2515622
Kukreja D, Dhurandher SK, Reddy BVR (2018) Power aware malicious nodes detection for securing MANETs against packet forwarding misbehavior attack. J Ambient Intell Human Comput 9:941–956. https://doi.org/10.1007/s12652-017-0496-2
Kumar A, Pais AR (2019) A new combinatorial design based key pre-distribution scheme for wireless sensor networks. J Ambient Intell Human Comput 10:2401–2416. https://doi.org/10.1007/s12652-018-0902-4
Li C-W, Jea K-F, Lin R-P, Yen S-F, Hsu C-W (2012) Mining frequent patterns from dynamic data streams with data load management. J Syst Softw 85(6):1346–1362. https://doi.org/10.1016/j.jss.2012.01.024
Linial N, Nisan N (1990) Approximate inclusion-exclusion. Combinatorica 10:349–365. https://doi.org/10.1007/BF02128670
Liu CL (1968) Introduction to combinatorial mathematics. McGraw-Hill, New York
Liu J, Wang K, Fung BCM (2016) Mining high utility patterns in one phase without generating candidates. IEEE Trans Knowl Data Engin 28(5):1245–1257. https://doi.org/10.1109/TKDE.2015.2510012
Manku GS, Motwani R (2002) Approximate frequency counts over data streams. In: Proceedings of the 28th International Conference on very large data bases (VLDB ’02). VLDB Endowment, pp 346–357
Nivedita V, Nandhagopal N (2020) Improving QoS and efficient multi-hop and relay based communication frame work against attacker in MANET. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01787-5
Qu Z, Keeney J, Robitzsch S, Zaman F, Wang X (2016) Multilevel pattern mining architecture for automatic network monitoring in heterogeneous wireless communication networks. China Commun 13(7):108–116. https://doi.org/10.1109/CC.2016.7559082
Rashid MM, Gondal I, Kamruzzaman J (2015) Share-frequent sensor patterns mining from wireless sensor network data. IEEE Trans Parallel Distrib Syst 26(12):3471–3484. https://doi.org/10.1109/TPDS.2014.2377713
Rawassizadeh R, Momeni E, Dobbins C, Gharibshah J, Pazzani M (2016) Scalable daily human behavioral pattern mining from multivariate temporal data. IEEE Trans Knowl Data Eng 28:3098–3112. https://doi.org/10.1109/TKDE.2016.2592527
Sumalatha MS, Nandalal V (2020) An intelligent cross layer security based fuzzy trust calculation mechanism (CLS-FTCM) for securing wireless sensor network (WSN). J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01834-1
Tseng VS, Wu C, Fournier-Viger P, Yu PS (2016) Efficient algorithms for mining top-K high utility itemsets. IEEE Trans Knowl Data Eng 28(1):54–67. https://doi.org/10.1109/TKDE.2015.2458860
Zhang Y, Zhang F, Jin Z, Bakos JD (2013) An FPGA-based accelerator for frequent itemset mining. ACM Trans Reconfig Technol Syst 6(1 Article 2):17. https://doi.org/10.1145/2457443.2457445
Zhang F, Liu M, Gui F et al (2015a) A distributed frequent itemset mining algorithm using Spark for Big Data analytics. Cluster Comput 18:1493–1501. https://doi.org/10.1007/s10586-015-0477-1
Zhang S, Du Z, Wang JTL (2015b) New techniques for mining frequent patterns in unordered trees. IEEE Trans Cybern 45(6):1113–1125. https://doi.org/10.1109/TCYB.2014.2345579
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Arun, S., Sudharson, K. DEFECT: discover and eradicate fool around node in emergency network using combinatorial techniques. J Ambient Intell Human Comput 14, 5995–6006 (2023). https://doi.org/10.1007/s12652-020-02606-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-02606-7