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Intelligent content-based cybercrime detection in online social networks using cuckoo search metaheuristic approach

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Abstract

The subject of content-based cybercrime has put on substantial coverage in recent past. It is the need of the time for web-based social media providers to have the capability to distinguish oppressive substance both precisely and proficiently to secure their clients. Support vector machine (SVM) is usually acknowledged as an efficient supervised learning model for various classification problems. Nevertheless, the success of an SVM model relies upon the ideal selection of its parameters as well as the structure of the data. Thus, this research work aims to concurrently optimize the parameters and feature selection with a target to build the quality of SVM. This paper proposes a novel hybrid model that is the integration of cuckoo search and SVM, for feature selection and parameter optimization for efficiently solving the problem of content-based cybercrime detection. The proposed model is tested on four different datasets obtained from Twitter, ASKfm and FormSpring to identify bully terms using Scikit-Learn library and LIBSVM of Python. The results of the proposed model demonstrate significant improvement in the performance of classification on all the datasets in comparison to recent existing models. The success rate of the SVM classifier with the excellent recall is 0.971 via tenfold cross-validation, which demonstrates the high efficiency and effectiveness of the proposed model.

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References

  1. Kraut R, Patterson M, Lundmark V, Kiesler S, Mukophadhyay T, Scherlis W (1998) Internet paradox: a social technology that reduces social involvement and psychological well-being? Am Psychol 53(9):1017

    Article  Google Scholar 

  2. Harridge-March S, Dunne Á, Lawlor MA, Rowley J (2010) Young people’s use of online social networking sites–a uses and gratifications perspective. J Res Interact Mark 4(1):46–58

    Article  Google Scholar 

  3. Yar M, Steinmetz KF (2019) Cybercrime and society. SAGE Publications Limited, Thousand Oaks

    Google Scholar 

  4. Jahankhani H, Al-Nemrat A, Hosseinian-Far A (2014) Cybercrime classification and characteristics. In: Cyber Crime and Cyber Terrorism Investigator’s Handbook 2014 Jan 1. Syngress, pp 149–164

  5. Thangiah M, Basri S, Sulaiman S (2012) A framework to detect cybercrime in the virtual environment. In: 2012 International Conference on Computer and Information Science (ICCIS) 2012 Jun 12, vol 1. IEEE, pp 553–557

  6. Kim W, Jeong OR, Kim C, So J (2011) The dark side of the internet: attacks, costs and responses. Inf Syst 36(3):675–705

    Article  Google Scholar 

  7. Aboujaoude E, Savage MW, Starcevic V, Salame WO (2015) Cyberbullying: review of an old problem gone viral. J Adolesc Health 57(1):10–18

    Article  Google Scholar 

  8. Hinduja S, Patchin JW (2014) Bullying beyond the schoolyard: preventing and responding to cyberbullying. Corwin Press, Thousand Oaks

    Google Scholar 

  9. Xu JM, Jun KS, Zhu X, Bellmore A (2012) Learning from bullying traces in social media. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 2012 Jun 3. Association for Computational Linguistics, pp 656–666

  10. Juvonen J, Gross EF (2008) Extending the school grounds?—bullying experiences in cyberspace. J Sch Health 78(9):496–505

    Article  Google Scholar 

  11. Zhao R, Zhou A, Mao K (2016) Automatic detection of cyberbullying on social networks based on bullying features. In: Proceedings of the 17th International Conference on Distributed Computing and Networking 2016 Jan 4. ACM, p 43

  12. Zhao R, Mao K (2016) Cyberbullying detection based on semantic-enhanced marginalized denoising auto-encoder. IEEE Trans Affect Comput 8(3):328–339

    Article  Google Scholar 

  13. Dinakar K, Jones B, Havasi C, Lieberman H, Picard R (2012) Common sense reasoning for detection, prevention, and mitigation of cyberbullying. ACM Trans Interact Intell Syst (TiiS) 2(3):18

    Google Scholar 

  14. Dinakar K, Reichart R, Lieberman H (2011) Modeling the detection of textual cyberbullying. In: Fifth International AAAI Conference on Weblogs and Social Media 2011 Jul 6

  15. Dadvar M, Trieschnigg D, Ordelman R, de Jong F (2013) Improving cyberbullying detection with user context. In: European Conference on Information Retrieval 2013 Mar 24. Springer, Berlin, pp 693–696

  16. Reynolds K, Kontostathis A, Edwards L (2011) Using machine learning to detect cyberbullying. In: 2011 10th International Conference on Machine Learning and Applications and Workshops 2011 Dec 18, vol 2. IEEE, pp 241–244

  17. Van Hee C, Jacobs G, Emmery C, Desmet B, Lefever E, Verhoeven B, De Pauw G, Daelemans W, Hoste V (2018) Automatic detection of cyberbullying in social media text. PLoS ONE 13(10):e0203794

    Article  Google Scholar 

  18. Zhang LB, Peng F, Qin L, Long M (2018) Face spoofing detection based on color texture Markov feature and support vector machine recursive feature elimination. J Vis Commun Image Represent 1(51):56–69

    Article  Google Scholar 

  19. Yu H, Kim S (2012) SVM tutorial—classification, regression and ranking. In: Handbook of natural computing, pp 479–506

  20. Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University

  21. Zhang J, Jin X, Sun J, Wang J, Sangaiah AK (2018) Spatial and semantic convolutional features for robust visual object tracking. Multimed Tools Appl. https://doi.org/10.1007/s11042-018-6562-8

    Article  Google Scholar 

  22. Tang Z, Liu K, Xiao J, Yang L, Xiao Z (2017) A parallel k-means clustering algorithm based on redundance elimination and extreme points optimization employing MapReduce. Concurr Comput Pract Exp 29(20):e4109

    Article  Google Scholar 

  23. Luo XQ, Liu LB, Ouyang A, Long G (2018) B-spline collocation and self-adapting differential evolution (jDE) algorithm for a singularly perturbed convection–diffusion problem. Soft Comput 22(8):2683–2693

    Article  Google Scholar 

  24. He P, Deng Z, Gao C, Wang X, Li J (2017) Model approach to grammatical evolution: deep-structured analyzing of model and representation. Soft Comput 21(18):5413–5423

    Article  Google Scholar 

  25. Wang J, Ju C, Gao Y, Sangaiah AK, Kim GJ (2018) A PSO based energy efficient coverage control algorithm for wireless sensor networks. Comput Mater Contin 1(56):433–446

    Google Scholar 

  26. Huang J, Xu Y, Peng X, Hu L, Yang J (2018) Development of a human head and neck muscle activation control model based on BPNN. J Intell Fuzzy Syst 34(2):1161–1167

    Article  Google Scholar 

  27. Wang J, Gao Y, Liu W, Sangaiah AK, Kim HJ (2019) An improved routing schema with special clustering using PSO algorithm for heterogeneous wireless sensor network. Sensors 19(3):671

    Article  Google Scholar 

  28. Zhang Z, Li Y, Wang C, Wang M, Tu Y, Wang J (2018) An ensemble learning method for wireless multimedia device identification. Secur Commun Netw. https://doi.org/10.1155/2018/5264526

    Article  Google Scholar 

  29. Sun R, Shi L, Yin C, Wang J (2019) An improved method in deep packet inspection based on regular expression. J Supercomput 75(6):3317–3333

    Article  Google Scholar 

  30. Yin C, Shi L, Wang J (2018) Improved collaborative filtering recommendation algorithm based on differential privacy protection. In: Advanced Multimedia and Ubiquitous Engineering 2018 Apr 23. Springer, Singapore, pp 253–258

  31. Yang XS (2010) Nature-inspired metaheuristic algorithms. Luniver press, Bristol

    Google Scholar 

  32. Gill SS, Garraghan P, Stankovski V, Casale G, Thulasiram RK, Ghosh SK, Ramamohanarao K, Buyya R (2019) Holistic resource management for sustainable and reliable cloud computing: an innovative solution to global challenge. J Syst Softw

  33. Yin D, Xue Z, Hong L, Davison BD, Kontostathis A, Edwards L (2009) Detection of harassment on web 2.0. In: Proceedings of the Content Analysis in the WEB. 2009 Apr 20, vol 2, pp 1–7

  34. Bayzick J, Kontostathis A, Edwards L (2011) Detecting the presence of cyberbullying using computer software. In: 3rd annual ACM web science conference (WebSci ’11), pp 1–2

  35. Nahar V, Unankard S, Li X, Pang C (2012) Sentiment analysis for effective detection of cyber bullying. In: Asia-Pacific Web Conference 2012 Apr 11. Springer, Berlin, pp 767–774

  36. Nahar V, Li X, Pang C, Zhang Y (2013) Cyberbullying detection based on text-stream classification. In: The 11th Australasian Data Mining Conference (AusDM 2013) 2013 Jan 1

  37. Nahar V, Al-Maskari S, Li X, Pang C (2014) Semi-supervised learning for cyberbullying detection in social networks. In: Australasian Database Conference 2014 Jul 14. Springer, Cham, pp 160–171

  38. Huang Q, Singh VK, Atrey PK (2014) Cyber bullying detection using social and textual analysis. In: Proceedings of the 3rd International Workshop on Socially-Aware Multimedia 2014 Nov 7. ACM, pp. 3–6

  39. Mangaonkar A, Hayrapetian A, Raje R (2015) Collaborative detection of cyberbullying behavior in Twitter data. In: 2015 IEEE International Conference on Electro/Information Technology (EIT) 2015 May 21. IEEE, pp 611–616

  40. Al-garadi MA, Varathan KD, Ravana SD (2016) Cybercrime detection in online communications: the experimental case of cyberbullying detection in the Twitter network. Comput Hum Behav 1(63):433–443

    Article  Google Scholar 

  41. Rafiq RI, Hosseinmardi H, Han R, Lv Q, Mishra S (2018) Scalable and timely detection of cyberbullying in online social networks. In: Proceedings of the 33rd Annual ACM Symposium on Applied Computing 2018 Apr 9. ACM, pp 1738–1747

  42. Agrawal S, Awekar A (2018) Deep learning for detecting cyberbullying across multiple social media platforms. In: European Conference on Information Retrieval 2018 Mar 26. Springer, Cham, pp 141–153

  43. Cheng L, Li J, Silva YN, Hall DL, Liu H (2019) Xbully: cyberbullying detection within a multi-modal context. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining 2019 Jan 30. ACM, pp 339–347

  44. Gunn SR (1998) Support vector machines for classification and regression. ISIS Tech Rep 14(1):5–16

    Google Scholar 

  45. Samanta BI, Al-Balushi KR, Al-Araimi SA (2003) Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Eng Appl Artif Intell 16(7–8):657–665

    Article  Google Scholar 

  46. Long M, Zeng Y (2019) Detecting iris liveness with batch normalized convolutional neural network. Comput Mater Contin 58(2):493–504

    Article  Google Scholar 

  47. Huang C, Liu B (2019) New studies on dynamic analysis of inertial neural networks involving non-reduced order method. Neurocomputing 24(325):283–287

    Article  Google Scholar 

  48. Zeng D, Dai Y, Li F, Wang J, Sangaiah AK (2019) Aspect based sentiment analysis by a linguistically regularized CNN with gated mechanism. J Intell Fuzzy Syst 36:3971–3980

    Article  Google Scholar 

  49. Wang D, Huang L, Tang L (2017) Synchronization criteria for discontinuous neural networks with mixed delays via functional differential inclusions. IEEE Trans Neural Netw Learn Syst 29(5):1809–1821

    Article  MathSciNet  Google Scholar 

  50. Chapelle O, Vapnik V, Bousquet O, Mukherjee S (2002) Choosing multiple parameters for support vector machines. Mach Learn 46(1–3):131–159

    Article  Google Scholar 

  51. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: 2009 World Congress on Nature and Biologically Inspired Computing (NaBIC) 2009 Dec 9. IEEE, pp 210–214

  52. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1–3):37–52

    Article  Google Scholar 

  53. Valentini G, Dietterich TG (2004) Bias-variance analysis of support vector machines for the development of SVM-based ensemble methods. J Mach Learn Res 5(Jul):725–775

    MathSciNet  MATH  Google Scholar 

  54. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27

    Google Scholar 

  55. Williamson DF, Parker RA, Kendrick JS (1989) The box plot: a simple visual method to interpret data. Ann Intern Med 110(11):916–921

    Article  Google Scholar 

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Acknowledgements

One of the authors, Amanpreet Singh, gratefully acknowledges University Grants Commission [No. F.159(JUNE 2014)/2014(NET)], for awarding him the Fellowship [Ref. No.: 3505/(NET-JUNE 2014)] to carry out this research work. We would like to thank Dr. Sukhpal Singh Gill (Queen Mary University of London, UK) for his valuable comments and useful suggestions to improve the quality of the paper.

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Correspondence to Maninder Kaur.

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Singh, A., Kaur, M. Intelligent content-based cybercrime detection in online social networks using cuckoo search metaheuristic approach. J Supercomput 76, 5402–5424 (2020). https://doi.org/10.1007/s11227-019-03113-z

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