Soft Computing

, Volume 21, Issue 21, pp 6297–6315 | Cite as

Developing a trust model for pervasive computing based on Apriori association rules learning and Bayesian classification

  • Gianni D’Angelo
  • Salvatore Rampone
  • Francesco Palmieri
Methodologies and Application


Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work, we review these general issues and propose a pervasive computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some artificial intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach, we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones.


Pervasive computing Trust model Artificial intelligence Apriori algorithm Naïve Bayes classifier 


Compliance with ethical standards

Conflict of interest

There are no authors’ conflicts of interest directly or indirectly related to this research work that can potentially influence or impart bias on it. Furthermore, the work does not contain any studies with human participants or animals performed by any of the authors.


  1. Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. ACM SIGMOD Rec 22(2):207–216CrossRefGoogle Scholar
  2. Agrawal R, Srikant R, et al (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th international conference on very large data bases, VLDB, vol 1215, pp 487–499Google Scholar
  3. Al-Karkhi A, Al-Yasiri A, Linge N (2012) Privacy, trust and identity in pervasive computing: a review of technical challenges and future research directions. Int J Distrib Parallel Syst (IJDPS) 3(3):197–218Google Scholar
  4. Blaze M, Feigenbaum J, Lacy J (1996) Decentralized trust management. In: 1996 IEEE symposium on security and privacy, 1996. Proceedings. IEEE, pp 164–173Google Scholar
  5. Blaze M, Ioannidis J, Keromytis AD (2003) Experience with the keynote trust management system: applications and future directions. In: Nixon P, Terzis S (eds) Trust management. Springer, Berlin, pp 284–300Google Scholar
  6. Boroujeni GAA (2013) A trust model for epinion dataset. In: 2013 7th International conference on e-Commerce in developing countries: with focus on e-Security (ECDC). IEEE, pp 1–7Google Scholar
  7. Bouckaert RR, Frank E, Hall MA, Holmes G, Pfahringer B, Reutemann P, Witten IH (2010) Weka—experiences with a java open-source project. J Mach Learn Res 11:2533–2541zbMATHGoogle Scholar
  8. Calvo RA, D’Mello S (2010) Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Trans Affect Comput 1(1):18–37Google Scholar
  9. Carullo G, Castiglione A, Cattaneo G, De Santis A, Fiore U, Palmieri F (2013) Feeltrust: providing trustworthy communications in ubiquitous mobile environment. In: 2013 IEEE 27th international conference on advanced information networking and applications (AINA). IEEE, pp 1113–1120Google Scholar
  10. Carullo G, Castiglione A, De Santis A, Palmieri F (2015) A triadic closure and homophily-based recommendation system for online social networks. World Wide Web 18(6):1579–1601Google Scholar
  11. Chen Z, Guo S, Zheng K, Yang Y (2007) Modeling of man-in-the-middle attack in the wireless networks. In: International conference on wireless communications, networking and mobile computing, 2007 (WiCom 2007). IEEE, pp 2255–2258Google Scholar
  12. D’Angelo G, Rampone S, Palmieri F (2015) An artificial intelligence-based trust model for pervasive computing. In: Proceedings international conference on P2P, parallel, grid, cloud and internet computing (3PGCIC) 2015Google Scholar
  13. Dasgupta P (1988) Chapter 4: Trust as a commodity. In: Gambetta D (ed) Trust: making and breaking cooperative relations. Basil Blackwell, Oxford, vol 4, pp 49–72Google Scholar
  14. Davis J, Goadrich M (2006) The relationship between precision–recall and ROC curves. In: Proceedings of the 23rd international conference on machine learning. ACM, pp 233–240Google Scholar
  15. Dellarocas C (2003) The digitization of word of mouth: promise and challenges of online feedback mechanisms. Manag Sci 49(10):1407–1424CrossRefGoogle Scholar
  16. Denko MK, Sun T, Woungang I (2011) Trust management in ubiquitous computing: a Bayesian approach. Comput Commun 34(3):398–406CrossRefGoogle Scholar
  17. Ekman P (2007) Emotions revealed: recognizing faces and feelings to improve communication and emotional life. Macmillan, New YorkGoogle Scholar
  18. Ellison C, Frantz B, Lampson B, Rivest R, Thomas B, Ylonen T (1999) SPKI certificate theory. IETF RFC 2693Google Scholar
  19. Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8):861–874MathSciNetCrossRefGoogle Scholar
  20. Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 17(3):37Google Scholar
  21. Ficco M, D’Arienzo M, D’Angelo G (2007) A bluetooth infrastructure for automatic services access in ubiquitous and nomadic computing environments. In: Proceedings of the 5th ACM international workshop on Mobility management and wireless access. ACM, pp 17–24Google Scholar
  22. Gallego D, Huecas G (2012) An empirical case of a context-aware mobile recommender system in a banking environment. In: 2012 third FTRA international conference on mobile, ubiquitous, and intelligent computing (MUSIC). IEEE, pp 13–20Google Scholar
  23. Gambetta D (1988) Chapter 13: Can we trust trust? Trust: making and breaking cooperative relations. Basil Blackwell, Oxford, pp 213–237Google Scholar
  24. Golosio B, Cangelosi A, Gamotina O, Masala GL (2015) A cognitive neural architecture able to learn and communicate through natural language. Plos One J 10(11):1–37CrossRefGoogle Scholar
  25. Gonzalez JM, Anwar M, Joshi JB (2011) A trust-based approach against ip-spoofing attacks. In: 2011 Ninth annual international conference on privacy, security and trust (PST). IEEE, pp 63–70Google Scholar
  26. Gorgoglione M, Panniello U (2009) Including context in a transactional recommender system using a pre-filtering approach: two real e-commerce applications. In: International conference on advanced information networking and applications: workshops, 2009. AINA’09. IEEE, pp 667–672Google Scholar
  27. Grandison T, Sloman M (2000) A survey of trust in internet applications. IEEE Commun Surv Tutor 3(4):2–16CrossRefGoogle Scholar
  28. Han E, Karypis G, Kumar V (1997) Min-apriori: An algorithm for finding association rules in data with continuous attributes. Tech rep TR-97-068: Department of Computer Science and Engineering, University of MinnesotaGoogle Scholar
  29. Hidber C (1999) Online association rule mining. ACM 28(2):145–156Google Scholar
  30. Hoffman K, Zage D, Nita-Rotaru C (2009) A survey of attack and defense techniques for reputation systems. ACM Comput Surv (CSUR) 42(1):1CrossRefGoogle Scholar
  31. Ivanova M (2013) Researching affective computing techniques for intelligent tutoring systems. In: 2013 International conference on interactive collaborative learning (ICL). IEEEGoogle Scholar
  32. Jiang L, Meng FR, Zhou Y (2011) Q-apriori algorithm of multivalue attribute association rules mining. Comput Eng 37(9):81–83Google Scholar
  33. Kagal L, Finin T, Joshi A (2001) Trust-based security in pervasive computing environments. Computer 34(12):154–157CrossRefGoogle Scholar
  34. Kamvar SD, Schlosser MT, Garcia-Molina H (2003) The eigentrust algorithm for reputation management in p2p networks. In: Proceedings of the 12th international conference on World Wide Web. ACM, pp 640–651Google Scholar
  35. Kantor PB, Rokach L, Ricci F, Shapira B (2011) Recommender systems handbook. Springer, BerlinzbMATHGoogle Scholar
  36. Khiabani H, Sidek ZM, Manan JlA (2010) Towards a unified trust model in pervasive systems. In: 2010 IEEE 24th international conference on advanced information networking and applications workshops (WAINA). IEEE, pp 831–835Google Scholar
  37. Kim JH, Chung KY, Ryu JK, Rim KW, Lee JH (2008) Personal history based recommendation service system with collaborative filtering. In: Advanced software engineering and its applications, 2008. ASEA 2008. IEEE, pp 261–264Google Scholar
  38. Konar A (1999) Artificial intelligence and soft computing: behavioral and cognitive modeling of the human brain. CRC Press, Boca Raton, FLCrossRefGoogle Scholar
  39. Kurniawan A, Kyas M (2015) A trust model-based Bayesian decision theory in large scale internet of things. In: 2015 IEEE tenth international conference on intelligent sensors, sensor networks and information processing (ISSNIP). IEEE, pp 1–5Google Scholar
  40. Leu FY, Liu JC, Hsu YT, Huang YL (2014) The simulation of an emotional robot implemented with fuzzy logic. Soft Comput J 18(9):1729–1743CrossRefGoogle Scholar
  41. Lewis DD (1998) Naive (bayes) at forty: The independence assumption in information retrieval. In: Machine learning: ECML-98, Springer, Berlin, pp 4–15Google Scholar
  42. Liu Y, Li F (2006) Pca: a reference architecture for pervasive computing. In: 2006 1st international symposium on pervasive computing and applications. IEEE, pp 99–103Google Scholar
  43. Malaga RA (2001) Web-based reputation management systems: problems and suggested solutions. Electron Commer Res 1(4):403–417CrossRefzbMATHGoogle Scholar
  44. Nguyen CT, Camp O, Loiseau S (2007) A Bayesian network based trust model for improving collaboration in mobile ad hoc networks. In: 2007 IEEE International conference on research, innovation and vision for the future. IEEE, pp 144–151Google Scholar
  45. Prax J (2003) The role of trust in collective performance. Manual of knowledge management—a second generation approachGoogle Scholar
  46. Ranganathan K (2004) Trustworthy pervasive computing: the hard security problems. In: Proceedings of the second IEEE annual conference on pervasive computing and communications workshops. IEEE, pp 117–121Google Scholar
  47. Razavi N, Rahmani AM, Mohsenzadeh M (2009) A context-based trust management model for pervasive computing systems. Int J Comput Sci Inf Secur 6(1):137–142Google Scholar
  48. Rodrigo MMT (2012) The effects of an interactive software agent on student affective dynamics while using; an intelligent tutoring system. IEEE Trans Affect Comput 3(2):224–236CrossRefGoogle Scholar
  49. Salleb-Aouissi A, Vrain C, Nortet C, Kong X, Rathod V, Cassard D (2013) Quantminer for mining quantitative association rules. J Mach Learn Res 14(1):3153–3157zbMATHGoogle Scholar
  50. Schafer JB, Konstan J, Riedl J (1999) Recommender systems in e-commerce. In: Proceedings of the 1st ACM conference on electronic commerce. ACM, pp 158–166Google Scholar
  51. Sharifi A, Khosravi M, Shah A (2013) Web-based reputation management systems: problems and suggested solutions. Int J Eng Innov Technol (IJEIT) 3(4):40–45Google Scholar
  52. Spiekermann S, Grossklags J, Berendt B (2001) E-privacy in 2nd generation e-commerce: privacy preferences versus actual behavior. In: Proceedings of the 3rd ACM conference on electronic commerce. ACM, pp 38–47Google Scholar
  53. Srikant R, Agrawal R (1996) Mining quantitative association rules in large relational tables. ACM SIGMOD Rec 25(2):1–12CrossRefGoogle Scholar
  54. Theodorakopoulos G, Baras JS (2006) On trust models and trust evaluation metrics for ad hoc networks. IEEE J Sel Areas Commun 24(2):318–328CrossRefGoogle Scholar
  55. Ullman JD (2000) A survey of association-rule mining. Proceedings of the third international conference, discovery science. Springer, Berlin, pp 1–14Google Scholar
  56. Viana MM, De Souza JN (2007) A complex analysis approach to the modelling for the tracing and identification of denial-of-service attackers. In: IEEE international conference on telecommunications and Malaysia international conference on communications, 2007. ICT-MICC 2007. IEEE, pp 124–128Google Scholar
  57. Wang K, An N, Li BN, Zhang Y (2015) Speech emotion recognition using Fourier parameters. IEEE Trans Affect Comput 6(1):69–75Google Scholar
  58. Wang Q, Wang L (2008) A vector-based trust model for p2p e-commerce. In: Fourth international conference on natural computation, 2008. ICNC’08. IEEE, vol 7, pp 117–123Google Scholar
  59. Wei K, Huang J, Fu S (2007) A survey of e-commerce recommender systems. In: 2007 International conference on service systems and service management. IEEE, pp 1–5Google Scholar
  60. Wei Z, Tang H, Yu FR, Mason P (2014) Trust establishment based on bayesian networks for threat mitigation in mobile ad hoc networks. In: 2014 IEEE military communications conference (MILCOM). IEEE, pp 171–177Google Scholar
  61. Weiser M (1999) The computer for the 21st century. Mobile Comput Commun Rev 3(3):3–11CrossRefGoogle Scholar
  62. Wu J, Ping L, Wang H, Lin Z, Zhang Q (2008) Research on improved collaborative filtering-based mobile e-commerce personalized recommender system. In: International conference on multimedia and information technology, 2008. MMIT’08. IEEE, pp 143–146Google Scholar
  63. Xiong L, Liu L (2003) A reputation-based trust model for peer-to-peer e-commerce communities. In: IEEE international conference on E-Commerce, 2003. CEC 2003. IEEE, pp 275–284Google Scholar
  64. Xiong L, Liu L (2004) Peertrust: supporting reputation-based trust for peer-to-peer electronic communities. IEEE Trans Knowl Data Eng 16(7):843–857CrossRefGoogle Scholar
  65. Yao Z, Kim D, Lee I, Kim K, Jang J (2005) A security framework with trust management for sensor networks. In: Workshop of the 1st international conference on security and privacy for emerging areas in communication networks, 2005. IEEE, pp 190–198Google Scholar
  66. Yuan W, Guan D, Lee S, Lee Y (2006) A dynamic trust model based on naive bayes classifier for ubiquitous environments. In: Gerndt M, Kranzlmüller D (eds) High performance computing and communications. Springer, Berlin, pp 562–571Google Scholar
  67. Zhang H (2005) Trust promoting seals in electronic markets: impact on online shopping decisions. J Inf Technol Theory Appl (JITTA) 6(4):29–40Google Scholar
  68. Zhou S, He S, Wang W (2013) Improved apriori for continuous attributes. Int J Inf Sci Intell Syst 2(1):37–43Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Gianni D’Angelo
    • 1
  • Salvatore Rampone
    • 1
  • Francesco Palmieri
    • 2
  1. 1.Department of Science and TechnologyUniversity of SannioBeneventoItaly
  2. 2.Department of Computer ScienceUniversity of SalernoFiscianoItaly

Personalised recommendations