An Immunological-Based Simulation: A Case Study of Risk Concentration for Mobile Spam Context Assessment

Kamahazira Zainal, Mohd Zalisham Jali

Abstract


Over the past two decades, there has been a substantial increase in spam messages that caused critical impact loss. Besides the factor of integration of Internet and mobile technology, this issue is also due to the human’s reaction towards spam. This paper presents RiCCA or Risk Concentration for Context Assessment model that performs a risk classification of text spam messages in Short Message Service (SMS) format. The identified risk levels will assist users in anticipating the potential impact of the spam message that they have been receiving. Danger Theory, a prominent theory from Artificial Immune Systems (AIS), inspires the developed model. During the simulation phase, an immunological-based testing lifecycle is applied, with the deployment of the dataset that is shared at UCI Machine Learning Repository and self-collected messages. The performance of the testing revealed a distinctive result, which more than 80% of true positive rate is achieved, employed with two variants algorithm from the Danger Theory; Dendritic Cell Algorithm (DCA) and Deterministic Dendritic Cell Algorithm (dDCA). This simulation demonstrated that the Danger Theory as a feasible model to be applied in measuring the risk of spam. The further articulation on how this immunological-based testing lifecycle is applied in computer simulation and adopting mobile spam as the case study is clarified thoroughly.

Keywords


danger theory; risk classification; SMS spam messages; mobile spam; knowledge discovery; information retrieval; immunological simulation.

Full Text:

PDF

References


P. Parham, The Immune System, Fourth Edition. Taylor & Francis Group, 2014.

D. Dasgupta, “Advances in artificial immune systems,†IEEE Comput. Intell. Mag., vol. 1, no. 4, pp. 40–49, Nov. 2006.

T. Lu, K. Zheng, R. Fu, Y. Liu, B. Wu, and S. Guo, “A Danger Theory Based Mobile Virus Detection Model and Its Application in Inhibiting Virus,†J. Networks, vol. 7, no. 8, pp. 1227–1232, Aug. 2012.

M. Gong, J. Zhang, J. Ma, and L. Jiao, “An efficient negative selection algorithm with further training for anomaly detection,†Knowledge-Based Syst., vol. 30, pp. 185–191, Jun. 2012.

L. E. Jim and M. A. Gregory, “A Review of Artificial Immune System Based Security Frameworks for MANET,†Int. J. Commun. Netw. Syst. Sci., vol. 9, no. 1, pp. 1–18, 2016.

H. Yang, T. Li, X. Hu, F. Wang, and Y. Zou, “A Survey of Artificial Immune System Based Intrusion Detection,†Sci. World J., vol. 2014, pp. 1–11, 2014.

Internet Governance Forumn, “Best Practice Forum on Regulation and Mitigation of Unsolicited Communications,†2014.

Cloudmark, “2013 Global Messaging Threat Report,†2013.

S. Alotaibi, S. Furnell, and N. Clarke, “A Novel Taxonomy for Mobile Applications Data,†Int. J. Cyber-Security Digit. Forensics, vol. 5, no. 3, pp. 115–121, 2016.

M. Theoharidou, A. Mylonas, and D. Gritzalis, “A Risk Assessment Method for Smartphones,†in International Information Security Conference, 2012, pp. 443–456.

D. Goel and A. K. Jain, “Mobile phishing attacks and defence mechanisms: State of art and open research challenges,†Comput. Secur., vol. 73, pp. 519–544, Mar. 2018.

J. Lin, N. Sadeh, S. Amini, J. Lindqvist, J. I. Hong, and J. Zhang, “Expectation and purpose,†in Proceedings of the 2012 ACM Conference on Ubiquitous Computing - UbiComp ’12, 2012, p. 501.

K. H. Haritha, R. S. Kumar, and M. S. Krishnan, “An Analytical Exploration on SMS Spam & User Retort,†Int. J. Pure Appl. Math., vol. 114, no. 11, pp. 147–156, 2017.

K. D. Mitnick and W. L. Simon, The Art of Deception: Controlling the Human Element of Security. 2003.

T. Chen and M.-Y. Kan, “Creating a live, public short message service corpus: the NUS SMS corpus,†Lang. Resour. Eval., vol. 47, no. 2, pp. 299–335, Aug. 2012.

N. Choudhary and A. K. Jain, “Towards Filtering of SMS Spam Messages Using Machine Learning Based Technique,†in International Conference on Advanced Informatics for Computing Research (ICAICR), vol. 712, D. Singh, B. Raman, A. K. Luhach, and P. Lingras, Eds. Singapore: Springer Singapore, 2017, pp. 18–30.

D. Suleiman and G. Al-Naymat, “SMS Spam Detection using H2O Framework,†Procedia Comput. Sci., vol. 113, pp. 154–161, 2017.

S. J. Warade, P. A.Tijare, and S. N.Sawalkar, “Implementation of SMS Spam Detection System,†Int. J. Res. Advent Technol., vol. 4, no. 5, pp. 46–52, 2016.

Y. Ge, H. Liang, L. Chen, and Q. Zhang, “The Designation of Bio-Inspired Intrusion Detection System Model in Cloud Computing Based on Machine Learning,†in International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE), 2015, pp. 1932–1937.

D. Singh and S. S. Bedi, “Novel Intrusion Detection in MANETs Based on Trust,†Int. J. Comput. Sci. Inf. Technol., vol. 6, no. 4, pp. 3556–3560, 2015.

P. Matzinger, “Tolerance, Danger, and the Extended Family,†Annu. Rev. Immunol., vol. 12, no. 1, pp. 991–1045, Apr. 1994.

P. Matzinger, “The Danger Model: A Renewed Sense of Self,†Science (80-. )., vol. 296, no. 5566, pp. 301–305, Apr. 2002.

J. Greensmith, “The Dendritic Cell Algorithm,†University of Nottingham, 2007.

J. Greensmith, U. Aickelin, and S. Cayzer, “Detecting Danger: The Dendritic Cell Algorithm,†in Robust Intelligent Systems, London: Springer London, 2008, pp. 89–112.

U. Aickelin and J. Greensmith, “Sensing danger: Innate immunology for intrusion detection,†Inf. Secur. Tech. Rep., vol. 12, no. 4, pp. 218–227, Jan. 2007.

J. Greensmith, U. Aickelin, and J. Twycross, “Articulation and Clarification of the Dendritic Cell Algorithm,†2006, pp. 404–417.

J. Greensmith and U. Aickelin, “Artificial Dendritic Cells: Multi-faceted Perspectives,†in Human-Centric Information Processing Through Granular Modelling, vol. 182, Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 375–395.

R. Oates, G. Kendall, and J. M. Garibaldi, “Frequency analysis for dendritic cell population tuning,†Evol. Intell., vol. 1, no. 2, pp. 145–157, Jun. 2008.

J. Greensmith and U. Aickelin, “The Deterministic Dendritic Cell Algorithm,†in Artificial Immune Systems, Berlin, Heidelberg: Springer Berlin Heidelberg, 2008, pp. 291–302.

C. J. Musselle, “Insights into the Antigen Sampling Component of the Dendritic Cell Algorithm,†in International Conference on Artificial Immune Systems (ICARIS), 2010, pp. 88–101.

J. Timmis, P. Andrews, N. Owens, and E. Clark, “An interdisciplinary perspective on artificial immune systems,†Evol. Intell., vol. 1, no. 1, pp. 5–26, Mar. 2008.

AISWeb, “AISWeb,†Systems, The Online Home of Artificial Immune. .

M. Abdelhaq, R. Alsaqour, M. Ismail, and S. Abdelhaq, “Dendritic Cell Fuzzy Logic Algorithm over Mobile Ad Hoc Networks,†in International Conference on Intelligent Systems, Modelling and Simulation, 2015, pp. 64–69.

A. C. M, S. Uma, and M. M. Kumar, “Detaining and Avoiding Mobile Virus Propagation by Considering Human Behavior,†Int. J. Adv. Comput. Technol., vol. 3, no. 3, pp. 619–623, 2014.

A. A. Al-Hasan and E.-S. M. El-Alfy, “Dendritic Cell Algorithm for Mobile Phone Spam Filtering,†Procedia Comput. Sci., vol. 52, pp. 244–251, 2015.

R. A. Mulay and A. S. Chauhan, “Review: Optimized Webpage Classification,†Int. J. Emerg. Trends Technol. Comput. Sci., vol. 3, no. 4, pp. 302–306, 2014.

E.-S. M. El-Alfy and A. A. Al-Hasan, “A novel bio-inspired predictive model for spam filtering based on dendritic cell algorithm,†in 2014 IEEE Symposium on Computational Intelligence in Cyber Security (CICS), 2014, pp. 1–7.

S. M. Abdulhamid et al., “A Review on Mobile SMS Spam Filtering Techniques,†IEEE Access, vol. 5, pp. 15650–15666, 2017.

T. Oda, “A Spam-Detecting Artificial Immune System,†Carleton University Ottawa, Canada, 2005.

T. A. Almeida and J. M. G. Hidalgo, “UCI Machine Learning Repository,†2012. .

S. Stepney, R. E. Smith, J. Timmis, A. M. Tyrell, M. J.Neal, and A. N.W.Hone, “Conceptual Frameworks for Artificial Immune Systems,†2005.

J. Twycross and U. Aickelin, “libtissue - implementing innate immunity,†in 2006 IEEE International Conference on Evolutionary Computation, 2010, pp. 499–506.

S. A. Hofmeyr and S. Forrest, “Architecture for an Artificial Immune System,†Evol. Comput., vol. 8, no. 4, pp. 443–473, Dec. 2000.

The Marshall Protocol Knowledge Base, “Differences Between In Vitro, In Vivo, and In Silico Studies,†Autoimmunity Research Foundation, 2012. [Online]. Available: https://mpkb.org/home/patients/assessing_literature/in_vitro_studies.

G. P. Figueredo, P.-O. Siebers, U. Aickelin, and S. Foan, “A Beginner’s Guide to Systems Simulation in Immunology,†in International Conference on Artificial Immune Systems, ICARIS 2012, 2012, pp. 57–71.

K. Zainal, N. F. Sulaiman, and M. Z. Jali, “An Analysis of Various Algorithms For Text Spam Classification and Clustering Using RapidMiner and Weka,†Int. J. Comput. Sci. Inf. Secur., vol. 13, no. 3, pp. 66–74, 2015.

K. Zainal and M. Z. Jali, “A Perception Model of Spam Risk Assessment Inspired by Danger Theory of Artificial Immune Systems,†Procedia Comput. Sci., vol. 59, pp. 152–161, 2015.

K. Zainal and M. Z. Jali, “A Review of Feature Extraction Optimization in SMS Spam Messages Classification,†in Communications in Computer and Information Science, vol. 652, 2016, pp. 158–170.

K. Zainal and M. Z. Jali, “The significant effect of feature selection methods in spam risk assessment using dendritic cell algorithm,†in 2017 5th International Conference on Information and Communication Technology (ICoIC7), 2017, pp. 1–8.

K. Zainal and M. Z. Jali, “Comparative Analysis of Danger Theory Variants in Rating Risk Concentration via Context Assessment of Text Spam Messages,†2017.

K. Zainal and M. Z. Jali, “The Design and Development of Spam Risk Assessment Prototype: In Silico of Danger Theory Variants,†Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 4, pp. 401–410, 2017.

N. Japkowicz and M. Shah, Evaluating Learning Algorithms. Cambridge: Cambridge University Press, 2011.

F. Gu, J. Greensmith, and U. Aickelin, “Further Exploration of the Dendritic Cell Algorithm: Antigen Multiplier and Time Windows,†in Artificial Immune Systems, Berlin, Heidelberg: Springer Berlin Heidelberg, 2008, pp. 142–153.




DOI: http://dx.doi.org/10.18517/ijaseit.8.3.2719

Refbacks

  • There are currently no refbacks.


Published by INSIGHT - Indonesian Society for Knowledge and Human Development