Skip to main content

Enriched multi-agent middleware for building rule-based distributed security solutions for IoT environments


The increasing number of connected devices and the complexity of Internet of Things (IoT) ecosystems are demanding new architectures for managing and securing these networked environments. Intrusion Detection Systems (IDS) are security solutions that help to detect and mitigate the threats that IoT systems face, but there is a need for new IDS strategies and architectures. This paper describes a development environment that allows the programming and debugging of distributed, rule-based multi-agent IDS solutions. The proposed solution consists in the integration of a rule engine into the agent, the use of a specialized, wrapping agent class with a graphical user interface for programming and debugging purposes, and a mechanism for the incremental composition of behaviors. A comparative study and an example IDS are used to test and show the suitability and validity of the approach. The JADE multi-agent middleware has been used for the practical implementations.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15


  1. 1.

    Walker-Roberts S, Hammoudeh M, Aldabbas O, Aydin M, Dehghantanha A (2020) Threats on the horizon: understanding security threats in the era of cyber-physical systems. J Supercomput 76(4):2643.

    Article  Google Scholar 

  2. 2.

    Savaglio C, Fortino G, Ganzha M, Paprzycki M, Bǎdicǎ C, Ivanović M (2017) Agent-based computing in the internet of things: a survey. Studies in Computational Intelligence

  3. 3.

    Coulter R, Pan L (2018) Intelligent agents defending for an IoT world: A review. Comput Secur 73(2018):439.

    Article  Google Scholar 

  4. 4.

    Bougueroua N, Mazouzi S, Belaoued M, Seddari N, Derhab A, Bouras A (2021) A survey on multi-agent based collaborative intrusion detection systems. J Artif Intell Soft Comput Res 11(2):111.

    Article  Google Scholar 

  5. 5.

    Pico-Valencia P, Holgado-Terriza JA (2018). Agentification of the Internet of Things: A systematic literature review.

    Article  Google Scholar 

  6. 6.

    Bellifemine F, Poggi A, Rimassa G (2001) JADE: A FIPA2000 compliant agent development environment. In: Proceedings of the Fifth International Conference on Autonomous Agents - AGENTS ’01, vol 153, ACM Press, New York, pp 216–217.

  7. 7.

    Aguayo-Canela FJ, Alaiz-Moretón H, García-Rodríguez I, Benavides-Cuellar C, Benítez-Andrades JA, Novais P (2019) A FIPA-compliant framework for integrating rule engines into software agents for supporting communication and collaboration in a multiagent platform. In: Rocha A, Adeli H, Reis LP, Costanzo S (eds) New knowledge in information systems and technologies. WorldCIST’19 2019. Advances in Intelligent Systems and Computing, vol 931, Cham, pp 124–133.

  8. 8.

    JC Giarratano. CLIPS 6.4 user’s guide (2014)

  9. 9.

    EJ Friedman-Hill, et al. Jess: Java Expert System Software (2018)

  10. 10.

    Proctor M (2012) Drools: a rule engine for complex event processing. In: Schürr A, Varró D, Varró G (eds) Applications of graph transformations with industrial relevance. AGTIVE 2011. Lecture Notes in Computer Science, vol 7233, Springer, Berlin

  11. 11.

    Bassiliades N (2012) Agents and knowledge interoperability in the semantic web era. In: Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics - WIMS ’12 (June 2012), 1 (2012).

  12. 12.

    Cardoso HL (2007) Integrating jade and jess. Accessed: 2020-10-05

  13. 13.

    P. Niemeyer. Beanshell - The Lightweight scripting for Java (2000)

  14. 14.

    Brahmkstri K, Thomas D, Sawant ST, Jadhav A, Kshirsagar DD (2014) Ontology based multi-agent intrusion detection system for web service attacks using self learning. In: Meghanathan N, Nagamalai D, Rajasekaran S (eds) Networks and communications (NetCom2013), Springer International Publishing, Cham, pp 265–274

  15. 15.

    Brahmi I, Brahmi H (2015) Omaids: a multi-agents intrusion detection system based ontology. In: Jackowski K, Burduk R, Walkowiak K, Wozniak M, Yin H (eds) Intelligent data engineering and automated learning – IDEAL 2015, Springer International Publishing, Cham, pp 156–163

  16. 16.

    Mehmood A, Mukherjee M, Ahmed SH, Song H, Malik KM (2018) NBC-MAIDS: Naïve Bayesian classification technique in multi-agent system-enriched IDS for securing IoT against DDoS attacks. J Supercomput 74(10):5156.

    Article  Google Scholar 

  17. 17.

    Shuang-Can Z, Chen-jun H, Wei-ming Z (2014) Multi-agent distributed intrusion detection system model based on BP neural network. Int J Secur Appl 8(2):183

    Google Scholar 

  18. 18.

    Laqtib S, Yassini KE, Hasnaoui ML (2019) A deep learning methods for intrusion detection systems based machine learning in manet. In: Proceedings of the 4th International Conference on Smart City Applications, SCA ’19, Association for Computing Machinery, New York, NY, USA.

  19. 19.

    Strzałek M, Pałka P (2012) The issue of confidentiality, authentication, integrity and data non-repudiation in the multiagent systems

  20. 20.

    Hatzivasilis G, Papadakis N, Hatzakis I, Ioannidis S, Vardakis G (2020) Artificial intelligence-driven composition and security validation of an internet of things ecosystem. Appl Sci 10(14).,

  21. 21.

    Calvaresi D, Dubovitskaya A, Calbimonte JP, Taveter K, Schumacher M (2018) Multi-agent systems and blockchain: Results from a systematic literature review. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10978 LNAI(June), 110.

  22. 22.

    Liang C, Shanmugam B, Azam S, Karim A, Islam A, Zamani M, Kavianpour S, Idris NB (2020) Intrusion detection system for the internet of things based on blockchain and multi-agent systems. Electronics 9(7).

  23. 23.

    Haro-Olmo FJ, Alvarez-Bermejo JA, Varela-Vaca AJ, López-Ramos JA (2021) Blockchain-based federation of wireless sensor nodes. J Supercomput.

    Article  Google Scholar 

Download references


This work was supported by Junta de Castilla y León, Spain [grant number LE078G18].

Author information



Corresponding author

Correspondence to José Alberto Benítez-Andrades.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Aguayo-Canela, F.J., Alaiz-Moretón, H., García-Ordás, M.T. et al. Enriched multi-agent middleware for building rule-based distributed security solutions for IoT environments. J Supercomput (2021).

Download citation


  • Rule-based agent
  • Multi-agent systems
  • Intrusion detection system
  • Development environment