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Intelligent IoT security monitoring based on fuzzy optimum-path forest classifier


Detection of intrusions in Internet of Things networks is essential to maintain the availability and integrity of the data generated and transmitted by connected devices. Such a procedure is paramount when the data originate from critical activities, such as military, financial, industrial, and health sectors. In the last decades, machine learning (ML)-based approaches have become one of the most suitable and adopted procedures for the task, providing automatic, fast, and accurate results. Despite such success, the literature still presents a gap regarding valid applications of intrusion detection in the IoT environments, which usually stands for a challenging task composed of different types of attacks. In this context, this work applies a recent technique based on graphs and logic fuzzy, namely Fuzzy Optimum-Path Forest (Fuzzy OPF), to detect threats that escape an IoT network’s regular traffic. We evaluate our model against five well-known ML algorithms, i.e., Linear Discriminant Analysis, Support Vector Machine, Naive Bayes, K-Nearest Neighbors, and the standard Optimum-Path Forest. Experimental results show that Fuzzy OPF outperforms the baselines considering accuracy, recall, and F1 metrics. As a result, the Fuzzy OPF proposal for intrusion detection had a hit rate of 98 and 99%.

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Data Availability Statement

Enquiries about data availability should be directed to the authors.


  1. Notice \(\mathcal{T}^*\) is obtained after calculating the selection of nearby samples that have different labels and after performing the Minimum Spanning Tree.




  • Alalade ED (2020) Intrusion detection system in smart home network using artificial immune system and extreme learning machine hybrid approach. In: 2020 IEEE 6th world forum on internet of things (WF-IoT), pp 1–2

  • Al-Garadi MA, Mohamed A, Al-Ali AK, Du X, Ali I, Guizani M (2020) A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Commun Surv Tutor 22(3):1646–1685

    Article  Google Scholar 

  • Almohri HM, Watson LT, Evans D (2020) An attack-resilient architecture for the internet of things. IEEE Trans Inf Forens Secur 15:3940–3954

    Google Scholar 

  • Alkadi O, Moustafa N, Turnbull B, Choo KKR (2020) A deep blockchain framework-enabled collaborative intrusion detection for protecting iot and cloud networks. IEEE Internet Things J 8(12):9463–9472

  • Arshad J, Azad MA, Abdeltaif MM, Salah K (2020) An intrusion detection framework for energy constrained IoT devices. Mech Syst Signal Process 136:106436

    Article  Google Scholar 

  • Butun I, Österberg P, Song H (2019) Security of the internet of things: Vulnerabilities, attacks, and countermeasures. IEEE Commun Surv Tutor 22(1):616–644

    Article  Google Scholar 

  • Cheema MA, Khaliq Qureshi H, Chrysostomou C, Lestas M (2020) Utilizing blockchain for distributed machine learning based intrusion detection in internet of things. In: 2020 16th international conference on distributed computing in sensor systems (DCOSS), pp 429–435

  • Chkirbene Z, Eltanbouly S, Bashendy M, AlNaimi N, Erbad A(2020) Hybrid machine learning for network anomaly intrusion detection. in 2020 IEEE international conference on informatics, IoT, and enabling technologies (ICIoT), pp 163–170

  • Cristiani AL, Lieira DD, Meneguette RI, Camargo HA (2020) A fuzzy intrusion detection system for identifying cyber-attacks on iot networks. In: 2020 IEEE Latin-American conference on communications (LATINCOM). IEEE, pp 1–6

  • da Costa KA, Papa JP, Lisboa CO, Munoz R, de Albuquerque VHC (2019) Internet of things: a survey on machine learning-based intrusion detection approaches. Comput Netw 151:147–157

    Article  Google Scholar 

  • de Souza RWR, Silva DS, Passos LA, Roder M, Santana MC, Pinheiro PR, de Albuquerque VHC (2021) Computer-assisted Parkinson’s disease diagnosis using fuzzy optimum-path forest and restricted Boltzmann machines. Comput Biol Med 131:104260

  • Guimaraes RR, Passos LA, Filho RH, Albuquerque VHCd, Rodrigues JJPC, Komarov MM, Papa JP (2019) Intelligent network security monitoring based on optimum-path forest clustering. IEEE Netw 33(2):126–131

    Article  Google Scholar 

  • Ghosh N, Maity K, Paul R, Maity S (2019) Outlier detection in sensor data using machine learning techniques for iot framework and wireless sensor networks: a brief study. In: International conference on applied machine learning (ICAML), pp 187–190

  • Ghazi AE, Moulay Rachid A (2020) Machine learning and datamining methods for hybrid iot intrusion detection. In: 2020 5th international conference on cloud computing and artificial intelligence: technologies and applications (CloudTech), pp. 1–6

  • Hassan MM, Hassan MR, Huda S, de Albuquerque VHC (2021) A robust deep-learning-enabled trust-boundary protection for adversarial industrial IoT environment. IEEE Internet Things J 8(12):9611–9621

    Article  Google Scholar 

  • Huang X, Xie C, Fang X, Zhang L (2015) Combining pixel- and object-based machine learning for identification of water-body types from urban high-resolution remote-sensing imagery. IEEE J Sel Top Appl Earth Obs Remote Sens 8(5):2097–2110

    Article  Google Scholar 

  • Jodas DS, Roder M, Pires R, Santana MCS, de Souza Jr LA, Passos LA (2022) Detecting atherosclerotic plaque calcifications of the carotid artery through optimum-path forest. In: Optimum-path forest. Elsevier, pp 137–154

  • Liu Z, Thapa N, Shaver A, Roy K, Yuan X, Khorsandroo S (2020) Anomaly detection on iot network intrusion using machine learning. In: 2020 international conference on artificial intelligence. Big Data, computing and data communication systems (icABCD), pp 1–5

  • Lin C-F, Wang S-D (2002) Fuzzy support vector machines. IEEE Trans Neural Netw 13(2):464–471

    Article  Google Scholar 

  • Lv Z, Qiao L, Li J, Song H (2020) Deep learning enabled security issues in the internet of things. IEEE Internet Things J 8(12):9531–9538

  • Manimurugan S, Majdi A-Q, Mohmmed M, Narmatha C, Varatharajan R (2020) Intrusion detection in networks using crow search optimization algorithm with adaptive neuro-fuzzy inference system. Microprocess Microsyst 79:103261

    Article  Google Scholar 

  • Magaia N, Fonseca R, Muhammad K, Segundo AHFN, Lira Neto AV, de Albuquerque VHC (2021) Industrial internet-of-things security enhanced with deep learning approaches for smart cities. IEEE Internet Things J 8(8):6393–6405

    Article  Google Scholar 

  • Maniriho P, Niyigaba E, Bizimana Z, Twiringiyimana V, Mahoro LJ, Ahmad T (2020) Anomaly-based intrusion detection approach for iot networks using machine learning. In: 2020 international conference on computer engineering, network, and intelligent multimedia (CENIM), pp 303–308

  • Moreira TP, Santana MCS, Passos LA, Papa JP, da Costa KAP (2022) An end-to-end approach for seam carving detection using deep neural networks. In: Iberian conference on pattern recognition and image analysis. Springer, pp 447–457

  • Naik N, Diao R, Shen Q (2017) Dynamic fuzzy rule interpolation and its application to intrusion detection. IEEE Trans Fuzzy Syst 26(4):1878–1892

    Article  Google Scholar 

  • Nugroho EP, Djatna T, Sitanggang IS, Buono A, Hermadi I(2020) A review of intrusion detection system in iot with machine learning approach: current and future research. In: 2020 6th international conference on science in information technology (ICSITech), pp 138–143

  • Papa JP, Falcao AX, Suzuki CT (2009) Supervised pattern classification based on optimum-path forest. Int J Imaging Syst Technol 19(2):120–131

    Article  Google Scholar 

  • Passos LA, Ramos CCO, Rodrigues D, Pereira DR, de Souza AN, da Costa KAP, Papa JP (2016) Unsupervised non-technical losses identification through optimum-path forest. Electr Power Syst Res 140:413–423

    Article  Google Scholar 

  • Passos LA, Jodas DS, Ribeiro LCF, Moreira T, Papa JP (2020) O\(^2\)PF: oversampling via optimum-path forest for breast cancer detection, In: IEEE 33rd international symposium on computer-based medical systems (CBMS). IEEE, pp 498–503

  • Passos LA, Jodas DS, Ribeiro LC, Akio M, de Souza AN, Papa JP (2022) Handling imbalanced datasets through optimum-path forest. Knowl-Based Syst 242:108445

    Article  Google Scholar 

  • Passos LA, Jodas D, da Costa KA, Júnior LAS, Colombo D, Papa JP(2022)A review of deep learning-based approaches for deepfake content detection. arXiv preprint arXiv:2202.06095

  • Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  • Ravi N, Shalinie SM (2020) Semisupervised-learning-based security to detect and mitigate intrusions in IoT network. IEEE Internet Things J 7(11):11041–11052

    Article  Google Scholar 

  • Ribeiro PB, Passos LA, Da Silva LA, da Costa KA, Papa JP, Romero RA (2015) Unsupervised breast masses classification through optimum-path forest, In: IEEE 28th international symposium on computer-based medical systems. IEEE, pp 238–243

  • Rocha LM, Cappabianco FAM, Falcão AX (2009) Data clustering as an optimum-path forest problem with applications in image analysis, Int J Imaging Syst Technol 19(2):50–68 (Online).

  • Saranya T, Sridevi S, Deisy C, Chung TD, Khan M (2020) Performance analysis of machine learning algorithms in intrusion detection system: a review. Procedia Comput Sci 171, 1251–1260. Third International Conference on Computing and Network Communications (CoCoNet’19)

  • Sarkar S, Chatterjee S, Misra S (2015) Assessment of the suitability of fog computing in the context of internet of things. IEEE Trans Cloud Comput 6(1):46–59

    Article  Google Scholar 

  • Souza RWR, De Oliveira JVC, Passos LA, Ding W, Papa JP, Albuquerque V (2019) A novel approach for optimum-path forest classification using fuzzy logic. IEEE Trans Fuzzy Syst 28(12):1

    Google Scholar 

  • Santos DF, Pires RG, Passos LA, Papa JP (2021) DDIPNet and DDIPNet+: discriminant deep image prior networks for remote sensing image classification, In: IEEE international geoscience and remote sensing symposium IGARSS. IEEE, pp 2843–2846

  • Shaver A, Liu Z, Thapa N, Roy K, Gokaraju B, Yuan X (2020) Anomaly based intrusion detection for iot with machine learning. In: IEEE applied imagery pattern recognition workshop (AIPR), pp 1–6

  • Swarna Sugi SS, Ratna SR (2020) Investigation of machine learning techniques in intrusion detection system for iot network. In: 2020 3rd international conference on intelligent sustainable systems (ICISS), pp 1164–1167

  • Tian Z, Luo C, Qiu J, Du X, Guizani M (2019) A distributed deep learning system for web attack detection on edge devices. IEEE Trans Ind Inf 16(3):1963–1971

    Article  Google Scholar 

  • Vikram A, Mohana (2020) Anomaly detection in network traffic using unsupervised machine learning approach. In: 2020 5th international conference on communication and electronics systems (ICCES), pp 476–479

  • Yao J, Chen H, Xu Z, Huang J, Li J, Jia J, Wu H (2020) Development of a wearable electrical impedance tomographic sensor for gesture recognition with machine learning. IEEE J Biomed Health Inf 24(6):1550–1556

    Article  Google Scholar 

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This study was financed in part by the Science and Technology Planning Project of Guangdong Province (Grant No. 2018A050506086), by Research Start-up Funds of DGUT (GC300502-60), by the KEY Laboratory of Robotics and Intelligent Equipment of Guangdong Regular Institutions of Higher Education (Grant No. 2017KSYS009).

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Correspondence to Yongzhao Xu or Victor Hugo C. de Albuquerque.

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Communicated by Deepak kumar Jain.

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Xu, Y., de Souza, R.W.R., Medeiros, E.P. et al. Intelligent IoT security monitoring based on fuzzy optimum-path forest classifier. Soft Comput 27, 4279–4288 (2023).

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  • Intrusion detection
  • Fuzzy optimum-path forest
  • IoT
  • Machine learning