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IPSHO-Fed: a hybrid federated learning and spotted hyena optimization approach for trust assessment

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Abstract

In the era of rapidly evolving intelligent transportation systems, the vehicle–road–cloud collaborative system emerges as a groundbreaking paradigm that integrates vehicles, road infrastructure, and cloud servers to optimize transportation-related tasks. However, the system’s success hinges on the reliable and secure exchange of data and model training among entities. Trust evaluation becomes paramount to ensure the credibility of information transmitted within the system and safeguard against potential security threats. To overcome this issue, an innovative trust evaluation scheme using the improved PSO-based spotted hyena optimization (IPSHO) algorithm is proposed. In this approach, each entity in the collaborative system is represented as a spotted hyena, and the algorithm’s integration with federated learning enables decentralized model training without sharing raw data. By leveraging the collective intelligence of the system, IPSHO collaboratively refines trust values and ensures personalized trust evaluation at the equipment, data, and model levels. Our simulation environment, implemented in MATLAB, models a realistic network of 100 sensor nodes representing vehicles, road infrastructure, and cloud servers. The entities communicate within a 100 m × 100 m area using secure channels. Through rigorous experimentation, the IPSHO algorithm's convergence behavior, its ability to find optimal trust values, and its performance against malicious nodes and hazardous events are assessed. The results demonstrate IPSHO's effectiveness in achieving accurate trust evaluations in diverse scenarios. Comparison with other optimization algorithms and trust evaluation methods reaffirms IPSHO’s superiority in enhancing trustworthiness assessment. The proposed scheme empowers legitimate nodes with the capability to discern trustworthiness and authenticity of information exchanged, ensuring the integrity and reliability of the vehicle–road–cloud collaborative system.

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Availability of data and material

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Hazra A, Adhikari M, Nandy S, Doulani K, Menon VG (2022) Federated-learning-aided next-generation edge networks for intelligent services. IEEE Netw 36(3):56–64

    Article  Google Scholar 

  2. Khan AA, Khan MM, Khan KM, Arshad J, Ahmad F (2021) A blockchain-based decentralized machine learning framework for collaborative intrusion detection within UAVs. Comput Netw 196:108217

    Article  Google Scholar 

  3. Qiao F, Wu J, Li J, Bashir AK, Mumtaz S, Tariq U (2020) Trustworthy edge storage orchestration in intelligent transportation systems using reinforcement learning. IEEE Trans Intell Transp Syst 22(7):4443–4456

    Article  Google Scholar 

  4. Manias DM, Shami A (2021) Making a case for federated learning in the internet of vehicles and intelligent transportation systems. IEEE Netw 35(3):88–94

    Article  Google Scholar 

  5. Antunes RS, André da Costa C, Küderle A, Yari IA, Eskofier B (2022) Federated learning for healthcare: systematic review and architecture proposal. ACM Trans Intell Syst Technol (TIST) 13(4):1–23

    Article  Google Scholar 

  6. Arulprakash M, Jebakumar R (2021) People-centric collective intelligence: decentralized and enhanced privacy mobile crowd sensing based on blockchain. J Supercomput 1–27

  7. Olugbade S, Ojo S, Imoize AL, Isabona J, Alaba MO (2022) A review of artificial intelligence and machine learning for incident detectors in road transport systems. Math Comput Appl 27(5):77

    Google Scholar 

  8. Barclay I, Preece A, Taylor I, Radha SK, Nabrzyski J (2022) Providing assurance and scrutability on shared data and machine learning models with verifiable credentials. Concurr Comput Pract Exp 35(18):e6997

    Article  Google Scholar 

  9. Kang J, Xiong Z, Niyato D, Zou Y, Zhang Y, Guizani M (2020) Reliable federated learning for mobile networks. IEEE Wirel Commun 27(2):72–80

    Article  Google Scholar 

  10. Salim S, Turnbull B, Moustafa N (2021) A blockchain-enabled explainable federated learning for securing internet-of-things-based social media 3.0 networks. IEEE Transactions on Computational Social Systems

  11. Yıldız BS (2020) The spotted hyena optimization algorithm for weight-reduction of automobile brake components. Mater Testing 62(4):383–388

    Article  ADS  Google Scholar 

  12. Luo Q, Li J, Zhou Y, Liao L (2021) Using spotted hyena optimizer for training feedforward neural networks. Cogn Syst Res 65:1–16

    Article  Google Scholar 

  13. Sabahno M, Safara F (2022) ISHO: improved spotted hyena optimization algorithm for phishing website detection. Multimed Tools Appl 81(24):34677–34696

    Article  Google Scholar 

  14. Jia H, Li J, Song W, Peng X, Lang C, Li Y (2019) Spotted hyena optimization algorithm with simulated annealing for feature selection. IEEE Access 7:71943–71962

    Article  Google Scholar 

  15. Dhiman G (2017) Kaur A (2019) A hybrid algorithm based on particle swarm and spotted hyena optimizer for global optimization. Soft Comput Prob Solv SocProS 1:599–615

    Google Scholar 

  16. Wang D, Yi Y, Yan S, Wan N, Zhao J (2023) A node trust evaluation method of vehicle–road–cloud collaborative system based on federated learning. Ad Hoc Netw 138:103013

    Article  Google Scholar 

  17. Hbaieb A, Ayed S, Chaari, L (2022) Federated learning based IDS approach for the IoV. In: Proceedings of the 17th international conference on availability, reliability and security, pp 1–6

  18. Chen X, Wang L (2017) A cloud-based trust management framework for vehicular social networks. IEEE Access 5:2967–2980

    Article  Google Scholar 

  19. Aladwan MN, Awaysheh FM, Alawadi S, Alazab M, Pena TF, Cabaleiro JC (2020) TrustE-VC: trustworthy evaluation framework for industrial connected vehicles in the cloud. IEEE Trans Ind Inf 16(9):6203–6213

    Article  Google Scholar 

  20. Gupta M, Awaysheh FM, Benson J, Alazab M, Patwa F, Sandhu R (2020) An attribute-based access control for cloud enabled industrial smart vehicles. IEEE Trans Ind Inf 17(6):4288–4297

    Article  Google Scholar 

  21. Ahmad F, Kurugollu F, Adnane A, Hussain R, Hussain F (2020) MARINE: Man-in-the-middle attack resistant trust model in connected vehicles. IEEE Internet Things J 7(4):3310–3322

    Article  Google Scholar 

  22. Vatankhah Barenji R (2022) A blockchain technology based trust system for cloud manufacturing. J Intell Manuf 33(5):1451–1465

    Article  PubMed  Google Scholar 

  23. Rjoub G, Bentahar J, Joarder YA (2022) Active federated yolor model for enhancing autonomous vehicles safety. In: International conference on mobile web and intelligent information systems 49–64

  24. Gnanajeyaraman R, Arul U, Michael G, Selvakumar A, Ramesh S, Manikandan T (2023) VANET security enhancement in cloud navigation with Internet of Things-based trust model in deep learning architecture. Soft Computing 1–12

  25. Rjoub G, Wahab OA, Bentahar J, Bataineh A (2022) Trust-driven reinforcement selection strategy for federated learning on IoT devices. Computing 1–23

  26. Hao M, Ye D, Wang S, Tan B, Yu R (2021) URLLC resource slicing and scheduling for trustworthy 6G vehicular services: a federated reinforcement learning approach. Phys Commun 49:101470

    Article  Google Scholar 

  27. Xu S, Guo C, Hu RQ, Qian Y (2021) Blockchain-inspired secure computation offloading in a vehicular cloud network. IEEE Internet Things J 9(16):14723–14740

    Article  Google Scholar 

  28. Lu FQ, Huang M, Ching WK, Siu TK (2013) Credit portfolio management using two-level particle swarm optimization. Inf Sci 237:162–175

    Article  MathSciNet  Google Scholar 

  29. Gad AG (2022) Particle swarm optimization algorithm and its applications: a systematic review. Arch Comput Methods Eng 29(5):2531–2561

    Article  MathSciNet  Google Scholar 

  30. Dhiman G, Kumar V (2019) Spotted hyena optimizer for solving complex and non-linear constrained engineering problems. In: Harmony search and nature inspired optimization algorithms: theory and applications, ICHSA 2018, pp 857-867

  31. Zhou X, Ji F, Wang L, Ma Y, Fujita H (2020) Particle swarm optimization for trust relationship based social network group decision making under a probabilistic linguistic environment. Knowl-Based Syst 200:105999

    Article  Google Scholar 

  32. Bi H, Lu F, Duan S, Huang M, Zhu J, Liu M (2020) Two-level principal–agent model for schedule risk control of IT outsourcing project based on genetic algorithm. Eng Appl Artif Intell 91:103584

    Article  Google Scholar 

  33. Gupta P, Bhatia KK, Duhan N (2022) An Assessment on Real Time Bidding Strategies for Advertising Markets. In: 2022 fifth international conference on computational intelligence and communication technologies (CCICT), pp 138–145

  34. Guo L, Zhu Y, Yang H, Luo Y, Sun L, Zheng X (2022) A k-nearest neighbor query method based on trust and location privacy protection. Concurr Comput Pract Exp 34(16):e5766

    Article  Google Scholar 

  35. Tian Y, Mirzabagheri M, Bamakan SMH, Wang H, Qu Q (2018) Ramp loss one-class support vector machine; a robust and effective approach to anomaly detection problems. Neurocomputing 310:223–235

    Article  Google Scholar 

  36. Alsarhan A, Al-Ghuwairi AR, Almalkawi IT, Alauthman M, Al-Dubai A (2021) Machine learning-driven optimization for intrusion detection in smart vehicular networks. Wirel Pers Commun 117:3129–3152

    Article  Google Scholar 

  37. Zheng Q, Liu G, Liu A, Li Z, Zheng K, Zhao L, Zhou X (2021) Implicit relation-aware social recommendation with variational auto-encoder. World Wide Web 24(5):1395–1410

    Article  Google Scholar 

  38. Snineh SM, Amrani NEA, Youssfi M, Bouattane O, Daaif A (2021) Detection of traffic anomaly in highways by using recurrent neural network. In: 2021 fifth international conference on intelligent computing in data sciences (ICDS), pp 1–6

  39. Arivudainambi D, KA VK, Visu P, (2019) Malware traffic classification using principal component analysis and artificial neural network for extreme surveillance. Comput Commun 147:50–57

    Article  Google Scholar 

  40. Singh K, Verma AK (2018) A trust model for effective cooperation in flying ad hoc networks using genetic algorithm. In: 2018 international conference on communication and signal processing (ICCSP), pp 0491–0495

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Authors and Affiliations

  1. Information Technology, Government College of Technology, Coimbatore, Tamil Nadu, India

    R. Devi

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All authors agreed on the content of the study. RD collected all the data for analysis. RD agreed on the methodology. RD completed the analysis based on agreed steps. Results and conclusions are discussed and written together. The author read and approved the final manuscript.

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Devi, R. IPSHO-Fed: a hybrid federated learning and spotted hyena optimization approach for trust assessment. Neural Comput & Applic 36, 5571–5594 (2024). https://doi.org/10.1007/s00521-023-09330-1

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