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Data access control method of cloud network secure storage under Social Internet of Things environment

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Abstract

The reliability interception of network attack is realized by data access control in network security storage area, and the data security in network is guaranteed. The traditional free link decentralized control model is used to control the data access in the secure storage area of the network. The data access control system of network security storage area includes communication library, protocol base, core control library and resource library. The association rule feature extraction method is used to design the data mining and attack detection algorithm of the network security storage area, which is the core of the data access control system software in the network security storage area. The kernel of the optimal interface in the network security storage area is set up by SDICmdCon register, and the software development of data access control in the network security storage area is realized by the adaptive partition weighted interface scheduling under the embedded Linux environment. This paper considers the novel perspective the data access control method of cloud network secure storage under Social Internet of Things environment. The simulation results show that the model is accurate for data mining and attack detection in the network security storage area, and the security of data access control is improved, which ensures the network security 21% higher than the traditional methods.

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References

  • Abd-Ellah MK, Khalaf AAM, Gharieb RR, Hassanin DA (2021) Automatic diagnosis of common carotid artery disease using different machine learning techniques. J Ambient Intell Humaniz Comput, 1–17

  • Ahmed HI, Elfeshawy NA, Elzoghdy SF, El-Sayed HS, Faragallah OS (2017) A neural network-based learning algorithm for intrusion detection systems. Wirel Pers Commun 97(2):3097–3112

    Article  Google Scholar 

  • Allam V, Madhav BTP, Anilkumar T (2021) A low-profile internet of things-controlled frequency reconfigurable triple band antenna for microwave sensing applications. Analog Integr Circuits Signal Process 109(1):69–77

    Article  Google Scholar 

  • Bi S, Ho CK, Zhang R (2015) Wireless powered communication: opportunities and challenges. IEEE Commun Mag 53(4):117–125

    Article  Google Scholar 

  • Cattivelli FS, Sayed AH (2011) Distributed detection over adaptive networks using diffusion adaptation. IEEE Trans Signal Process 59(5):1917–1932

    Article  MathSciNet  MATH  Google Scholar 

  • Deivasigamani S, Senthilpari C, Yong WH (2021) Machine learning method based detection and diagnosis for epilepsy in EEG signal. J Ambient Intell Humaniz Comput 12(3):4215–4221

    Article  Google Scholar 

  • Evangelio RH, Patzold M, Keller I (2014) Adaptively splitted GMM with feedback improvement for the task of background subtraction. IEEE Trans Inf Forensics Secur 9(5):863–874

    Article  Google Scholar 

  • Fu YL, Li HF, Zhang QH et al (2014) Block-sparse recovery via redundant block OMP. Signal Process 97(7):162–171

    Article  Google Scholar 

  • Ganeshan R, Rodrigues P (2020) Crow-AFL: crow based adaptive fractional lion optimization approach for the intrusion detection. Wirel Pers Commun 111(4):2065–2089

    Article  Google Scholar 

  • Gupta V, Mittal M (2019) QRS complex detection using STFT, chaos analysis, and PCA in standard and real-time ECG databases. J Inst Eng India Ser B 100(5):489–497

    Article  Google Scholar 

  • Gupta V, Mittal M (2020a) A novel method of cardiac arrhythmia detection in electrocardiogram signal. Int J Med Eng Informatics 12(5):489–499

    Article  Google Scholar 

  • Gupta V, Mittal M (2020b) Efficient R-peak detection in electrocardiogram signal based on features extracted using Hilbert transform and Burg method. J Inst Eng India Ser B 101(1):23–34

    Article  Google Scholar 

  • Gupta V, Mittal M, Mittal V (2019) R-peak detection using chaos analysis in standard and real time ECG databases. IRBM 40(6):341–354

    Article  Google Scholar 

  • Gupta V, Mittal M, Mittal V (2020) R-peak detection based chaos analysis of ECG signal. Analog Integr Circ Sig Process 102(3):479–490

    Article  Google Scholar 

  • Hallfors NG, Alhawari M, Abi Jaoude M, Kifle Y, Saleh H, Liao K, Ismail M, Isakovic AF (2018) Graphene oxide: nylon ECG sensors for wearable IoT healthcare—nanomaterial and SoC interface. Analog Integr Circuits Signal Process 96(2):253–260

    Article  Google Scholar 

  • Harifi-Mood M, Bijari A, Alizadeh H, Forouzanfar M, Kandalaft N (2021) Power efficiency enhancement analysis of an inverse class D power amplifier for NB-IoT applications. Analog Integr Circ Sig Process 107(3):551–565

    Article  Google Scholar 

  • Islabudeen M, Kavitha Devi MK (2020) A smart approach for intrusion detection and prevention system in mobile ad hoc networks against security attacks. Wirel Pers Commun 112(1):193–224

    Article  Google Scholar 

  • Khan S, Shah AP, Chouhan SS, Rani S, Gupta N, Pandey JG, Vishvakarma SK (2020) Utilizing manufacturing variations to design a tri-state flip-flop PUF for IoT security applications. Analog Integr Circuits Signal Process 103(3):477–492

    Article  Google Scholar 

  • Khanna A, Rani P, Garg P, Singh PK, Khamparia A (2021) An enhanced crow search inspired feature selection technique for intrusion detection based wireless network system. Wirel Pers Commun, 1–18

  • Kumar N, Agrawal A, Khan RA (2019) Cost estimation of cellularly deployed IoT-enabled network for flood detection. Iran J Comput Sci 2(1):53–64

    Article  Google Scholar 

  • Leclere J, Botteron C, Farine PA (2014) Acquisition of modern GNSS signals using a modified parallel code-phase search architecture. Signal Process 95(5):177–191

    Article  Google Scholar 

  • Li Y, Ghoreishi SM, Issakhov A (2021) Improving the accuracy of network intrusion detection system in medical IoT systems through butterfly optimization algorithm. Wirel Pers Commun, 1–19

  • Mehedi SK, Shamim AAM, Miah MBA (2019) Blockchain-based security management of IoT infrastructure with Ethereum transactions. Iran J Comput Sci 2(3):189–195

    Article  Google Scholar 

  • Mu-dong LI, Hui ZHAO, Xing-wei WENG, Tong HAN (2016) Differential evolution based on optimal Gaussian random walk and individual selection strategies. Control Decis 31(08):1379–1386

    Google Scholar 

  • Nayak R, Kianpoor I, Bahubalindruni PG (2017) Low power ring oscillator for IoT applications. Analog Integr Circuits Signal Process 93(2):257–263

    Article  Google Scholar 

  • Ouechtati H, Nadia BA, Lamjed BS (2021) A fuzzy logic-based model for filtering dishonest recommendations in the Social Internet of Things. J Ambient Intell Humaniz Comput, 1–20.

  • Talbi S, Bouabdallah A (2020) Interest-based trust management scheme for social internet of things. J Ambient Intell Humaniz Comput 11(3):1129–1140

    Article  Google Scholar 

  • Torrieri D (2011) Principles of spread-spectrum communication systems. Springer Science, New York, pp 177–202

    Book  Google Scholar 

  • Wen L (2021) Cloud computing intrusion detection technology based on BP-NN. Wirel Pers Commun, 1–18

  • Wu Q, Zhang YD, Amin MG et al (2014) Complex multitask Bayesian compressive sensing. In: 2014 IEEE international conference on acoustics, speech and signal processing, florence, pp 3375–3379

  • Yanambaka VP, Mohanty SP, Kougianos E (2017) Making use of semiconductor manufacturing process variations: FinFET-based physical unclonable functions for efficient security integration in the IoT. Analog Integr Circ Sig Process 93(3):429–441

    Article  Google Scholar 

  • Zhang Z, Rao BD (2013) Extension of SBL algorithms for the recovery of block sparse signals with Intra-Block correlation. IEEE Trans Signal Process 61(8):2009–2015

    Article  Google Scholar 

  • Zhang Y, Jiang C, Song L et al (2017) Incentive mechanism for mobile crowdsourcing using an optimized tournament model. IEEE J Sel Areas Commun 35(4):880–892

    Article  Google Scholar 

  • Zhang J, Sun J, He H (2021a) Clustering detection method of network intrusion feature based on support vector machine and LCA block algorithm. Wirel Pers Commun, 1–15

  • Zhang T, Han D, Marino MD, Wang L, Li KC (2021b) An evolutionary-based approach for low-complexity intrusion detection in wireless sensor networks. Wirel Pers Commun, 1–24

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This research work is self-funded.

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Authors and Affiliations

  1. State Grid Zhejiang Electric Power Corporation Information & Telecommunication Branch, Hangzhou, 310007, Zhejiang Province, China

    Huifen Wu, Wei Ye & Yaqiong Guo

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  1. Huifen Wu
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  2. Wei Ye
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  3. Yaqiong Guo
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Correspondence to Wei Ye.

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Wu, H., Ye, W. & Guo, Y. Data access control method of cloud network secure storage under Social Internet of Things environment. Int J Syst Assur Eng Manag 14, 1379–1386 (2023). https://doi.org/10.1007/s13198-023-01942-z

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  • DOI: https://doi.org/10.1007/s13198-023-01942-z

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