A Novel Framework for Secure Broadband and MPLS Network Design in Critical Infrastructure Sectors

References

[1] Gupta, V. K. (1983). The locust and grasshopper agricultural manual 1982.

[2] Huang, K. H. J. (2016, July). Remote sensing of locust and grasshopper plague in China: A review. In 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics) (pp. 1-6). IEEE.

[3] Le Gall, M., Overson, R., & Cease, A. (2019). A global review on locusts (Orthoptera: Acrididae) and their interactions with livestock grazing practices. Frontiers in Ecology and Evolution, 263.

[4] Zhang, L., Lecoq, M., Latchininsky, A., & Hunter, D. (2019). Locust and grasshopper management. Annual review of entomology, 64, 15-34.

[5] Kimathi, E., Tonnang, H. E., Subramanian, S., Cressman, K., Abdel-Rahman, E. M., Tesfayohannes, M., ... &Kelemu, S. (2020). Prediction of breeding regions for the desert locust Schistocerca gregaria in East Africa. Scientific Reports, 10(1), 1-10.

[6] Steedman, A. (1990). Locust handbook (No. SB 945. L7. L62 1990).

[7] Cease, A. J., Elser, J. J., Fenichel, E. P., Hadrich, J. C., Harrison, J. F., & Robinson, B. E. (2015). Living with locusts: Connecting soil nitrogen, locust outbreaks, livelihoods, and livestock markets. Bioscience, 65(6), 551-558.

[8] Cullen, D. A., Cease, A. J., Latchininsky, A. V., Ayali, A., Berry, K., Buhl, J., ... & Rogers, S. M. (2017). From molecules to management: mechanisms and consequences of locust phase polyphenism. In Advances in Insect Physiology (Vol. 53, pp. 167-285). Academic Press.

[9] Sergeev, M. G., &Van’kova, I. A. (2008). The dynamics of a local population of the Italian locust (calliptatusitalicus l.) in an anthropogenic landscape. Contemporary Problems of Ecology, 1(1), 88-95.

[10] Sword, G. A., Lecoq, M., & Simpson, S. J. (2010). Phase polyphenism and preventative locust management. Journal of Insect Physiology, 56(8), 949-957.

[11] Xiang, C., Tang, S., Cheke, R. A., & Qin, W. (2016). A locust phase change model with multiple switching states and random perturbation. International Journal of Bifurcation and Chaos, 26(13), 1630037.

[12] Pedgley, D. (1980). Desert locust forecasting manual (Volume 2 of 2). Centre for Overseas Pest Research.

[13] Cressman, K. (2013). Role of remote sensing in desert locust early warning. Journal of Applied Remote Sensing, 7(1), 075098.

[14] Van Huis, A., Cressman, K., &Magor, J. I. (2007). Preventing desert locust plagues: optimizing management interventions. EntomologiaExperimentalis et Applicata, 122(3), 191-214.

[15] Prior, C., &Streett, D. A. (1997). Strategies for the use of entomopathogens in the control of the desert locust and other acridoid pests. The Memoirs of the Entomological Society of Canada, 129(S171), 5-25.

[16] Azhbenov, V. K., Baibussenov, K. S., Sarbaev, A. T., &Harizanova, V. B. (2015, February). Preventive approach of phytosanitary control of locust pests in Kazakhstan and adjacent areas. In Int. Conf. Agric. Ecol. Med. Sci (pp. 33-37).

[17] Latchininsky, A. V. (2013). Locusts and remote sensing: a review. Journal of Applied Remote Sensing, 7(1), 075099.

[18] Sivanpillai, R., Latchininsky, A. V., Driese, K. L., &Kambulin, V. E. (2006). Mapping locust habitats in River Ili Delta, Kazakhstan, using LANDSAT imagery. Agriculture, Ecosystems & Environment, 117(2-3), 128-134.

[19] Ye, S., Zhang, C., Wang, Y., Liu, D., Du, Z., & Zhu, D. (2017). Design and implementation of automatic orthorectification system based on GF-1 big data. Transactions of the Chinese Society of Agricultural Engineering, 33(1), 266-273.

[20] Ye, S., Liu, D., Yao, X., Tang, H., Xiong, Q., Zhuo, W., ... & Song, C. (2018). RDCRMG: A raster dataset clean & reconstitution multi-grid architecture for remote sensing monitoring of vegetation dryness. Remote Sensing, 10(9), 1376.

[21] Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132-7141).

[22] Van Der Werf, W., Woldewahid, G., Van Huis, A., Butrous, M., &Sykora, K. (2005). Plant communities can predict the distribution of solitarious desert locust Schistocerca gregaria. Journal of Applied Ecology, 989-997.

[23] Bryceson, K. P., & Wright, D. E. (1986). An analysis of the 1984 locust plague in Australia using multitemporal Landsat multispectral data and a simulation model of locust development. Agriculture, ecosystems & environment, 16(2), 87-102.

[24] Bryceson, K. P. (1990). Digitally processed satellite data as a tool in detecting potential Australian plague locust outbreak areas. Journal of Environmental Management, 30(3), 191-207.

[25] Waldner, F., Ebbe, M. A. B., Cressman, K., &Defourny, P. (2015). Operational monitoring of the Desert Locust habitat with Earth Observation: An assessment. ISPRS International Journal of Geo-Information, 4(4), 2379-2400.

[26] Gómez, D., Salvador, P., Sanz, J., Casanova, C., Taratiel, D., & Casanova, J. L. (2019). Desert locust detection using Earth observation satellite data in Mauritania. Journal of Arid Environments, 164, 29-37.

[27] Senay, S. D., &Worner, S. P. (2019). Multi-scenario species distribution modeling. Insects, 10(3), 65.

[28] Qing, Y. A. O., CHEN, G. T., Zheng, W. A. N. G., Zhang, C., YANG, B. J., & Jian, T. A. N. G. (2017). Automated detection and identification of white-backed planthoppers in paddy fields using image processing. Journal of Integrative Agriculture, 16(7), 1547-1557.

[29] Martineau, M., Conte, D., Raveaux, R., Arnault, I., Munier, D., &Venturini, G. (2017). A survey on image-based insect classification. Pattern Recognition, 65, 273-284.

[30] Goto, A., Liu, X., Robinson, C. A., & Ridgway, N. D. (2012). Multisite phosphorylation of oxysterol-binding protein regulates sterol binding and activation of sphingomyelin synthesis. Molecular biology of the cell, 23(18), 3624-3635.

[31] Hirafuji, M., Yoichi, H., Watanabe, T., Asai, M., Hu, H. M., Tanaka, K., ... & Ninomiya, S. (2008). Real-time insect monitoring system by using Field Server. In World conference on agricultural information and IT, IAALD AFITA WCCA (pp. 277-282).

[32] Long, Y., Gao, T., Liu, S., Zhang, Y., Li, X., Zhou, L., ... & Yang, Y. (2022). Analysis of the Humoral Immunal Response Transcriptome of Ectropisobliqua Infected by Beauveria bassiana. Insects, 13(3), 225.

[33] Cai, Q. (2010). Research of vegetable leaf-eating pests based on image analysis. Northwest A & F University.

[34] Zhang, F. Q. (2003). Machine Vision and Wavelet Analysis-Based Farmland Pest Identification System.

[35] Zheng, Y., Wu, G., Wang, Y., & Mao, W. (2010). Locust images detection based on fuzzy pattern recognition. Transactions of the Chinese Society of Agricultural Engineering, 26(1), 21-25.

[36] Xiong, X., Wang, Y., & Zhang, X. (2007). Detection of locusts using Near-Infrared spectroscopy and cluster analysis. In Proceedings 35th International Symposium'Actual tasks on agricultural engineering', Opatija, Croatia, 19-23 veljače, 2007 (pp. 199-204). Zavod za MehanizacijuPoljprivrede, AgronomskiFakultetSveučilišta u Zagrebu.

[37] Wang, B. (2022). Identification of Crop Diseases and Insect Pests Based on Deep Learning. Scientific Programming, 2022.

[38] Thenmozhi, K., & Reddy, U. S. (2019). Crop pest classification based on deep convolutional neural network and transfer learning. Computers and Electronics in Agriculture, 164, 104906.

[39] Hunter, D. M. (1989). The response of Mitchell grasses (Astrebla spp.) and Button grass (Dactylocteniumradulans (R. Br.)) to rainfall and their importance to the survival of the Australian plague locust, Chortoicetes terminifera (Walker), in the arid zone. Australian Journal of Ecology, 14(4), 467-471.

[40] Hunter, D. M. (1996). Rapport entre les pullulations du Criquetaustralien, Chortoicetes terminifera (Walker)(Orthoptera: Acridiadae) et la pluviométrie dans l’intérieuraride de l’Australie. Science et changementsplanétaires/Sécheresse, 7(2), 87-90.

[41] Wright, D. E. (1983). The Origin and Development of Major Plagues of" Chortoicetes Terminifera"(Walker) (Doctoral dissertation).

[42] Wardhaugh, K. G. (1973). A study of some factors affecting egg development in Chortoicetes terminifera, Walker (Orthoptera: Acrididae). The Australian National University (Australia).

[43] Gregg, P. C. (1981). Aspects of the adaptation of a locust to its physical environment. The Australian National University (Australia).

[44] Casimir, M. (1974). outbreak of the Australian plague locust, Chortoicetes terminifera (Walk.), during 1966-67 and the influence of weather on swarm flight.

[45] Nguyen, N. T., & Watt, J. W. (1980). The distribution of ultra-low volume sprays from a light aircraft equipped with rotary atomizers. Australian Journal of Experimental Agriculture, 20(105), 492-496.

[46] Nguyen, N. T., & Watt, J. W. (1981). The distribution and recovery of aerial ultra‐low volume sprays for controlling nymphs of the Australian plague locust, Chortoicetes terminifera Walker. Australian Journal of Entomology, 20(4), 269-275.

[47] Johari, S. N. A. M., Khairunniza-Bejo, S., Shariff, A. R. M., Husin, N. A., Basri, M. M. M., &Kamarudin, N. (2022). Identification of bagworm (Metisa plana) instar stages using hyperspectral imaging and machine learning techniques. Computers and Electronics in Agriculture, 194, 106739.

[48] Chandel, R. S., Chandla, V. K., Verma, K. S., &Pathania, M. (2022). Insect pests of potato in India: biology and management. In Insect pests of potato (pp. 371-400). Academic Press.

[49] Melvani, K., Stacey, N., Bristow, M., Crase, B., & Moles, J. (2022). Farmers’ values for land, trees and biodiversity underlie agricultural sustainability. Land Use Policy, 105688.

[50] Rosenberg, L. J. (2000, June). 17 Information Systems for Locust Forecasting. In Workshop on Research Priorities for Migrant Pests of Agriculture in Southern Africa (Vol. 3, No. 37, p. 165).

[51] Healey, R. G., Robertson, S. G., MAGOR, J., Pender, J., &Cressman, K. (1996). A GIS for desert locust forecasting and monitoring. International Journal of Geographical Information Science, 10(1), 117-136.

[52] Burt, P. J. A., Colvin, J., & Smith, S. M. (1995). Remote sensing of rainfall by satellite as an aid to Oedaleus senegalensis (Orthoptera: Acrididae) control in the Sahel. Bulletin of Entomological Research, 85(4), 455-462.

[53] Burt, P. J. A., Colvin, J., & Smith, S. M. (1997). Forecasting the early-season eclosion of Oedaleus senegalensis in the Sahel: the role of remotely sensed rainfall data. In New Strategies in Locust Control (pp. 55-61). Birkhäuser Basel.

[54] Todd, M. C., Washington, R., Cheke, R. A., &Kniveton, D. (2002). Brown locust outbreaks and climate variability in southern Africa. Journal of Applied Ecology, 39(1), 31-42.

[55] Jianwen, M., & Qin, D. (2005, July). Migratory locust hazard monitoring and prediction using the Bayesian network inference. In Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS'05. (Vol. 5, pp. 3623-3626). Ieee.

[56] Ceccato, P., Cressman, K., Giannini, A., &Trzaska, S. (2007). The desert locust upsurge in West Africa (2003–2005): Information on the desert locust early warning system and the prospects for seasonal climate forecasting. International Journal of Pest Management, 53(1), 7-13.

[57] Vallebona, C., Genesio, L., Crisci, A., Pasqui, M., Di Vecchia, A., &Maracchi, G. (2008). Large-scale climatic patterns forcing desert locust upsurges in West Africa. Climate Research, 37(1), 35-41.

[58] Piou, C., Lebourgeois, V., Benahi, A. S., Bonnal, V., elHacenJaavar, M., Lecoq, M., & Vassal, J. M. (2013). Coupling historical prospection data and a remotely-sensed vegetation index for the preventative control of Desert locusts. Basic and Applied Ecology, 14(7), 593-604.

[59] Tronin, A. A., Gornyy, V. I., Kiselev, A. V., Kritsuk, S. G., &Latypov, I. S. (2014). Forecasting of locust mass breeding by using satellite data. Curr. Probl. Remote Sens. Earth Space, 11, 37-50.

[60] Veran, S., Simpson, S. J., Sword, G. A., Deveson, E., Piry, S., Hines, J. E., & Berthier, K. (2015). Modeling spatiotemporal dynamics of outbreaking species: influence of environment and migration in a locust. Ecology, 96(3), 737-748.

[61] Zheng, S., Zheng, J., Mu, C., Ni, Y., Dawuti, B., & Wu, J. (2015, June). GIS-based multi-criteria analysis model for identifying probable sites of locust outbreak in Xinjiang, China. In 2015 23rd International Conference on Geoinformatics (pp. 1-6). IEEE.

[62] Mangeon, S., Spessa, A., Deveson, E., Darnell, R., &Kriticos, D. J. (2020). Daily mapping of Australian Plague Locust abundance. Scientific reports, 10(1), 1-11.

[63] Crooks, W. T., &Cheke, R. A. (2014). Soil moisture assessments for brown locust Locustanapardalina breeding potential using synthetic aperture radar. Journal of Applied Remote Sensing, 8(1), 084898.

[64] Meynard, C. N., Gay, P. E., Lecoq, M., Foucart, A., Piou, C., &Chapuis, M. P. (2017). Climate‐driven geographic distribution of the desert locust during recession periods: Subspecies’ niche differentiation and relative risks under scenarios of climate change. Global Change Biology, 23(11), 4739-4749.

[65] Piou, C., Gay, P. E., Benahi, A. S., BabahEbbe, M. A. O., Chihrane, J., Ghaout, S., ... &Escorihuela, M. J. (2019). Soil moisture from remote sensing to forecast desert locust presence. Journal of Applied Ecology, 56(4), 966-975.

[66] Ma, J., Hasibagan, H. X., &Devision, T. (2002, June). Calibration and verification of remote sensing data for East Asia migratory plague locust reed habitat monitoring. In IEEE International Geoscience and Remote Sensing Symposium (Vol. 5, pp. 2868-2870). Ieee.

[67] Weiss, J. E. R. (2016). Do Locusts Seek Greener Pastures?: An Evaluation of MODIS Vegetation Indices to Predict Presence, Abundance and Impact of the Australian Plauge Locust in South-eastern Australia (Doctoral dissertation, University of Melbourne, School of BioSciences and Department of Infrastructre and Engineering).

[68] Hunter, D. M., McCulloch, L., &Spurgin, P. A. (2008). Aerial detection of nymphal bands of the Australian plague locust (Chortoicetes terminifera (Walker))(Orthoptera: Acrididae). Crop Protection, 27(1), 118-123.

[69] Cracknell, A. P. (1991). Rapid remote recognition of habitat changes. Preventive Veterinary Medicine, 11(3-4), 315-323.

[70] Crooks, W. T., & Archer, D. J. (2002, June). SAR observations of dryland moisture-towards monitoring outbreak areas of the brown locust in South Africa. In IEEE International Geoscience and Remote Sensing Symposium (Vol. 4, pp. 1994-1996). IEEE.

@article{1703976,
      author = {Afees Olanrewaju Akinade, Peter Adeyemo Adepoju, Adebimpe Bolatito Ige, Adeoye Idowu Afolabi},
      title = {A Novel Framework for Secure Broadband and MPLS Network Design in Critical Infrastructure Sectors},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {7},
      pages = {430-449},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703976.pdf},
      abstract = {In critical infrastructure sectors, secure and efficient network design is paramount to ensure operational resilience, safeguard sensitive data, and mitigate cyber threats. This paper proposes a novel framework for designing secure broadband and Multi-Protocol Label Switching (MPLS) networks tailored to the unique demands of critical infrastructure. The framework emphasizes a layered security approach, combining advanced encryption protocols, intelligent traffic segmentation, and adaptive routing mechanisms to enhance network robustness. Leveraging cutting-edge technologies such as software-defined networking (SDN) and artificial intelligence (AI), the framework offers real-time threat detection, anomaly detection, and dynamic response capabilities, thereby minimizing vulnerabilities. A key feature of the proposed design is its scalability and flexibility, which enables seamless integration with existing network infrastructures while accommodating future technological advancements. The use of SDN simplifies network management and allows for centralized control, ensuring optimal performance and rapid adaptability to changing network conditions. Furthermore, the integration of AI-driven analytics enhances situational awareness by providing predictive insights into network performance and security risks. The framework also addresses compliance with industry standards and regulations, ensuring alignment with best practices for cybersecurity and data protection in critical infrastructure sectors. A case study is presented to validate the framework's effectiveness in a real-world setting, focusing on its application in the energy sector to secure data exchanges between distributed control systems and remote monitoring units. Results indicate significant improvements in network reliability, reduced latency, and strengthened security postures. This research provides a comprehensive roadmap for deploying secure broadband and MPLS networks, catering to the needs of critical infrastructure operators. It contributes to the broader discourse on resilient network design, offering practical insights for stakeholders aiming to bolster the security and efficiency of their communication networks.},
      keywords = {Secure Broadband, MPLS, Critical Infrastructure, Network Design, Cybersecurity, Software-Defined Networking (SDN), Artificial Intelligence (AI), Threat Detection, Adaptive Routing, Compliance},
      month = {January},
  }