Author Details

Climate services involve the timely production, translation, and delivery of useful climate data, information and knowledge for societal decision making. In order to create climate services for farmers that are truly integrated with user-centric design into the development process in an African context, the study has finished an important and crucial step by conducting a literature review and designing a prototype for the application. The goal of this study was to create climate services for farmers in an African context that are genuinely integrated with user-centric design. This led to the co-design and development and integration of a mobile application that provide climate and weather information as well as agricultural information for the main crops such millet, maize and sorghum. The research applied using qualitative research using interview with 3 farmers in the field using random sampling with the approach to inform the study. A survey has been administered to find out how people understand climate services, Agro meteorology and help enhance the mobile application’s user experience. A Results shows that farmers are determined and ready to use and excited with the application. These innovations helped farmers to reduce the cost, increase crop capacity and profit. A hypothesis was set that there is a need for integrating AI into a farmer’s application for making farming process more progressive and efficient farming and the integration of Market Place (MP) for farmer’s application to market and sell their product the integration of notification system that allows farmers to receive real-time data and IOT for real-time data. The data collected and the survey results demonstrated that the research objectives were being met. The study aims to develop the application that would be scalable, durable and fault tolerant for farmers to use the application successfully.

Keywords

Climate Services for Farmers, Co-Design, Mobile Application, Artificial Intelligence, Internet of Things.

Full Text

References

Krell, N. T., Giroux, S. A., Guido, Z., Hannah, C., Lopus, S. E., Caylor, K. K., & Evans, T. P. (2021). Smallholder farmers' use of mobile phone services in central Kenya. Climate and Development, 13(3), 215-227.

Anjum, R. (2015). Design of mobile phone services to support farmers in developing countries.

Chattopadhyay, N., Ghosh, K., & Chandras, S. V. (2016). Agrometeorological advisory to assist the farmers in meeting the challenges of extreme weather events. Mausam, 67(1), 277-288.

Nyareza, S., & Dick, A. L. (2012, September). Use of community radio to communicate agricultural information to Zimbabwe's peasant farmers. In Aslib Proceedings. Emerald Group Publishing Limited.

Halleen, F., & Holz, G. (2001). An Overview of the Biology, Epidemiology and Control of Uncinula necator (Powdery Mildew) on Grapevine, with Ref ere nee to South Africa. South african journal of Enology and Viticulture, 22(2), 111-121.

Sheffield, J., Wood, E. F., Chaney, N., Guan, K., Sadri, S., Yuan, X., ... & Ogallo, L. (2014). A drought monitoring and forecasting system for sub-Sahara African water resources and food security. Bulletin of the American Meteorological Society, 95(6), 861-882.

Caine A, Dorward P, Clarkson G, Evans N, Canales C and Stern D. 2015. Review of Mobile Applications that Involve the Use of Weather and Climate Information: their use and potential for Smallholder Farmers. CCAFS Working Paper no.150. Copenhagen, Denmark: CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS).

Govind, A., & Stigter, K. (2010). III. 2.2.(i) Improving the Issuing, Absorption and Use of Climate Forecast Information. Applied Agrometeorology, 341.

Stigter, K. (2010). Introductory Part. In Applied Agrometeorology (pp. 3-51). Springer, Berlin, Heidelberg.

Stigter, T. Y., Ribeiro, L., & Dill, A. C. (2006). Application of a groundwater quality index as an assessment and communication tool in Agro-environmental policies–Two Portuguese case studies. Journal of Hydrology, 327(3-4), 578-591.

Stigter, C. J., Mohammed, A. E., Al-amin, N. K. N., Onyewotu, L. O. Z., Oteng’i, S. B. B., & Kainkwa, R. M. R. (2002). Agroforestry solutions to some African wind problems. Journal of wind engineering and industrial aerodynamics, 90(10), 1101-1114.

Ren, L., Zhang, N., Wu, P., Huo, H., Xu, G., & Wu, G. (2015). Arbuscular mycorrhizal colonization alleviates Fusarium wilt in watermelon and modulates the composition of root exudates. Plant Growth Regulation, 77(1), 77-85.

Cárdenas, A. A., Amin, S., & Sastry, S. (2008). Research challenges for the security of control systems. HotSec, 5, 15.

Mberego, S., & Sanga-Ngoie, K. (2014). Using locally captured climatic information for guiding local-level agriculturalists in Africa: a case study of Makonde district in Zimbabwe. Journal of land use science, 9(2), 178-194.

Pressman, R. S. (2010). A practitioner’s approach. Software Engineering, 2, 41-42.

Detection of Structured Query Language Injection Attacks Using Machine Learning Techniques

Abstract Views :39 | PDF Views:25

Authors

Taapopi John Angula ¹, Valerianus Hashiyana ¹

Affiliations
1 Department of Computing, Mathematical and Statistical Sciences, University of Namibia, Windhoek, NA

Source

AIRCC's International Journal of Computer Science and Information Technology, Vol 15, No 4 (2023), Pagination: 13-26

Abstract

This paper presents a comparative analysis of various machine learning classification models for structured query language injection prevention. The objective is to identify the best-performing model in terms of accuracy on a given dataset. The study utilizes popular classifiers such as Logistic Regression, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine. Based on the tests used to evaluate the performance of the classifiers, the Naïve Bayes gets the highest level of accurate detection. The results show a 97.06% detection rate for the Naïve Bayes, followed by LogisticRegression (0.9610), Support Vector Machine (0.9586), RandomForest (0.9530), DecisionTree (0.9069), and K-Nearest Neighbor (0.6937). The code snippet provided demonstrates the implementation and evaluation of these models.

Keywords

Classification models, SQL-I, Python, Machine learning, Evaluations

Full Text

References

Lu, D., Fei, J., & Liu, L. (2023). A semantic learning-based SQL injection attack detection technology. Electronics, 12(6), 1-22. https://doi.org/10.3390/electronics12061344

Yunmar, R. A. (2018). Hybrid intrusion detection system using fuzzy logic inference engine for SQL injection attack. Kursor, 9(3), 83-93. https://doi.org/10.28961/kursor.v9i3.147

Triloka, J., &Sutedi, H. (2022). Detection of SQL Injection Attack Using Machine Learning Based on Natural Language Processing. International Journal of Artificial Intelligence Research, 6(2).

Demilie, W. B., &Deriba, F. G. (2022). Detection and prevention of SQLI attacks and developing compressive framework using machine learning and hybrid techniques. Journal of Big Data, 9(1), 1-30. https://doi.org/10.1186/s40537-022-00678-0

Daniyal, A., Maha, A., &Suaad, A. (2022, 09). Detection of SQL Injection Attack Using Machine Learning Techniques: A Systematic Literature Review. Journal of Cybersecurity and Privacy, 2, 764-777.

Vähäkainu, P., &Lehto, M. (2019). Artificial intelligence in the cyber security environment. In Proceedings of the 14th International Conference on CyberWarfare and Security (pp. 431-440). Stellenbosch: ICCWS 2019.

Satapathy, S., Govardhan, A., Raju, K., & Mandal, J. (2015). SQL Injection Detection and Correction Using Machine Learning Techniques. Advances in Intelligent Systems and Computing, 435–442.

Halfond, W. G. J., &Orso, A. (2005). AMNESIA: Analysis and monitoring for NEutralizing SQL-injection attacks. In ASE ‘05: Proceedings of the 20th IEEE/ACM international conference on automated software engineering (pp. 174-183). https://doi.org/10.1145/1101908.1101935

Zhang, W., Yueqin, L., Xiaofeng, L., Shao, M., Mi, Y., Zhang, H., &Zhi, G. (2022). Deep neural network-based SQL injection detection method. Security and Communication Networks, 2022, 1-9. https://doi.org/10.1155/2022/4836289

Theobald, O. (2019). Machine Learning with python. In O. Theobald. Scatterplot Press.

Username
Password
Remember me