iosr-JCE   Volume-26 ~ Issue-2 ~ Mar. - Apr. 2024

Abstract: In the rapidly changing field of cybersecurity, artificial Intelligence (AI) is seen as a hope and a worry for ethics and rules. This complete research paper explores the possible effects of using AI technology to secure cyberspace. It mainly looks at ethical difficulties and regulatory issues that come with incorporating these technologies into cybersecurity methods. As AI systems are used more often to find, avoid and handle cyber dangers, it raises important questions about privacy, favoritism, responsibility and whether current law arrangements are enough. This article points out the built-in privacy worries associated with AI-powered cybersecurity mechanisms that use data by looking......

Key Word: Cybersecurity; Artificial Intelligence; Regulatory implications.

[1] Abushgra, A. (2023). How Quantum Computing Impacts Cyber Security. 2023 Intelligent Methods, Systems, And Applications (Imsa), 74-79. Https://Doi.Org/10.1109/Imsa58542.2023.10217756.
[2] Bostrom, N., & Yudkowsky, E. (2014). "The Ethics Of Artificial Intelligence." In K. Frankish & W. M. Ramsey (Eds.), *The Cambridge Handbook Of Artificial Intelligence*. Cambridge: Cambridge University Press, Pp. 316-334.
Https://Doi.Org/10.1017/Cbo9781139046855.020
[3] Cath, C. (2018). Governing Artificial Intelligence: Ethical, Legal And Technical Opportunities And Challenges. Philosophical Transactions Of The Royal Society A: Mathematical, Physical And Engineering Sciences.
[4] Coeckelbergh, M. (2019). Artificial Intelligence: Some Ethical Issues And Regulatory Challenges. , 2019, 31-34. Https://Doi.Org/10.26116/Techreg.2019.003.
[5] European Union General Data Protection Regulation. (2016). Retrieved From Https://Eur-Lex.Europa.Eu/Eli/Reg/2016/679/Oj

Abstract: In India, traditional farming methods have struggled with unpredictable weather patterns. This emphasizes the need for advanced tools that can enhance yield and agricultural decision-making processes. Machine learning (ML) aims to provide a strong framework through which variations in rainfall impact crop production can be predicted, facilitating strategic agricultural planning. By using numerous ML algorithms - including Linear Regression.....

Keywords:machine learning; crop production, rainfall, predictive model

[1]. Gandhi, N., Petkar, O., Armstrong, L., & Tripathy, A. (2016). Rice Crop Yield Prediction In India Using Support Vector Machines. 2016 13th International Joint Conference On Computer Science And Software Engineering (JCSSE), 1-5.
Https://Doi.Org/10.1109/JCSSE.2016.7748856.
[2]. Gulati, P., & Jha, S. (2020). Efficient Crop Yield Prediction In India Using Machine Learning Techniques. International Journal Of Engineering Research And Technology, 8.
[3]. Josephine, B., Ramya, K., Rao, K., Kuchibhotla, S., Kishore, P., , S., & , R. (2020). Crop Yield Prediction Using Machine Learning. ADALYA JOURNAL. Https://Doi.Org/10.37896/Aj9.4/012.
[4]. Kalpana, P., Prem, I., Josephine, S., Mary, R., & Rani, A. (2023). Crop Yield Prediction Using Machine Learning. REST Journal On Data Analytics And Artificial Intelligence. Https://Doi.Org/10.46632/Jdaai/2/1/3.
[5]. Kavita & Mathur, P. (2021). Satellite-Based Crop Yield Prediction Using Machine Learning Algorithm. 2021 Asian Conference On Innovation In Technology (ASIANCON), 1-5. Https://Doi.Org/10.1109/ASIANCON51346.2021.9544562

Abstract: This paper introduces an architecture and methodology for enhancing web service security. We utilize digital signatures and encryption to safeguard SOAP XML messages. Our approach is applied to the transmission and reception of environmental monitoring data between monitoring stations and the resources and environment department's server. By implementing this security protocol, our solution ensures data integrity and confidentiality. This stands in contrast to previous methods that transmit unencrypted and unsigned data, leaving it vulnerable to tampering by malicious actors during transmission.

Keywords: Web service, digital signature, encrypt, environmental monitoring, SOAP XML

[1] Amazon Web Services: Overview Of Security Processes, June 2013.
[2] Joe M. Tekli, Ernesto Damiani, Richard Chbeir And Gabriele Gianini, "Soap Processing Performance And Enhancement" Ieee Transactions On Services Computing, Vol. 5, No. 3, July-September 2012.
[3] Nils Agne Nordbotten, "Xml And Web Services Security Standards", Ieee Communications Surveys & Tutorials, Vol. 11, No. 3, Third Quarter 2009.
[4] Hongbing Wang, Joshua Zhexue Huang, Yuzhong Qu, Junyuan Xie, "Web Services: Problems And Future Directions", 2005.
[5] Doug Tidwell, James Snell, Pavel Kulchenko "Programming Web Services With Soap", First Edition, December 2001.

Abstract: Women are good at sharing different teams to achieve common goals. They often promote peace by eliminating ethnic, religious, cultural and intellectual differences. We all know the importance of women's safety, but we also need to know that they should be left alone. Another factor is the unexplained deaths of women while working in the entertainment and tourism industry. This was not because of suicide, but because of the struggle of women. In today's world, the number of smartphone users is rapidly increasing and therefore smartphones can be used effectively for personal security or various protection purposes. The serious incident that shocked the country also made us think about security issues and led to the emergence of many new applications that will enable women to ensure security on their mobile phones......

Keywords: Women's Safety Alert, Newspaper. android studio HTML5, CSS3, Bootstrap, PHP, MYSQL. Android emulator, xampp-win32-5.5.19-0-VC11.

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Abstract: La dissection artérielle spontanée est une affection relativement rare pouvant toucher les artères à destinée cérébrale (carotides et vertébrales) l'aorte et les artères coronaires ainsi que les artères rénales. Le pronostic dépend de l'artère touchée : le taux de mortalité le plus élevé se voit en cas de dissection aortique où il avoisine les 90% à un mois en l'absence de chirurgie. (1) Dans cet article nous nous intéresserons aux dissections des artères cervicales....

[1] G Sarlon-Bartoli , R Soler Et Al. Dissection Artérielle : Un Même Nom, Plusieurs Entités. Presse Med. 2016; 45: 885–891
[2] Giroud M, Lemesle M, Madinier G, Billiar T, Dumas R. Unilateral Lenticular Infarcts: Radiological And Clinical Syndromes, Aetiology, And Prognosis. J Neurol Neurosurg Psychiatry, 1997 ; 63 : 611-5.
[3] Schievink Wi. Spontaneous Dissection Of The Carotid And Vertebral Arteries. N Engl J Med, 2001 ; 344 : 898-906.
[4] Mas Jl, Bousser Mg, Hasboun D, Laplane D. Extracranial Vertebral Artery Dissections: A Review Of 13 Cases. Stroke 1987;18:1037–47.
[5] Bodiguel E, Naggara O, Mas Jl. Dissection Des Artères Carotides Et Vertébrales Extracrâniennes. Emc - Neurologie 2020;44(3):1-14 [Article 17-046-B-10].

Abstract: Network traffic classification plays a vital role in various aspects of network management and security. With the proliferation of sophisticated network attacks and the increasing complexity of network environments, traditional methods for traffic classification are proving inadequate. In recent years, machine learning techniques have emerged as powerful tools for analyzing network traffic, offering the ability to handle large-scale data streams and adapt to dynamic network conditions. This review paper explores the latest advancements in network traffic classification using machine learning, focusing on techniques for handling challenges such as concept drift, class imbalance......

Keywords: Network Traffic Classification, Multiclass Imbalance, Concept Drift, Review

[1] Khanchi, S., Zincir-Heywood, N., Heywood, M., 2018. Streaming Botnet Traffic Analysis Using Bio-Inspired Active Learning. In: Noms 2018-2018 Ieee/Ifip Network Operations And Management Symposium, Pp. 1–6.
[2] Korycki, L., Krawczyk, B., 2021. Concept Drift Detection From Multi-Class Imbalanced Data Streams. In: 2021 Ieee 37th International Conference On Data Engineering(Icde), Pp. 1068–1079.
[3] Koziarski, M., Woźniak, M., Krawczyk, B., 2020. Combined Cleaning And Resampling Algorithm For Multi-Class Imbalanced Data With Label Noise. Knowl.-Based Syst. 204. Li, W., Canini, M., Moore, A.W., Bolla, R., 2009. Efficient Application Identification And The Temporal And Spatial Stability Of Classification Schema. Comput. Netw. 53, 790–809.
[4] Liu, Q., Liu, Z., 2012. A Comparison Of Improving Multi-Class Imbalance For Internet Traffic Classification. Inf. Syst. Front. 16, 509– 521.
[5] Liu, S.M., Sun, Z.X., 2014. Active Learning For P2p Traffic Identification. Peer-To-Peer Netw. Appl..

Abstract: Besides the legal infrastructure in a society are the security apparatuses that enforce the law with the interest to maintain law and order. The legal infrastructures determine the extent to which law enforcement agents can act or protect the common interests of individuals in the society. It also outlines the civic responsibilities and human rights for the members of the society. Customarily, members of the society are supposed to report any incidence of breakdown of law and order to the appropriate civil and military security apparatuses. The quest to control crime and breakdown of law-and-order increases as the society grows and diversity of human intentions and interactions......

[1] Blau, P., And Joseph E. (2016). Crosscutting Social Circles: Testing A Macro Structural Theory Of Intergroup Relations. Orlando, Academic Press.
[2] Benneth, O And Cyril J (2019). Klein's Comprehensive Etymological Dictionary Of The English Language Standards, Methods, & Technology (Smt) Committee White Paper.
[3] Ekpenyong, S. (2019). Social Inequalities, Collusion, And Armed Robbery In Nigerian Cities. Nigeria, Macmillan Publisher
[4] Elizabeth, A. (2020). Formulating And Evaluating Theoretical Frameworks For Career And Technical Education Research. Journal Of Vocational Educational Research, 26 (1), 27-39.
[5] Forrest, T. (2018). Politics And Economic Development In Nigeria. Journal Of Criminal Investigation, 38 (3), 15-18.

Abstract: Artificial Intelligence (AI) has changed the landscape of security and privacy, with a rapid march toward adversarial AI as one of the central driving themes for the field. Adversarial AI seeks to unearth and constructively attack the vulnerabilities lying within machine learning (ML) models. Primarily, the strikes threaten the very veracity and reliability of the model, leading to severe privacy penetration. This comprises inference attacks, member inference, and model inversion techniques, unveiling the delicate information while preceding severe privacy......

Keywords Adversarial Algorithm, Less Privacy, Machine Learning Security, Adversarial Poisoning, Privacy Technology, Model Robustness, Membership Inference, Model Inversion, Secure AI Systems.

[1]. Oseni, A., Moustafa, N., Janicke, H., Liu, P., Tari, Z., & Vasilakos, A. (2021). Security and privacy for artificial intelligence: Opportunities and challenges. arXiv preprint arXiv:2102.04661.
[2]. Hathaliya, J. J., Tanwar, S., & Sharma, P. (2022). Adversarial learning techniques for security and privacy preservation: A comprehensive review. Security and Privacy, 5(3), e209.
[3]. KEJRIWAL, D. K., & SHARMA, A. (2024). A Hybrid Neuro-Symbolic Framework for Real-Time Detection of Adversarial Attacks in Autonomous Systems.
[4]. Song, L., Shokri, R., & Mittal, P. (2019, November). Privacy risks of securing machine learning models against adversarial examples. In Proceedings of the 2019 ACM SIGSAC conference on computer and communications security (pp. 241-257).
[5]. Malik, J., Muthalagu, R., & Pawar, P. M. (2024). A systematic review of adversarial machine learning attacks, defensive controls and technologies. IEEE Access..

Abstract: This study introduces the Federated Edge-DP framework as a cutting edge approach for guaranteeing GDPR-compliant deployment of TinyML systems via a federated learning (FL) architecture embedded with the provision of differential privacy (DP). The proposed approach, combining lightweight noise injection and selective model partitions.....

Keywords Federated Learning, Differential Privacy, TinyML, GDPR Compliance, Edge AI, Privacy-preserving Machine Learning, Microcontrollers, Legal-Tech Integration, Secure Edge Deployment, Real-time Privacy Protection.

[1]. Dash, B., Sharma, P., & Ali, A. (2022). Federated learning for privacy-preserving: A review of PII data analysis in Fintech. International Journal of Software Engineering & Applications (IJSEA), 13(4).
[2]. Dutta, L., & Bharali, S. (2021). TinyML meets IoT: A comprehensive survey. Internet of Things, 16, 100461.
[3]. El Ouadrhiri, A., & Abdelhadi, A. (2022). Differential privacy for deep and federated learning: A survey. IEEE Access, 10, 22359–22380.
[4]. Jiang, B., Li, J., Yue, G., & Song, H. (2021). Differential privacy for industrial internet of things: Opportunities, applications, and challenges. IEEE Internet of Things Journal, 8(13), 10430–10451.
[5]. Jin, X., Katsis, C., Sang, F., Sun, J., Kundu, A., & Kompella, R. (2022). Edge security: Challenges and issues. arXiv preprint arXiv:2206.07164...