iosr-JCE   Volume-25 ~ Issue-5 ~ Sep - Oct. 2023

Abstract: The proliferation of the Internet of Things (IoT) has ushered in a new era of connectivity and automation, revolutionizing industries and enhancing everyday lives. However, the rapid growth and widespread adoption of IoT devices have also exposed an alarming landscape of cybersecurity threats that challenge the security and integrity of these interconnected ecosystems. This research paper delves into the intricate web of cybersecurity threats that loom over the IoT and explores the vulnerabilities that contribute to their proliferation. In this study, we provide an in-depth analysis of various cybersecurity threats that target IoT devices and networks, ranging from.......

Key Word: Internet of Things; Cybersecurity; Attack, Vulnerabilities

[1]. Altulaihan, E.; Almaiah, M.A.; Aljughaiman, A. Cybersecurity Threats, Countermeasures And Mitigation Techniques On The Iot: Future Research Directions. Electronics 2022, 11, 3330. Https:// Doi.Org/10.3390/Electronics11203330program (NCEP) Expert Panel On Detection, Evaluation, And Treatment Of Highblood Cholesterol In Adults (Adult Treatment Panel III) Finalreport. Circulation. 2002;106(25, Article 3143).
[2]. El Hakim, Ahmed. (2018). Internet Of Things (Iot) System Architecture And Technologies, White Paper..
10.13140/RG.2.2.17046.19521.
[3]. Sidhu S, Mohd BJ, Hayajneh T. Hardware Security In Iot Devices With Emphasis On Hardware Trojans. Journal Of Sensor And Actuator Networks. 2019; 8(3):42. Https://Doi.Org/10.3390/Jsan8030042
[4]. Dr. Shikha Gupta, Vaama Nikam, Tanay Mukadam, Prathmesh Deshmukh, Prathamesh Bhanse. Cyber Security For Internet Of Things, IJRASET, Volume 10 Issue X, Oct 22, ISSN 2321-9853.
[5]. Phillip Williams, Indira Kaylan Dutta, Hisham Daoud, Magdy Bayoumi, A Survey On Security In Internet Of Things With A Focus On The Impact Of Emerging Technologies, Internet Of Things, Volume 19, 2022, 100564, ISSN 2542-6605,.

Abstract: Forex trading plays an indispensable role in the financial market, and the profit reaped through it are high. The benefits made are subject to the volume exchanged and the currencies in which it is exchanged. Prediction of overseas change rate is quite tough due to the fact of its irregular and nonlinear data. There are many parameters which affect the forex rate prediction like the inflation rates, economic growth, interest rates, government and employment data which makes the prediction of forex rate difficult. The accuracies obtained using forex prediction algorithms had to be significantly improved for monetary gains.......

Key Word: Forex, ARIMA Model, Forex Prediction, Artificial Neural network (ANN), Backpropagation

[1]. Ramadhani, I., Jondri, & Rismala, R. (2016). Prediction of multi-currency exchange rates using correlation analysis and backpropagation. 2016 International Conference on ICT For Smart Society (ICISS). doi:10.1109/ictss.2016.7792850
[2]. Ranjit, S., Shrestha, S., Subedi, S., & Shakya, S. (2018). Foreign Rate Exchange Prediction Using Neural Network and Sentiment Analysis. 2018 International Conference on Advances in Computing, Communication Control and Networking
(ICACCCN).doi:10.1109/icacccn.2018.8748819
[3]. Dr. S. Kumar Chandar, Dr. M. Sumathi and Dr S. N. Sivanandam, "Foreign Exchange Rate Forecasting", Vol 9(8), Indian Journal of Science and Technology, 2016
[4]. Ananya Narula, Chandra Bhanu Jha and Ganapati Panda, Development and Performance Evaluation of Three Novel Prediction Models for Mutual Fund NAV Prediction, Annual Research Journal of Symbiosis Centre for Management Studies, Pune, Vol. 3, April 2015, pp. 227–238
[5]. Wang, S., Tang, Z., & Chai, B. (2016). Exchange rate prediction model analysis based on improved artificial neural network algorithm. 2016 International Conference on Communication and Electronics Systems (ICCES). doi:10.1109/cesys.2016.7889912.

Abstract: Loan business is one of the major income sources for bank. Loan default problem is a major issue for loan business. Loans, specifically whether borrowers repay the loan or default on it, have a significant impact on a bank's profitability. By anticipating loan defaulters, the bank is able to reduce its non-performing assets. Three primary predictive analytics techniques—I Data Collection, II Data Cleaning, and III Performance Assessment—are used to research the prediction of loan defaulters. Experimental investigations reveal that when it comes to loan forecasting, the KNN model performs better than the Decision tree model..

Key Word: Machine learning, Loan prediction, Banking, Decision tree, KNN

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[2] T.M. Luong, Harald Scheule & Nitya Wanzare, - "Impact Of Mortgage Soft Information In Loan Pricing On Default Prediction Using Machine Learning," International Review Of Finance, September 2022.
[3] Baodong Li, - "Online Loan Default Prediction Model Based On Deep Learning Neural Network," Hindawi Computational Intelligence And Neuroscience, August 2022.
[4] Weidong Chen & Yiheng Li, - "Entropy Method Of Constructing A Combined Model For Improving Loan Default Prediction: A Case Study In China," Journal Of The Operational Research Society, December 2019.
[5] T. Aditya Sai Srinivas, Somula Ramasubbareddy, And K.Govinda, - "Loan Default Prediction Using ML Techniques," Innovations In Computer Science And Engineering, March 2022.