M Jagadeeshwar’s scientific contributions

The usage of credit cards has increased significantly as the world becomes increasingly digital and financial transactions happen online. The increasing amount of fraud associated with it causes financial institutions to endure huge losses. We must, therefore, investigate and distinguish between fraudulent and non-fraudulent transactions. We planned to apply the full model training process from start to finish for this investigation. The outcome will be the acquisition of the most effective model capable of differentiating regular transactions from abnormal ones. Credit card fraud is detected using machine learning algorithms, but no systems that are particularly successful at detecting it have been produced so far. The relatively new field of deep learning has been used to solve difficult problems across numerous domains. The purpose of this article is to examine various machine learning models for detecting credit card fraud. We compare each model's performance and output. The best possible performance is possible when the SMOTE technique is used. Undersampling the majority (normal) class is a useful strategy for increasing a classifier's sensitivity to the minority class. This work illustrates that our method of oversampling the minority (abnormal) class and undersampling the majority (normal) class together can increase the classifier's performance more than just undersampling the majority class.

Fraudulent activities pose an important threat in several areas, requiring robust and efficient mechanisms for detection. It is critical to halt fraudulent transactions since they have a long-term influence on financial circumstances. Anomaly detection has several essential applications for detecting fraud. This paper presents a novel fraud detection method using deep learning algorithms, combining Convolutional Neural Networks (CNNs) for feature extraction from transaction data and Long Short-Term Memory (LSTM) networks for capturing temporal dependencies in financial transactions, thereby enhancing robust and efficient detection mechanisms. This paper proposes a new framework that combines Spark with a deep learning technique. However, it compares the performance of deep learning approaches for credit card fraud detection with other machine learning algorithms, including CNN-LSTM, on three distinct financial datasets. This paper also employs several machine learning algorithms for fraud detection, such as random forest, SVM, and KNN. Various parameters are used in comparative analysis. Both the training and testing datasets achieved more than 96% accuracy. The text outlines the creation of a high-performance deep learning model for detecting credit card fraud. The paper proposes hybrid attention to integrate current time output with unit state, determining its weight, and optimizes accuracy using Adam optimization. It uses various machine learning methods for a comparative study using the proposed deep architecture.

ABSTARCT The paper titled " Identity Theft in Cyber Security : Literature Survey " discusses the different security issues facing over Internet with possible literature. The paper also gives the basic idea of cyber security. It also provides possible cyber frauds deals specifically in Identity Theft over the Internet and also various Identity Theft Techniques. This paper mainly focuses on literature survey or review of few research papers from different authors and scholars. This paper explains the various opinions of different authors or scholars on different research papers and articles on cyberspace, cyber security, identity theft in cyberspace and also cyber crimes.

This paper deals with the different security issues facing over Internet and the challenges to solve those issues by considering some cyber security tools and standards. The word Cyber Security is having two terms: cyber and security. The term "Cyber" related to computer or computer networks or it denotes the information technology, or it may be program or data. The term "Security" may be system security, network security, information security or application security. Cyber Security can also be defined as a set of principles and practices that are designed to protect computing resources and online information against threats. In today"s world , in any area the maximum amount of work is doing through the Internet and sensitive and confidential information should be transformed over network. So that there is a chance of hack that information and masquerade it. This paper deals with different security issues of Internet such as Hackers, Phishing, Viruses, Spyware, Worms, Spamming and Identity Theft. This paper also covers the challenges or measures to be taken in order to protect from cyber crimes by enforcing some laws to those cyber criminals and goals to be achieved with the usage of confidentiality tools. Identity Theft is also known as Identity Fraud, is a crime in which obtaining personal identifiable information such as name, identifying number or credit card number , bank account and passwords, digital signatures, social security numbers, fingerprints, driving license numbers or any other information without their permission. This paper deals with various kinds of Identity Theft happened in Internet and the cyber laws with the help of confidentiality tools.