A mobile agent is a small piece of program that migrates automatically among different platforms, on which it executes assigned tasks. Mobile agents migrate in malicious and unsecure networks. So, there will be chances of compromising mobile agent as well as host computer. So, during the mobile agent executing life cycle there will be chances of stealing of confidential information about host and agents. The application of mobile agents is increasing day by day in various domains such as distributed computing, cloud computing and Internet of Things. In this article, we propose a method based on lattice-based dynamic threshold Chinese Remainder Theorem for the security of mobile agents. The combination of dynamic threshold, lattice-based encryption-decryption and Chinese remainder theorem provides optimal security as compared to traditional approach. After the analysis of turnaround time of creation of share, distribution and recreation of secret is optimal as compared to traditional approach. Another comparison is also done on the basis of memory utilization, computational cost, communication overhead and scalability of proposed method. Proposed approaches provide effectiveness of security for mobile agents in a decentralized and malicious environment. The framework was implemented in Python and analyzed with 10 to 1000 mobile agents. With 1000 mobile agents, the framework gains a turnaround time of 12.4 ms, memory utilization of 18.7 MB, and CPU efficiency of 91.3%, demonstrating better scalability and computational cost compared with Shamir Secret Sharing, Integer CRT, Homomorphic Encryption, Vectorized Tree Parity Machine and Boneh Goh Nissim techniques. Performance evaluation based on computational cost, communication overhead and scalability further shows the effectiveness of the framework. These outcomes show that the proposed methods provide an efficient, scalable and post-quantum-secure method for securing mobile agents in cloud computing, Internet of Things (IoT), and other decentralized distributed computing environments.

Electronic devices and internet purchasing are more common today. For online line shopping most of people are using internet banking and credit for doing payment for purchasing. For time saving and various offers on credit card and debit card customer prefer on line shopping like various platform Amazon, Flip cart, big basket etc. For online transaction security is prime concern. There is various type of attack possible during online transaction, stealing of password, fraud transaction, and meet in middle attack etc. During the online transaction stealing confidential information like OTP, transfer money from someone account to another account is a crime.   In the digital world fraud during the online transaction day by day increases exponentially. To detect the unauthenticated transaction and fraud during online used various methods. Data is playing very important role during the online fraud. So, knowledge discovery is most frequently used to protect online fraud. In this paper suggested a technique based on knowledge discovery and machine learning methods, we strive to develop the best model possible in this research study to predict transactions involving fraud and transactions involving no fraud. Fraud detection uses a variety of machine learning techniques, including K-Means clustering methods, Support Vector Classifier, Logistic Regression, and Anomaly Detection Algorithm Techniques. After analysis it was found that Anomaly Detection Algorithm Techniques gives best accuracy for fraud detection 99.85%.