Cloud computing is a cutting-edge system that's widely considered the future of data processing, making cloud computing one of the widely used platforms worldwide. Cloud computing raises problems around privacy, security, anonymity, and availability. Because of this, it is crucial that all data transfers be encrypted. The overwhelming majority of files stored on the cloud are of little to no significance while the data of certain users may be crucial. To solve the problems around security, privacy, anonymity, and availability, so we propose a novel method for protecting the confidentiality and security of data while it is being processed by a cloud platform. The primary objective of this study is to enhance the cloud security with RC6 and 3DES algorithms while attained low cost encryption, and explore variety of information safety strategies. Inside the proposed system, RC6 and 3DES algorithms have been used to enhance data security and privacy. The 3DES has been used to data with a high level of sensitivity to encrypt the key of RC6 and this method is significant improve over the status quo since it increases data security while reduce the amount of time needed for sending and receiving data. Consequently, several metrics, such as encryption time, false positive rate, and P-value, have been determined by analyzing the data. According to the findings, the suggested system attained less encryption time in different file size by securely encrypting data in a short amount of time and it gives outperformance as compared to other methods.

To prevent medical data leakage to third parties, algorithm developers have enhanced and modified existing models and tightened the cloud security through complex processes. This research utilizes PlayFair and K-Means clustering algorithm as double-level encryption/ decryption technique with ArnoldCat maps towards securing the medical images in cloud. K-Means is used for segmenting images into pixels and auto-encoders to remove noise (de-noising); the Random Forest regressor, tree-method based ensemble model is used for classification. The study obtained CT scan-images as datasets from ‘Kaggle’ and classifies the images into ‘Non-Covid’ and ‘Covid’ categories. The software utilized is Jupyter-Notebook, in Python. PSNR with MSE evaluation metrics is done using Python. Through testing-and-training datasets, lower MSE score (‘0’) and higher PSNR score (60%) were obtained, stating that, the developed decryption/ encryption model is a good fit that enhances cloud security to preserve digital medical images.

Intrusion detection system is the most important part of the network security system because the volume of unauthorized access to the network resources and services increase day by day. In this paper a genetic algorithm based intrusion detection system is proposed to solve the problem of the small disjunct in the decision tree. In this paper genetic algorithm is used to improve the coverage of those rules which are cope with the problem of the small disjunct. The proposed system consists of two modules rule generation phase, and the second module is rule optimization module. We tested the effectiveness of the system with the help of the KDD CUP dataset and the result is compared with the REP Tree, Random Tree, Random Forest, Na?ve Bayes, and the DTLW IDS (decision tree based light weight intrusion detection system). The result shows that the proposed system provide the best result in comparison to the above mentioned classifiers.

In the era of information and communication technology, Security is an important issue. A lot of effort and finance are being invested in this sector. Intrusion detection is one of the most prominent fields in this area. Data mining in network intrusion detection can automate the network intrusion detection field with a greater efficiency. This paper presents a literature survey on intrusion detection system. The research papers taken in this literature survey are published from 2000 to 2012. We can see that almost 67 % of the research papers are focused on anomaly detection, 23 % on both anomaly and misuse detection and 10 % on misuse detection. In this literature survey statistics shows that 42 % KDD cup dataset, 20 % DARPA dataset and 38 % other datasets are used by the different researchers for testing the effectiveness of their proposed method for misuse detection, anomaly detection or both.