In recent times new Antivirus software are using Machine learning to make their detection even more sophisticated. Machine learning, reinforcement learning, and deep learning along with data analysis have made it possible to implement a dynamic analysis procedure to detect any malware. So in this paper, we will be introducing an algorithm by which we will not only be able to bypass signature-based detection by ‘rephrasing the code’ using CLP, along with the behavioral-based analysis, which are the most prominent methods for the job, but also will be attempting to go around the real-time monitoring and try to be undetected during the forensic investigation by clearing code footprint.

Machine learning and data mining techniques are utiized for enhancement of the security of any network. Researchers used machine learning for pattern detection, anomaly detection, dynamic policy setting, etc. The methods allow the program to learn from data and make decisions without human intervention, consuming a huge training period and computation power. This paper discusses a novel technique to predict an upcoming attack in a network based on several data parameters. The dataset is continuous in real-time implementation. The proposed model comprises dataset pre-processing, and training, followed by the testing phase. Based on the results of the testing phase, the best model is selected using which, event class which may lead to an attack is extracted. The event statistics are used for attack prediction. Implementation of the proposed architecture is evaluated against accuracy, precision, recall, and F1 score for processed data.

Cloud computing offers various services to its users, ranging from infrastructure, and system development environment, to software as a service over the internet. Having such promising services available over the internet consistently, it has become an ever-demanding facility. As a reliable services provider, a cloud service provider (CSP) needs to deliver its services seamlessly to users and is also required to optimally utilize the resources. Optimal resource utilization eliminates over and under-provisioning and improves the availability of cloud services. Therefore, it is a great need to have a model allowing CSP to systematize its resources to cater to customers' demands. Such a model should be computationally light and quick enough to produce effective results. In this work, a simple yet effective neural network-based resource prediction model named MVMS is proposed, which enables a CSP to predict the customer's resource demand in advance. The results show that compared to GRU, the proposed Multi-Variate Multi-Step (MVMS) model predicts the resources accurately. Thus, CSP can schedule the resources precisely and process real-time requests of users. Experiments on the bitbrains dataset indicate that the proposed MVMS resource prediction model is quick and accurate, with lower RMSE and MAE values.