Abstract-The classical feedback control systems has been a successful theory in many engineering applications like electrical power, process, and manufacturing industries. For more than a decade there is active research in exploring feedback control systems applications in computing and some of the results are applied to the commercial software products. There are good number of research review papers on this subject exist, giving high level overview, explaining specific applications like load balancing or CPU utilization power management in data centers. We observe that majority of the control system applications are in Web and Application Server environments. We attempt to discuss on how control systems is applied to Web and Application(JEE) Servers that are deployed in Enterprise and cloud environments. Our paper presents this review with a specific emphasis on Java based Web, Application and Enterprise Server Bus environments. We conclude with the future reserach in applying control systems to Enterprise and Cloud environments.

Intrusion detection system (IDS) is typically responsible for tracking and identifying fraudulent behaviours in any operating network. This paper propose an extended sparse transient search deep transfer learning-based intrusion detection system (STSDTL-IDS) that overcomes the limitations and classifies complex attacks accurately and finely. A feature selection method, namely, chimp optimization algorithm (COA) is used to eliminate the unwanted features and it is used to detect assaults by identifying relevant aspects in the dataset with high precision. The proposed method is a hybrid method which includes sparse transient auto encoder with deep transfer learning, and extended Transient Search Optimization algorithm to improve the cloud IDS efficiency. The performance of the sparse deep transfer learning algorithm is improved using the extended transient search optimization. The python tool is used and the UNSW-NB15 and CICIDS2017 datasets are used. The experimental result shows that the proposed method outperforms when compared to the existing methods.