Automated Contextual Anomaly Detection for Network Interface Bandwidth Utilisation: A Case Study in Network Capacity Management

We present a contextual anomaly detection methodology utilised for the capacity management process of a managed service provider that administers networks for large enterprises. We employ an ensemble of forecasts to identify anomalous network traffic. Stream of observations, upon their arrival, are compared against these baseline forecasts and alerts generated only if the anomalies are sustained. The results confirm that our approach significantly reduces false alerts, triggering rather more accurate and meaningful alerts to a level that could be proactively consumed by a small team. We believe our methodology makes a useful contribution to the applications enabling proactive capacity management.

instance: Soma-based multi-user interaction design for the telematic sonic arts

The telematic work instance is a performance for viola and dance that digitally connects performers in Vancouver and Cape Town. The network interface enables a violist and a dancer to simultaneously play multi-user digital music-dance instruments over the internet with music and dance. The composition, design and performance interaction of instance draw from acoustic multi-user instrument paradigms and music-dance interactions in the African performing arts to explore the idiosyncrasies of the telematic performance space. The iterative design process implements soma-based research methods to inspire sonic compositional material with the body and to explore the performers’ embodied experience of sonic aesthetics during their interaction.

Digital Video Image Preprocessing Algorithm Based on Embedded System

To solve the massive noise contained in the images acquired under low illumination, we designed a digital video image Preprocessing device with the denoising function. Based on the embedded CPU and operating system, video images are acquired by the camera. The noise contained in the video images is filtered by the improved median filtering algorithm and wavelet image denoising. Subsequently, the images are transmitted through USB and network interface, and the storage function of image files is implemented. The device can remove the noise contained in videos effectively, which is conducive to performing more advanced processing on the images.

Vulnerability Recognition and Resurgence in Network based on Prediction Model and Cognitive based Elucidation

The challengeable factor in current network issue is identification of vulnerability and the same can be prevented before it occurs. Many traditional measures applied to keep track of assessing the system in terms of misconduct calculation. There are two different kind application running via network interface which are known process application and unknown process application. Known applications can be managed with the help of existing approaches whereas resolving problems of unknown applications are questionable. The proposed solution addresses this issue by applying cognitive based solutions and supervised learning model. Traffic parameters considered here as major concern and feature extraction is done against parameters flow of information does after pre-process the data. Training, Automation and detection is a sequence of process is used to find vulnerability misconduct in network and simulated with the help of python.