Time series prediction based on linear regression and SVR

The application of SVR in the time series prediction is increasingly popular. Because some time series prediction based on SVR wasn't very nice in the efficiency of the forecast, this article presents a new regression based on linear regression and SVR. The new regression separates time series into linear part and nonlinear part, then predicts the two parts respectively, and finally integrates the two parts to forecast. Experiments show that the new regression advances the precision of the forecasting compared to the common SVR

Soft Sensor for Oxide Scales on the Steam Side of Superheater Tubes under Uneven Circumferential Load

A soft sensor for oxide scales on the steam side of superheater tubes of utility boiler under uneven circumferential loading is proposed for the first time. First finite volume method is employed to simulate oxide scales growth temperature on the steam side of superheater tube. Then appropriate time and spatial intervals are selected to calculate oxide scales thickness along the circumferential direction. On the basis of the oxide scale thickness, the stress of oxide scales is calculated by the finite element method. At last, the oxide scale thickness and stress sensors are established on support vector machine (SMV) optimized by particle swarm optimization (PSO) with time and circumferential angles as inputs and oxide scale thickness and stress as outputs. Temperature and stress calculation methods are validated by the operation data and experimental data, respectively. The soft sensor is applied to the superheater tubes of some power plant. Results show that the soft sensor can give enough accurate results for oxide scale thickness and stress in reasonable time. The forecasting model provides a convenient way for the research of the oxide scale failure

Scalable and Efficient Network Anomaly Detection on Connection Data Streams

Everyday, security experts and analysts must deal with and face the huge increase of cyber security threats that are propagating very fast on the Internet and threatening the security of hundreds of millions of users worldwide. The detection of such threats and attacks is of paramount importance to these experts in order to prevent these threats and mitigate their effects in the future. Thus, the need for security solutions that can prevent, detect, and mitigate such threats is imminent and must be addressed with scalable and efficient solutions. To this end, we propose a scalable framework, called Daedalus, to analyze streams of NIDS (network-based intrusion detection system) logs in near real-time and to extract useful threat security intelligence. The proposed system pre-processes massive amounts of connections stream logs received from different participating organizations and applies an elaborated anomaly detection technique in order to distinguish between normal and abnormal or anomalous network behaviors. As such, Daedalus detects network traffic anomalies by extracting a set of significant pre-defined features from the connection logs and then applying a time series-based technique in order to detect abnormal behavior in near real-time. Moreover, we correlate IP blocks extracted from the logs with some external security signature-based feeds that detect factual malicious activities (e.g., malware families and hashes, ransomware distribution, and command and control centers) in order to validate the proposed approach. Performed experiments demonstrate that Daedalus accurately identifies the malicious activities with an average F_1 score of 92.88\%. We further compare our proposed approach with existing K-Means and deep learning (LSTMs) approaches and demonstrate the accuracy and efficiency of our system