In this paper, we propose a BGP anomaly detection framework called BAlet that delivers both temporal and spatial localization of the potential anomalies. It requires only a simple count of BGP update messages collected over a certain period. We first investigate the self-similarity in BGP update traffic and present a quantitative validation. The strength of wavelet analysis in handling signals with scaling property and earlier success in applying it for network anomaly detection motivate us to apply the same technique on BGP routing traffic. Later by clustering the anomalies detected at different locations, BAlet is capable of identifying possible network-wide anomalous events. Our method does not rely on any information within the BGP messages, and serves as a complementary tool to reduce the candidate data set for further detailed root cause analysis. We evaluate BAlet on real BGP data sets that are known to contain anomalies. Results show that it is capable of detecting network-wide events such as message volume surges caused by slammer worm attack, and separating affected ASes from the rest.

In infrared small target detection, the infrared patch image (IPI)-model-based methods produce better results than other popular approaches (such as max-mean, top-hat, and human visual system) but in some extreme cases it suffers from long processing times and inconsistent performance. In order to overcome these issues, we propose a novel approach of dividing the traditional target detection process into two steps: suppression of background noise and elimination of clutter. The workflow consists of four steps: after importing the images, the second step applies the alternating direction multiplier method to preliminarily remove the background. Comparatively to the IPI model, this step does not require sliding patches, resulting in a significant reduction in processing time. To eliminate residual noise and clutter, the interim results from morphological filtering are then processed in step 3 through an improved new top-hat transformation, using a threefold structuring element. The final step is thresholding segmentation, which uses an adaptive threshold algorithm. Compared with IPI and the new top-hat methods, as well as some other widely used methods, our approach was able to detect infrared targets more efficiently (90% less computational time) and consistently (no sudden performance drop).

A typical pulsed thermography procedure results in a sequence of infrared images that reflects the evolution of temperature over time. Many features of defects, such as shape, position, and size, are derived from single image by image processing. Hence, determining the key frame from the sequence is an important problem to be solved first. A maximum standard deviation of the sensitive region method was proposed, which can identify a reasonable image frame automatically from an infrared image sequence; then, a stratagem of image composition was applied for enhancing the detection of deep defects in the key frame. Blob analysis had been adopted to acquire general information of defects such as their distributions and total number of defects. A region of interest of the defect was automatically located by its key frame combined with blob analysis. The defect information was obtained through image segmentation techniques. To obtain a robustness of results, a method of two steps of detection was proposed. The specimen of polyvinyl chloride with two artificial defects at different depths as an example was used to demonstrate how to operate the proposed method for an accurate result. At last, the proposed method was successfully adopted to examine the damage of carbon fiber-reinforced polymer. A comparative study between the proposed method and several state-of-the-art ones shows that the former is accurate and reliable and may provide a more useful and reliable tool for quality assurance in the industrial and manufacturing sectors.

In order to visualize the evolution process of impact damage inside carbon fiber-reinforced polymer (CFRP) laminates, the concept of impact damage reconstruction of CFRP laminates was proposed, and a background subtraction image segmentation method based on RGB color space was proposed to extract the damage of materials feature. First, infrared thermography was used to collect damage sequence images of CFRP laminates under different impact loads. The infrared sequence thermal images were prescreened, combined with weighted average fusion to obtain the fusion images and background images, and the background subtraction operation was performed to obtain the subtraction result image. The results of the group with the highest similarity were confirmed and based on this, the 3D reconstruction of the impact interlaminar damage of the laminate was constructed with the help of the 3D Volume of the IMAGE J software.

This study introduces a graphical user interface (GUI) based on MATLAB to realize the automatic ex-traction of sizes of defects from the infrared sequence. To obtain the edge of the defect at deeper layer, a fusion stratagem of the maximum of gray values is adopted for an image subset in the sequence. Blob analysis to the fusion image is used to obtain the general information of defects of a specimen including the distributions and numbers of defects. The frame image is determined for a certain defect according to the peak of the time history curve of sensitive region variance. It can yield a region of interest (ROI) to expand the blob in the selected frame and the defect can be acquired by image segmentation. The results show that through this GUI, a better thermal image can be selected from a set of infrared sequence diagrams for quantitative extraction of different buried depth defect areas, which realizes automatic defect extraction, and its relative error is within 5%. The research on infrared automatic detection technology has certain significance.