Provenance-Based Endpoint Detection and Response (P-EDR) systems are deemed crucial for future Advanced Persistent Threats (APT) defenses. Despite the fact that numerous new techniques to improve P-EDR systems have been proposed in academia, it is still unclear whether the industry will adopt P-EDR systems and what improvements the industry desires for P-EDR systems. To this end, we conduct the first set of systematic studies on the effectiveness and the limitations of P-EDR systems. Our study consists of four components: a one-to-one interview, an online questionnaire study, a survey of the relevant literature, and a systematic measurement study. Our research indicates that all industry experts consider P-EDR systems to be more effective than conventional Endpoint Detection and Response (EDR) systems. However, industry experts are concerned about the operating cost of P-EDR systems. In addition, our research reveals three significant gaps between academia and industry (1) overlooking client-side overhead; (2) imbalancedalarm triage cost and interpretation cost; and (3) excessive server side memory consumption. This paper's findings provide objective data on the effectiveness of P-EDR systems and how much improvements are needed to adopt P-EDR systems in industry.

Belief rule base (BRB) has attracted considerable interest due to its interpretability and exceptional modeling accuracy. Generally, BRB construction relies on prior knowledge or historical data. The limitations of knowledge constrain the knowledge-based BRB and are unsuitable for use in large-scale rule bases. Data-driven techniques excel at extracting model parameters from data, thus significantly improving the accuracy of BRB. However, the previous data-based BRBs neglected the study of interpretability, and some still depend on prior knowledge or introduce additional parameters. All these factors make the BRB highly problem-specific and limit its broad applicability. To address these problems, a model-agnostic statistical BRB (MAS-BRB) modeling approach is proposed in this paper. It adopts a model-agnostic statistical methodology for parameter extraction, ensuring that the parameters both fulfill their intended roles within the BRB framework and accurately represent complex, nonlinear data relationships. A comprehensive interpretability analysis of MAS-BRB components further confirms their compliance with established BRB interpretability standards. Experiments conducted on multiple public datasets demonstrate that MAS-BRB not only achieves improved modeling performance but also shows greater effectiveness compared to existing rule-based and traditional machine learning models.

Wireless capsule endoscopy (WCE) has become an important tool for gastrointestinal examination due to its non-invasive nature and minimal patient discomfort. However, the quality of WCE images is often limited by built-in lighting and the complex gastrointestinal environment, particularly in the region filled with small intestinal villi. Additionally, the morphology of these villi usually serves as a crucial indicator for related diseases. To address this, we propose a novel method to enhance the clarity of small intestinal villi in WCE images. Our method uses a guided filter to separate the low- and high-frequency components of WCE images. Illumination gain factors are calculated from the low-frequency components, while gradient gain factors are derived from Laplacian convolutions on different regions. These factors enhance the high-frequency components, combined with the original image. This approach improves edge detail while suppressing noise and avoiding edge overshoot, providing clearer images for diagnosis. Experimental results show that our proposed method achieved a 45.47% increase in PSNR compared to classical enhancement algorithms, a 12.63% improvement in IRMLE relative to the original images, and a 31.84% reduction in NIQE with respect to the original images.

Mirror reflections and highlights are commonly observed in images captured by wireless capsule endoscopy (WCE), leading to image blurring or distortion, which increases the difficulty for doctors to observe and analyze gastrointestinal images. Existing methods fail to satisfactorily address specular highlights in the gastrointestinal tract, often resulting in loss of image details, texture blurring, or texture continuity errors. Therefore, we propose a highlight removal method for capsule endoscopy images. This approach enhances confidence term calculation in pixel prioritization by leveraging the ratio characteristics between the R channel and the B channel in the RGB color space of WCE images, enabling the evaluation of the repair possibility for each pixel. Furthermore, by dynamically adjusting the size of the sample block window based on variance and optimizing the selection of matching patches in the sample block window using RGB color channel distance and pixel distance, the accuracy of the best matching patch search is improved. Our extensive experimental results demonstrate that compared to the best-performing Criminisi method among existing traditional approaches, our method achieves reductions of 3.10% and 2.75% in standard deviation and coefficient of variation, respectively. Additionally, compared to the DeepGin method in deep learning, reductions of 4.22% and 10.50% are observed in these metrics, respectively. The regions after highlight removal and the surrounding regions near the highlights have more similar colors and continuous textures.

Feature matching is crucial in image recognition. However, blurring caused by illumination changes often leads to deviations in local appearance-based similarity, resulting in ambiguous or false matches—an enduring challenge in computer vision. To address this issue, this paper proposes a method named MultS-ORB (Multistage Oriented FAST and Rotated BRIEF). The proposed method preserves all the advantages of the traditional ORB algorithm while significantly improving feature matching accuracy under illumination-induced blurring. Specifically, it first generates initial feature matching pairs using KNN (K-Nearest Neighbors) based on descriptor similarity in the Hamming space. Then, by introducing a local motion smoothness constraint, GMS (Grid-Based Motion Statistics) is applied to filter and optimize the matches, effectively reducing the interference caused by blurring. Afterward, the PROSAC (Progressive Sampling Consensus) algorithm is employed to further eliminate false correspondences resulting from illumination changes. This multistage strategy yields more accurate and reliable feature matches. Experimental results demonstrate that for blurred images affected by illumination changes, the proposed method improves matching accuracy by an average of 75%, reduces average error by 33.06%, and decreases RMSE (Root Mean Square Error) by 35.86% compared to the traditional ORB algorithm.

An image enhancement method, which is solving the issue of detail loss caused by the inability of existing image enhancement methods to balance brightness and saturation in Wireless Capsule Endoscope (WCE) low-light environment, is proposed. Firstly, we design a multi-scale fast guided filter to estimate the illumination component and utilize the OTSU method to determine the function parameters based on the grayscale information of the illumination component. Secondly, we construct a brightness enhancement function based on the Weber-Fechner law to achieve brightness enhancement of the V component image. At the same time, we designed the brightness enhancement coefficient and combined with Haar wavelet to operate the S component image to balance the brightness and saturation of the WCE enhanced image. Finally, the image enhancement result is obtained by merging the channels and converting to the RGB color space. Comprehensive experimental results show that compared with existing methods, our proposed method improves the mean, standard deviation and information entropy evaluation criteria by 18.2, 5.81 and 0.26 respectively. Furthermore, the feature point detection and matching numbers of the enhanced images increased by an average of 59.3% and 32.9% respectively. Moreover, the effectiveness of this method is further verified through the improvement of experimental results of single-image depth estimation accuracy.