The complexity and variability of Internet traffic data present significant challenges in feature extraction and selection, often resulting in ineffective abnormal traffic monitoring. To address these challenges, an improved Bidirectional Long Short-Term Memory (BiLSTM) network-based approach for Internet abnormal traffic monitoring was proposed. In this method, a constrained minimum collection node coverage strategy was first applied to optimize the selection of collection nodes, ensuring comprehensive data coverage across network nodes while minimizing resource consumption. The collected traffic dataset was then transformed to enhance data validity. To enable more robust feature extraction, a combined Convolutional Neural Network (CNN) and BiLSTM model was employed, allowing for a comprehensive analysis of data characteristics. Additionally, an attention mechanism was incorporated to weigh the significance of attribute features, further enhancing classification accuracy. The final traffic monitoring results were produced through a softmax classifier, demonstrating that the proposed method yields a high monitoring accuracy with a low false positive rate of 0.2, an Area Under the Curve (AUC) of 0.95, and an average monitoring latency of 5.7 milliseconds (ms). These results indicate that the method provides an efficient and rapid response to Internet traffic anomalies, with a marked improvement in monitoring performance and resource efficiency.
In the field of jurisprudence, judgment element extraction has become a crucial aspect of legal judgment prediction research. The introduction of pre-trained language models has provided significant momentum for the advancement of Natural Language Processing (NLP) technologies, with the Bidirectional Encoder Representations from Transformers (BERT) model being particularly notable for its ability to enhance semantic understanding in unsupervised learning. A fusion model combining BERT and an attention mechanism-based Recurrent Convolutional Neural Network (RCNN) was utilized in this study for multi-label classification tasks, aiming to further extract contextual features from legal texts. The dataset used in this research was derived from the "China Legal Research Cup" judgment element extraction competition, which includes three types of cases (divorce, labor, and lending disputes), with each case type divided into 20 label categories. Four comparative experiments were conducted to investigate the optimization of the model by placing the attention mechanism at different positions. At the same time, previous models were learned and studied and their advantages were analyzed. The results obtained from replicating and optimizing those previous models demonstrate promising legal instrument classification performance.
Open-source intelligence in aerospace technology often contains lengthy text and numerous technical terms, which can affect classification accuracy. To enhance the precision of classifying such intelligence, a classification algorithm integrating the Bidirectional Encoder Representations from Transformers (BERT) and Extreme Gradient Boosting (XGBoost) models was proposed. Initially, key features within the intelligence were extracted through the deep structure of the BERT model. Subsequently, the XGBoost model was utilised to replace the final output layer of BERT, applying the extracted features for classification. To verify the algorithm's effectiveness, comparative experiments were conducted against prominent language models such as Text Recurrent Convolutional Neural Network (TextRCNN) and Deep Pyramid Convolutional Neural Network (DPCNN). Experimental results demonstrate that, for open-source intelligence classification in aerospace technology, this algorithm achieved accuracy improvements of 1.9% and 2.2% over the TextRCNN and DPCNN models, respectively, confirming the algorithm's efficacy in relevant classification tasks.
Blackhole attacks represent a significant threat to the security of communication networks, particularly in emerging network architectures such as Mobile Ad Hoc Networks (MANETs). These attacks, characterized by their ability to obscure malicious behavior, evade conventional detection methods due to their loosely defined signatures and their ability to bypass traditional filtering mechanisms. This study investigates the application of machine learning techniques, specifically Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Decision Tree (DT), for the detection and mitigation of blackhole attacks in MANETs. Simulations conducted in MATLAB 2023a examined network configurations with node densities of 50, 100, 250, and 500 nodes to assess the performance of these classifiers in comparison to conventional detection approaches. The results demonstrated that both SVM and CNN achieved near-perfect detection accuracy of 100% across all network configurations, outperforming traditional methods. SVM was chosen due to its efficacy in handling high-dimensional data, CNN for its ability to learn complex, nonlinear hierarchical features, and DT for its interpretability. The findings underscore the potential of these machine learning models in enhancing the precision of blackhole attack detection, thereby improving network security. Future research is recommended to explore the scalability and training efficiency of these models, particularly through the integration of advanced techniques such as model fusion and deep learning architectures. This study contributes to the growing body of literature on radar wave radio (RWR)-based and machine learning-based attack detection and highlights the potential of artificial intelligence (AI) solutions in transforming traditional emitter identification methods, offering significant improvements to network protection systems.
This study presents a novel image restoration method, designed to enhance defective fuzzy images, by utilizing the Fuzzy Einstein Geometric Aggregation Operator (FEGAO). The method addresses the challenges posed by non-linearity, uncertainty, and complex degradation in defective images. Traditional image enhancement approaches often struggle with the imprecision inherent in defect detection. In contrast, FEGAO employs the Einstein t-norm and t-conorm for non-linear aggregation, which refines pixel coordinates and improves the accuracy of feature extraction. The proposed approach integrates several techniques, including pixel coordinate extraction, regional intensity refinement, multi-scale Gaussian correction, and a layered enhancement framework, thereby ensuring superior preservation of details and minimization of artifacts. Experimental evaluations demonstrate that FEGAO outperforms conventional methods in terms of image resolution, edge clarity, and noise robustness, while maintaining computational efficiency. Comparative analysis further underscores the method’s ability to preserve fine details and reduce uncertainty in defective images. This work offers significant advancements in image restoration by providing an adaptive, efficient solution for defect detection, machine vision, and multimedia applications, establishing a foundation for future research in fuzzy logic-based image processing under degraded conditions.
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