Optical networks play a critical role in modern information society. Statistics have revealed that around 50% of optical service faults stem from "errors" in the optical transmission system links, which are monitored through frame overheads. Unfortunately, current maintenance methods are insufficient in accurately predicting these errors, leading to potential disruptions in network services. To address this issue, this study introduces the Gini importance-based feature selection approach and maximum likelihood-based logistic regression algorithm for predicting optical transmission system link performance degradation. To facilitate the application of this model in real-world networks, the Network AI (NAI) three-level intelligent architecture is proposed, and an optical transmission performance degradation prediction system is developed based on this architecture. Experimental results from the network experiment demonstrate the effectiveness of the proposed performance degradation model and NAI-based system.
Deep learning (DL) introduces a novel data-driven programming paradigm, where the system logic is constructed through data training. However, this approach poses challenges in terms of system analysis and defect detection. To address this issue, recent efforts have focused on testing deep learning systems using intuitive criteria known as Neuron Coverage (NC) and its variants. These criteria measure the activation proportion of neurons in a neural network. Unfortunately, recent DL applications have largely overlooked the importance of context during testing. To address this gap, this paper first incorporates the context of the DL pipeline deployed before test execution, such as medical diagnosis and Android Malware detection. Next, we formulate structural coverage criteria to guide test suite generation based on the properties of DL pipeline in different contexts. Furthermore, we proposed a coverage-guided search algorithm to efficiently generate test suites. Experimental results highlight the effectiveness of our approach in uncovering numerous erroneous behaviors in contexts such as medical image diagnosis and Android malware detection. This approach significantly enhances the robustness of DL models by considering the structural aspects of the DL pipeline.
In this paper, we present an Improved Transformer model and a self-supervised framework for detecting anomalies in satellite telemetry data. Previous approaches based on RNN and LSTM variants have shown some progress in anomaly detection, but still require improvement in detection performance and context parallelism. To address these challenges, we propose an innovative Improved Transformer model that incorporates correlation modeling, long-range modeling, and parallel reasoning capabilities. This model features a token split structure, position embedding, and mask unit, which enhance its overall performance. Our self-supervised framework consists of two stages: pre-training and fine-tuning. We conduct extensive experiments on NASA’s public dataset and demonstrate the effectiveness of our proposed framework. Using our self-supervised approach, we achieved a significant 95.7% (3-class) F1-score on the validation set, outperforming supervised counterparts that utilized the same training data.
Bitcoin transactions are created through the concept called Unspent Transaction Output (UTXO). Users put their own UTXOs as inputs into a transaction for Bitcoin transfer and create multiple outputs, each specifying the recipient’s wallet address and the amount to be sent. UTXO refers to an output that has not been used as an input for any transaction yet and each UTXO can only be used as an input once. However, attempting to use a UTXO more than once is called a double-spending attack. Although double-spending in Bitcoin is ultimately impossible due to the system structure, it can occur when a transaction is deemed confirmed and off-chain goods or services are provided before sufficient transaction finality is guaranteed. We consider an attempt of double-spending attack when a UTXO used as an input in a transaction for payment exists together with another transaction on the Bitcoin network that uses the same UTXO as an input. In previous research, we randomly deployed observer nodes on the Bitcoin network and proposed a method to detect double-spending attacks using transaction data in the memory pool and a graph neural network model. In this paper, we analyze the impact of adding observer nodes to the Bitcoin network on the performance of graph neural network-based Bitcoin double-spending attack detection. We conducted experiments to examine the performance differences among three strategies for adding observer nodes. However, it was difficult to compare clear differences due to the performance degradation of the model caused by the differences in graph structure between datasets. Therefore, we provide an analysis of the causes and suggestions for improvement.
The traditional monolithic system architecture faces challenges such as low development efficiency, high maintenance costs, and limited scalability. Refactoring it into a microservice architecture improves efficiency and scalability. Database decomposition is crucial in this process to achieve independence and decoupling between microservices. A decomposition method for relational database is proposed in this paper, which combines static analysis and dynamic analysis to build the incidence matrix between tables, and then clusters the incidence matrix by spectral clustering algorithm to obtain database decomposition results, and automatically matches the decomposition results with microservices. Based on the above mentioned method, an automatic relational database decomposition tool has been designed and implemented. Experimental verification has shown that the tool can efficiently achieve relational database decomposition, thereby enabling support for microservices refactoring to legacy systems.
