Image steganalysis has been a key technology in information security in recent years. However, existing methods are mostly limited to the binary classification for detecting steganographic images used in digital watermarking, privacy protection, illicit data concealment, and security images, such as unaltered cover images or surveillance images. They cannot identify the steganography algorithms used in steganographic images, which restricts their practicality. To solve this problem, this paper proposes a general steganography algorithms recognition scheme based on image big data matching with improved ResNet50. The scheme first intercepts the image region with the highest complexity and focuses on the key features to improve the analysis efficiency; subsequently, the original image of the image to be detected is accurately located by the image big data matching technique and the steganographic difference feature image is generated; finally, the ResNet50 is improved by combining the pyramid attention mechanism and the joint loss function, which achieves the efficient recognition of the steganography algorithm. To verify the feasibility and effectiveness of the scheme, three experiments are designed in this paper: verification of the selection of the core analysis region, verification of the image similarity evaluation based on Peak Signal-to-Noise Ratio ($PSNR$), and performance verification of the improved ResNet50 model. The experimental results show that the scheme proposed in this paper outperforms the existing mainstream steganalysis models, such as ZhuNet and YeNet, with a detection accuracy of 96.11%, supports the recognition of six adaptive steganography algorithms, and adapts to the needs of analysis of multiple sizes and image formats, demonstrating excellent versatility and application value.

In the realm of urban logistics, optimizing vehicle routes for varied cargo types—including refrigerated, fragile, and standard cargo—poses significant challenges amid complex urban infrastructures and heterogeneous vehicle capacities. This research paper introduces a novel model for the multi-type capacitated vehicle routing problem (MT-CVRP) that harnesses an advanced ant colony optimization algorithm, dubbed Lévy-EGACO. This algorithm integrates Lévy flights and elitist guiding principles, enhancing search efficacy and pheromone update processes. The primary objective of this study is to minimize overall transportation costs while optimizing the efficiency of intricate route planning for vehicles with diverse load capacities. Through rigorous simulation experiments, we corroborated the validity of the proposed model and the effectiveness of the Lévy-EGACO algorithm in optimizing urban cargo transportation routes. Lévy-EGACO demonstrated a consistent reduction in transportation costs, ranging from 1.8% to 2.5% compared to other algorithms, across different test scenarios following base data modifications. These findings reveal that Lévy-EGACO substantially improves route optimization, presenting a robust solution to the challenges of MT-CVRP within urban logistics frameworks.

Network anomaly detection faces unique challenges from dynamic traffic, including large data volume, few attributes, and human factors that influence it, making it difficult to identify typical behavioral characteristics. To address this, we propose using Sketch-based Profile Evolution (SPE) to detect network traffic anomalies. Firstly, the Traffic Graph (TG) of the network terminal is generated using Sketch to identify abnormal data flow positions. Next, the Convolutional Neural Network and Long Short-Term Memory Network (CNN-LSTM) are used to develop traffic behavior profiles, which are then continuously updated using Evolution to detect behavior pattern changes in real-time data streams. SPE allows for direct processing of raw traffic datasets and continuous detection of constantly updated data streams. In experiments using real network traffic datasets, the SPE algorithm was found to be far more efficient and accurate than PCA and Basic Evolution for outlier detection. It is important to note that the value of φ can affect the results of anomaly detection.

For classified and sensitive electronic documents within the scope of enterprises and organizations, in order to standardize and strengthen the confidentiality management of enterprises and meet the actual needs of secret text classification, a document automatic classification optimization method based on keyword retrieval and the kNN classification algorithm is proposed. The method supports keyword classification management, provides users with keywords of multiple risk levels, and then combines a matching scanning algorithm to label keywords of different levels. The text with labels is used as the training set of the kNN algorithm to classify the target text and realize the classification protection of text data. Aimed at solving the shortcomings of large feature vector dimension, low classification efficiency, and low accuracy in existing kNN text classification methods, an optimization method is proposed using a feature selection algorithm and a kNN algorithm based on an AVX instruction set to realize real-time classification of massive texts. By constructing a keyword dictionary and an optimized feature vector, parallel calculation of the feature vector weight and distance vector is realized, and the accuracy and efficiency of text classification are improved. The experimental results show that the multi-classification effect of the feature selection algorithm used in this paper, tf-DE, is better than that of the traditional tf-idf algorithm, and the classification effect of kNN is comparable to that of the support vector machine (SVM) algorithm. With the increase in feature vector dimensions, the classification effect of the text classification algorithm is improved and the classification time also increases linearly. The AVX-256 acceleration method takes about 55% of the time of the original version, thus verifying the effect of multi-classification of massive texts for document confidentiality management.

The traditional blockchain deployment process is too complicated and has high technical requirements for blockchain deployers. Deploying a blockchain requires building a complex software-dependent environment, being able to use Linux commands for cumbersome parameter configurations, as well as the need to consider whether the hardware meets the requirements for running a blockchain. To address these current challenges in blockchain deployment both domestically and internationally, a web-based automatic deployment system with an interactive front-end and back-end has been developed. This system streamlines the process by automatically configuring and deploying blockchains while providing deployers with a graphical interface to monitor the entire deployment procedure. Meanwhile, in order to improve the efficiency of blockchain deployment, a Hidden Markov Model has been designed for blockchain deployment, which can predict the best deployment method for blockchain deployment under the current software environment. As one of the excellent blockchain platforms in China, Chainmaker has the outstanding features of independent control, flexible assembly, software and hardware integration, open source, and openness. The system takes Chainmaker as an experimental object and after a lot of tests, it can easily build a blockchain network on the server.