Various malicious activities performed by the social bots have brought a crisis of trust to the online social networks. In this paper, we propose a social bot detection method, named Accou2vec, based on community walk. First, in order to cut off the attacking edges between the human and bot accounts, the deep autoencoder-like non-negative matrix factorization community detection algorithm is leveraged to divide the social graph into multiple subgraphs. Then, we design the community walk rule that controls the intra-community walk and inter-community walk differently, considering both the number of nodes and edges in the community. Subsequently, the graph representation learning is used to learn the representation vector of each account. Finally, the representation vectors of labeled social bots and human accounts are used to train the classifier for social bots detection. Extensive experimental results on two real-world datasets show the superior performance of the proposed method over the state-of-the-art.

Social network alignment aims to discover the potential correspondence between users across different social platforms. Recent advances in graph representation learning have brought a new upsurge to network alignment. Most existing representation-based methods extract local structural information of social networks from users’ neighborhoods, but the global structural information has not been fully exploited. Therefore, this manuscript proposes a dual graph convolutional networks-based method (DualNA) for social network alignment, which combines user representation learning and user alignment in a unified framework. Specifically, we design dual graph convolutional networks as feature extractors to capture the local and global structural information of social networks, and apply a two-part constraint mechanism, including reconstruction loss and contrastive loss, to jointly optimize the graph representation learning process. As a result, the learned user representations can not only preserve the local and global features of original networks, but also be distinguishable and suitable for the downstream task of social network alignment. Extensive experiments on three real-world datasets show that our proposed method outperforms all baselines. The ablation studies further illustrate the rationality and effectiveness of our method.

Various malicious activities performed by social bots have brought a crisis of trust to online social networks. Existing social bot detection methods often overlook the significance of community structure features and effective fusion strategies for multimodal features. To counter these limitations, we propose BotCF, a novel social bot detection method that incorporates community features and utilizes cross-attention fusion for multimodal features. In BotCF, we extract community features using a community division algorithm based on deep autoencoder-like non-negative matrix factorization. These features capture the social interactions and relationships within the network, providing valuable insights for bot detection. Furthermore, we employ cross-attention fusion to integrate the features of the account’s semantic content, properties, and community structure. This fusion strategy allows the model to learn the interdependencies between different modalities, leading to a more comprehensive representation of each account. Extensive experiments conducted on three publicly available benchmark datasets (Twibot20, Twibot22, and Cresci-2015) demonstrate the effectiveness of BotCF. Compared to state-of-the-art social bot detection models, BotCF achieves significant improvements in accuracy, with an average increase of 1.86%, 1.67%, and 0.47% on the respective datasets. The detection accuracy is boosted to 86.53%, 81.33%, and 98.21%, respectively.

In social media, coordinated groups spread false information and guide public opinion through organized social behaviors. Coordination detection methods based on behavioral sequences identify coordinated groups among users by extracting temporal and structural data. However, existing methods generally concentrate on identification and analysis from an individual perspective, lacking evaluation and detection from a group perspective to measure the degree of coordination, which results in low accuracy of detection results. To address these issues, we propose an unsupervised evaluation method of Relative Coordination Degree of Groups (RCDG). This method models the global time and differentiated network features from group perspective using Neural Time Point Process (NTPP) and Graph Contrastive Learning (GCL), and evaluates the relative degree of coordination based on the fused features. Experimental results show that RCDG can effectively evaluate the relative degree of coordination among groups. Furthermore, it can achieve an accuracy rate of up to 99.8% in detecting coordinated groups.

Social bot detection methods using graph neural networks (GNNs) are thriving, but the structural complexity of GNN also brings more training costs on large-scale data and interpretability concerns. In this paper, we propose a social bot detection method, BotCS, which utilizes both the attribute and the structural features of the social graph at a smaller computational cost than GNN-based detection methods. BotCS makes a base prediction with a simple multilayer perceptron classifier (MLP) and then propagates the classification residuals of the training set to other nodes for further correction. Then, it smooths the corrected prediction by label propagation. With little end-to-end training, this course is low-cost and scalable. We analyze the local interaction pattern between bots and human users, and designed the corresponding residual propagation and smoothing rules from the local perspective, which ensures the interpretability of BotCS. Experimental results show that BotCS achieves similar detection results to state-of-the-art methods with one or two orders of magnitude fewer parameters.

With the rapid development of Internet technologies, an increasing amount of auxiliary data can be readily obtained through Web services. To alleviate the data sparsity issue, auxiliary data based recommendation has emerged for better recommendation performance. However, existing auxiliary data based methods suffer from two problems. First, only the relation features related to the meta-paths are extracted from auxiliary data, which may lead to features useful for recommendation being lost irreversibly. Second, an assumption is made that an individual has the same preference over the identical characteristic of different items, which is often invalid and may lead to misleading recommendations. Actually, a user may place different importance on the same feature of different items, and an item may get different attention from the same feature of different users. In this paper, we propose a neural network framework, named Neural Attention Recommendation model (NARec), for auxiliary data based collaborative filtering. For the first problem, we characterize users and items from three aspects, namely latent features, attribute features, and meta-path based relation features, which can comprehensively extract the useful recommendation features from auxiliary data. Regarding the second problem, we integrate different user features and item features into an attention mechanism based rating prediction model for recommendation, which can adaptively characterize the personalized features of users and items. Extensive experiments on three real-world datasets demonstrate that NARec significantly outperforms the state-of-the-art recommendation methods in the rating prediction task.

Abstract To address the security risk caused by fixed offset mapping and the limited recoverability of random mapping used in image watermarking, a self‐embedding fragile image watermarking algorithm based on deneighbourhood mapping are proposed. First, the image is divided into several 2 × 2 blocks, and authentication watermark and recovery watermark are generated based on the average value of the image blocks. Then, the denighbourhood mapping is implemented as, for each image block, its mapping block is randomly selected outside its neighbourhood. Finally, the authentication watermark and the recovery watermark are embedded into the image block itself and its mapping block. Theoretical analysis indicates that in the case of continuous area tampering, the proposed watermarking algorithm can achieve a better recovery rate than that of the method based on the random mapping. The experimental results verify the rationality and effectiveness of the theoretical analysis. Moreover, compared with the existing embedding algorithms based on random mapping, chaos mapping, and Arnold mapping, in the case of continuous area tampering, the proposed algorithm also achieves a higher average recovery rate.

The Cluster-based Routing Algorithm for Wireless Sensor Network based on Subtractive Clustering (SCC algorithm) generates nodes of cluster head in dense area of the node by relying on subtractive clustering. The application of this algorithm effectively solves the attribution problem of non-cluster head node in conventional algorithms, and consumption is evenly distributed throughout the network. Through simulation experiments, it can be found that the application of the SCC algorithm can map the distribution of nodes of cluster head reasonably and delay the death time of the first node to prolong the lifetime of network and balance the energy consumption of nodes.

Wireless sensor network (WSN) is the cutting-edge technology of modern monitoring technology. There is a direct relationship between the length of its lifetime and its performance. Efficient and reasonable clustering and routing algorithm can reduce the energy loss in the network, which is of great significance for prolonging the network lifetime. The traditional WSN protocol has been difficult to meet the needs of the contemporary society. This paper focuses on the advantages and disadvantages of the traditional WSN protocol, and puts forward relevant improvement measures to optimize the traditional WSN protocol and extend the network life time.

3D steganalysis aims to find the changes embedded through steganographic or information hiding algorithms into 3D models. This research study proposes to use new 3D features, such as the edge vectors, represented in both Cartesian and Laplacian coordinate systems, together with other steganalytic features, for improving the results of 3D steganalysers. In this way the local feature vector used by the steganalyzer is extended to 124 dimensions. We test the performance of the extended local feature set, and compare it to four other steganalytic features, when detecting the stego-objects watermarked by six information hiding algorithms.

The CAPTCHA has become an important issue in multimedia security. Aimed at a commonly used text-based CAPTCHA, this paper outlines some typical methods and summarizes the technological progress in text-based CAPTCHA breaking. First, the paper presents a comprehensive review of recent developments in the text-based CAPTCHA breaking field. Second, a framework of text-based CAPTCHA breaking technique is proposed. And the framework mainly consists of preprocessing, segmentation, combination, recognition, postprocessing, and other modules. Third, the research progress of the technique involved in each module is introduced, and some typical methods of segmentation and recognition are compared and analyzed. Lastly, the paper discusses some problems worth further research.

Fingerprinting attacks are one of the most severe threats to the security of networks. Fingerprinting attack aims to obtain the operating system information of target hosts to make preparations for future attacks. In this paper, a fingerprint hopping method (FPH) is proposed based on software-defined networks to defend against fingerprinting attacks. FPH introduces the idea of moving target defense to show a hopping fingerprint toward the fingerprinting attackers. The interaction of the fingerprinting attack and its defense is modeled as a signal game, and the equilibriums of the game are analyzed to develop an optimal defense strategy. Experiments show that FPH can resist fingerprinting attacks effectively.

Code obfuscation is intended to thwart reverse engineering by making programmes hard to understand. Call chains collected by stack tracing can be used to understand the behaviour of programmes. To hinder reverse analysis of stack tracing, a binary code obfuscation method based on random obfuscated table and hash coding is proposed. Random obfuscated table is used to map call addresses while call and ret instructions are executing. Hash coding and random value can be used to encode and decode the data of stack frames in the run‐time programmes. Experiment and analysis show that the obfuscation can effectively impede stack trace analysis and increase the cost of reverse analysis for programmes.

In this paper, an authentication watermark algorithm for JPEG images is proposed, which is basing on the current watermark algorithm proposing and realizing a counterfeiting attack for the current watermarking algorithm security. In order to reduce the miss alarm caused by the mode of embedding watermark, in this algorithm the watermark embedded coefficients are as a factor of the watermark generation, and embedding watermark information by adopting the lowest bit substitute, so as to resist the counterfeiting attack effectively and improve the security of the current algorithm. The theoretical analysis and the realization show that the watermark algorithm presented by this paper has a lower miss alarm probability compared with the current algorithm, and further more the algorithm security.

This paper explores two multiclass steganalysis schemes to recognize stego algorithms in use. First of all, Xuan's universal steganalysis is improved to distinguish cover and stego images by the means of applying Bhattacharyya distance to select the most important features. Then, more attentions are paid to design two schemes to recognize stego algorithms with respect to accuracy, reliability and the decision-making cost. Experimental works show that the proposed schemes have satisfactory performance on Jpeg steganography like Jsteg, F5, Outguess and MB2.