Federated learning (FL) is widely used in Internet-of-Things (IoT) systems, but its distributed training process also exposes it to backdoor attacks. Existing studies mainly consider single-target or centralized multi-target settings, while coordinated distributed multi-target attacks remain underexplored. In practical IoT scenarios, one adversarial entity may control multiple distributed malicious clients and assign each client distinct triggers and target labels. Under this setting, existing distributed backdoor methods often fail to preserve the effectiveness of all backdoors because malicious updates conflict during aggregation. To address this issue, we propose a Distributed Multi-Target Backdoor Attack (DMBA) for FL. DMBA introduces a Backdoor Replay (BR) mechanism to reduce discrepancies among malicious gradients and a Channel-Frequency Composite Trigger (CFCT) strategy to improve trigger distinguishability and alleviate local interference. Experiments on multiple datasets show that DMBA ensures attack success rates above 80% for all implanted back-doors, whereas some baseline backdoors fall below 50% and may even approach 0.

Anchor link prediction enhances the effectiveness of digital forensics through the identification of multiple social network users. The current methods based on deep learning are characterized by both the exaggerated similarity between adjacent nodes in the same latent space and the variation in the feature spaces caused by semantics. A novel approach is developed to fuse the semantic features of different networks in this paper. The proposed method is divided into two stages. Firstly, representation learning pays more attention to the influence of uncertainty on the equivalence of node network structure, and introduces the difference between adjacent nodes from the latent space. Secondly, a joint representation learning framework trains and exchanges the parameters depending on known anchor links. The joint representation learning framework injects fused features into the representation learning processes of different networks. The combination of enhanced discrimination and cross-network feature fusion reduces the feature space differences caused by the semantics of different social networks. This paper conducts comprehensive experiments on social networks in the real world. The outcome shows that the proposed approach is more efficient and robust compared to the existing state-of-theart methods.

Anchor link prediction exacerbates the risk of privacy leakage via the de-anonymization in edge computing. The predictive effect of traditional unsupervised learning methods is too dependent on user attributes and supervised learning methods are sensitive to network structure noise. Anchor link prediction methods based on graph embedding are restricted by the sparsity of the observable anchor links which can be used for training. To facilitate the effectiveness and robustness of the anchor link prediction, we have proposed a novel method which reduces the restrictions on the observable anchor links used for training. The proposed method consists of two phases. First, graph embedding based on network structural roles is used to generate the latent feature space, reconciling the distinction and similarity between nodes. Second, the supervised learning for optimizing the Wasserstein distance which estimates the minimum amount of work to change one distribution into the other. The combination of the reconciled latent feature space and the estimate for the amount of change alleviates the restriction on observable overlapping parts. Extensive experiments on real-life social networks have demonstrated that the proposed method significantly outperforms the state-of-the-art methods in terms of both precision and robustness.

Federated learning is designed to train models in a distributed scheme while keeping the clients' data stored locally. The aggregation server only receives local models from clients and does not require clients to upload their local data, in which way it protects the clients' privacy. However, federated learning is vulnerable. The federated learning models are sensitive to poisoning attacks. Existing data poisoning attack methods assume that the attacker and the client have the same data distribution and data volume, which is unpractical. In this paper, we first propose a privacy inference-based poisoning data generation method, FLPDG. FLPDG changes the relationship between data and labels, and uses the data of benign clients to launch poisoning attacks. This method relies on the global model of an iterative update to obtain the data and labels of benign clients. Second, a privacy inference-based data poisoning attack model Poi_PDG is proposed. This model uses the FLPDG method to launch a data poisoning attack under conditions of insufficient original data volume of the attacker. Meanwhile, a defense method PDG_DF is proposed for Poi_PDG. It splits the image data into variance regions and utilizes GANs to hide the visual features of each image region. It controls the degree of feature hiding by setting different thresholds to keep the classification features of the image while ensuring the accuracy of the training model. Finally, several experiments are conducted to evaluate the proposed attack and defense methods, and the experimental results indicate the effectiveness of the methods.

Facing the explosive growth of data, the introduction of cloud computing in the Space-Air-Ground Integrated Internet of Things (SAGIIoT) can solve the problem of limited computing power of the terminals. At the same time, data security on the cloud is also a focus that cannot be ignored. Secure outsourcing computing is helpful in improving privacy preserving. Due to the wide applicability of$K $-means clustering, outsourcing computing for$K $-means has become a major research hotspot in industry and academia. Most of the existing work on outsourcing$K $-means clustering is based on homomorphic encryption, which has a high computational overhead due to the mathematical puzzles’ nature of homomorphic encryption. In addition, the high computational overhead of designing a verification algorithm based on homomorphic encryption is unacceptable. To address the above issues, we design a${K}$-means clustering outsourcing algorithm by sparse matrix transformation, which can verify the deceptive behavior of cloud while achieving high efficiency. In this article, we theoretically prove the accuracy, security, efficiency, and verifiability of the proposed algorithm. Extensive experiments indicate that our algorithm is efficient.

Anchor link prediction exacerbates the risk of privacy leakage via the de-anonymization of social network data. Embedding-based methods for anchor link prediction are limited by the excessive similarity of the associated nodes in a latent feature space and the variation between latent feature spaces caused by the semantics of different networks. In this article, we propose a novel method which reduces the impact of semantic discrepancies between different networks in the latent feature space. The proposed method consists of two phases. First, graph embedding focuses on the network structural roles of nodes and increases the distinction between the associated nodes in the embedding space. Second, a federated adversarial learning framework which performs graph embedding on each social network and an adversarial learning model on the server according to the observable anchor links is used to associate independent graph embedding approaches on different social networks. The combination of distinction enhancement and the association of graph embedding approaches alleviates variance between the latent feature spaces caused by the semantics of different social networks. Extensive experiments on real social networks demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of both precision and robustness.

Anchor link prediction across social networks plays an important role in multiple social network analysis. Traditional methods rely heavily on user privacy information or high-quality network topology information. These methods are not suitable for multiple social networks analysis in real-life. Deep learning methods based on graph embedding are restricted by the impact of the active privacy protection policy of users on the graph structure. In this paper, we propose a novel method which neutralizes the impact of users’ evasion strategies. First, graph embedding with conditional estimation analysis is used to obtain a robust embedding vector space. Secondly, cross-network features space for supervised learning is constructed via the constraints of cross-network feature collisions. The combination of robustness enhancement and cross-network feature collisions constraints eliminate the impact of evasion strategies. Extensive experiments on large-scale real-life social networks demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of precision, adaptability, and robustness for the scenarios with evasion strategies.

There is a new cache pollution attack in the information-centric network (ICN), which fills the router cache by sending a large number of requests for nonpopular content. This attack will severely reduce the router cache hit rate. Therefore, the detection of cache pollution attacks is also an urgent problem in the current information center network. In the existing research on the problem of cache pollution detection, most of the methods of manually setting the threshold are used for cache pollution detection. The accuracy of the detection result depends on the threshold setting, and the adaptability to different network environments is weak. In order to improve the accuracy of cache pollution detection and adaptability to different network environments, this paper proposes a detection algorithm based on gradient boost decision tree (GBDT), which can obtain cache pollution detection through model learning. Method. In feature selection, the algorithm uses two features based on node status and path information as model input, which improves the accuracy of the method. This paper proves the improvement of the detection accuracy of this method through comparative experiments.

As an innovative strategy, edge computing has been considered a viable option to address the limitations of cloud computing in supporting the Internet-of-Things applications. However, due to the instability of the network and the increase of the attack surfaces, the security in edge-assisted IoT needs to be better guaranteed. In this paper, we propose an intelligent intrusion detection mechanism, FedACNN, which completes the intrusion detection task by assisting the deep learning model CNN through the federated learning mechanism. In order to alleviate the communication delay limit of federal learning, we innovatively integrate the attention mechanism, and the FedACNN can achieve ideal accuracy with a 50% reduction of communication rounds.

In this issue, we consider the location-aware targeted influence blocking maximization (LTIBM) problem, which plays a very important role in viral marketing and rumor control. LTIBM aims to find a set of positive seeds in a given social network to block the influence propagation of negative seeds over the targeted nodes located in a given region and having a preference on a given topic set as much as possible. We devise a simulation-based greedy algorithm based on monotone and submodular characteristics of influence function under the homogeneous independent cascade model. To improve the efficiency of the greedy algorithm, we propose LTIBM-H, a heuristic algorithm based on QT-tree and maximum influence arborescence (MIA). Experimental results show that the proposed LTIBM-H algorithm can achieve matching the blocking effect to the greedy algorithm and often performs better in terms of effectiveness than other baseline algorithms, while LTIBM-H is four orders of magnitude faster than the greedy algorithm.

Human behavior data is the basis of behavior analysis, and usually we need to collect large quantities of data before analysis. Most existing data collection methods are labor intensive works in which the volunteers need to be asked to behave naturally under the monitoring of researchers. Identity identification can be used in passive data collection of human behavior analysis systems in big data. Previous researches show the sensing potential of WiFi signals in a device-free passive manner. It is confirmed that human's gait is unique from each other like fingerprint and iris. As a result, researchers start to explore the ability of WiFi in human identification. However, the identification accuracy of existing approaches is not satisfactory in practice. In this paper, we present Wii, a device-free WiFi-based Identity Identification approach utilizing human's gait based on Channel State Information (CSI) of WiFi signals. Principle Component Analysis (PCA) and low pass filter are applied to remove the noises in the signals. We then extract entities' gait features from both time and frequency domain. Based on these features, Wii realizes identity identification through a Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. It is implemented using commercial WiFi devices and evaluated in a typical indoor scenario. The results indicate that Wii achieves high identification accuracy with low computational cost and has the potential to work in human behavior analysis systems.

Intrusion detection plays a critical role in security of people's possessions. Approaches such as video-based, infrared-based, RFID, UWB, etc. can provide satisfying detection accuracy. However, they all require specialized hardware deployment and strict using conditions which hinder their wide deployment. Beyond communication, WLANs can also act as generalized sensor networks and there are several researches working on motion detection via WLAN due to its advantages in deployment flexibility, coverage, and cost efficiency. Nevertheless, they are unsuitable for intrusion detection as none of them can accurately detect human motion when the moving speed is very slow. This paper proposes SIED as an accurate method for Speed Independent device-free Entity Detection which is suitable for intrusion detection even when the entity's moving speed is very slow. The influence becomes much smaller when the entity is moving with a very slow speed. Previous methods have the limitations in that their performance downgrades sharply when the entity's moving speed is very slow. Recently, it has been shown that Channel State Information (CSI) at PHY layer of wireless network has the potential to detect moving entities more accurately. In this paper we leverage CSI of 802.11n wireless network and probability technique to detect entities of different moving speeds. SIED captures the variance of variances of amplitudes of each CSI subcarrier, and combines Hidden Markov Model (HMM) to make entity detection a probability problem. We implement SIED using commercial WiFi devices and evaluate our method using two typical testbeds and show that SIED can achieve an average detection accuracy of greater than 98% under different entity moving speed.