The 18th International Conference on Mobility, Sensing and Networking (MSN 2022)
1st International Workshop on Cryptographic Security and Information Hiding Technology for IoT System
Session CSIHTIS
CSIHTIS
Conference
8:30 AM
—
10:00 AM HKT
Local
Dec 13 Tue, 7:30 PM
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9:00 PM EST
Semantic Image Synthesis via Location Aware Generative Adversarial Network
Jiawei Xu, Rui Liu, Jing Dong, Pengfei Yi, Wanshu Fan, Dongsheng Zhou
2
Semantic image synthesis aims to synthesize photorealistic images through the given semantic segmentation masks. Most existing models use conditional batch normalization (CBN) to regulate normalization activation by spatially varying modulation parameters. It can prevent semantic information from being eliminated during normalization. But the modulation parameters in CBN lack location constraint, resulting in the lack of structural information in the synthetic image. And CBN is highly dependent on the batch size. To address these limitations, we propose location aware conditional group normalization (LACGN) and construct a location aware generative adversarial network (LAGAN) based on this method. LACGN can learn spatial location aware information in a weakly supervised manner that relies on the current image synthesis process to guide transformations spatially. It allows the synthetic image to have more structural information and detailed features. At the same time, group normalization(GN) replace the traditional BN to eliminate the dependence on batch size. Extensive experiments show that LAGAN is better than other methods.
Low-Complexity Code Clone Detection using Graph-based Neural Networks
Hu Liu, Hui Zhao, Changhao Han, Lu Hou
0
Code clone detection is of great significance for intellectual property protection and software maintenance. Deep learning has been applied in some research and achieved better performance than traditional methods. To adapt to more application scenarios and improve the detection efficiency, this paper proposes a low-complex code clone detection with the graphbased neural network. As the input of the neural network, code features are represented by abstract syntax trees (ASTs), in which the redundant edges are removed. The operation of pruning avoids interference in the message passing of the network and reduces the size of the graph. Then, the graph pairs for the code clone detection are sent into the message passing neural networks (MPNN). In addition, the gated recurrent unit (GRU) is used to learn the information between graph pairs to avoid the operation of Graph mapping. After multiple iterations, the attention mechanism is used to read out the graph vector, and the cosine similarity is calculated on the graph vector to obtain the code similarity. Through the experiments on two datasets, the results show that the proposed clone detection scheme removes about 20% of the redundant edges and reduces 25% of model weights, 16% of multiply-accumulate operations (MACs). In the end, the proposed method effectively reduces the training time of graph neural network while presenting a similar performance to the baseline network.
Publishing Weighted Graph with Node Differential Privacy
Xuebin Ma, Ganghong Liu, Aixin Lin
1
At present, how to protect user privacy and security while publishing user data has become an increasingly important problem. Differential privacy is mainly divided into two directions in graph data publishing. One is to publish the statistical characteristics of the graph that meets the differential privacy, and the other is to publish the synthesis graph that meets the differential privacy. This paper proposes a weighted graph publishing method based on node difference privacy. First, this paper proposes a projection method that constrains the degree of nodes and the number of triangles and reduces the increase in noise by reducing the sensitivity. Afterward, select appropriate statistical characteristics of the weighted graph to form node attributes as the parameters of the syn-thesis weighted graph. The next part proposes a graph publishing method based on node attributes and weights. This method synthesizes the initial graph according to the degree in the node attribute. It then adds or deletes the edges of the initial graph according to the number of triangles in the node attribute to obtain the final synthesis graph. Finally, this paper verifies the weighted graph publishing method proposed on three data sets. The results show that the method proposed in this paper satisfies the different privacy conditions of nodes while maintaining certain utility.
SSA and BPNN based Efficient Situation Prediction Model for Cyber Security
Minglong Cheng, Guoqing Jia, Weidong Fang, Zhiwei Gao, Wuxiong Zhang
1
Establishing an effective situation prediction model for cyber security can know the active situation of future network malicious events in advance, which plays a vital role in cyber security protection. However, traditional models cannot achieve sufficient prediction accuracy when predicting cyber situations. To solve this problem, the initial location information of the sparrow population is optimized, and a sparrow search algorithm based on the Tent map is proposed. Then, the BP neural network is optimized using the improved sparrow search algorithm. Finally, a situation prediction model based on the sparrow search algorithm and BP neural network is proposed, namely T-SSA-BPNN. The simulation results show that the convergence speed and global search ability of the prediction model are improved. It can effectively predict the network security situation with high accuracy.
IA-DD: An SDN Topological Poisoning Attack Defense Scheme Based on Blockchain
Bin Gu, Xingwei Wang, Kaiqi Yang, Yu Wang, and Qiang He
1
Software defined networking (SDN) have the advantages of centralized control, global visibility, and programmability, but these features also bring new security issues, such as Topological Poisoning Attack (TPA), where attackers can attack topology discovery services by stealing host locations or forging link information. Considering the three levels of identity, data package and path, this paper designs a chain authentication defense scheme. The scheme includes authentication mechanism, transaction information storage mechanism, source IP authentication mechanism and smart contract notification mechanism. The received packets are authenticated by digital signature algorithm, and the trusted identity and location information are stored securely. At the same time, an improved block storage structure is designed to avoid data redundancy, and malicious information is processed by smart contract notification and stream rule installation. The experimental results show that the defense scheme designed in this paper can effectively defend against TPA attacks. Compared with the benchmark mechanism, the deployment of this scheme has less impact on controller performance and less impact on the delay of topology discovery in SDN.
Low-power Robustness Learning Framework for Adversarial Attack on Edges
Bingbing Song, Haiyang Chen, Jiashun Suo, Wei Zhou
0
Recent works on adversarial attacks uncover the intrinsic vulnerability of neural networks, which reveal a critical issue that the neural networks are easily misled by adversarial attacks. As the development of edge computing, more and more real-time tasks are deployed on edge devices. The safety of these neural network-based applications is threatened by adversarial attack. Therefore, the defense technique against adversarial attack has very important application value for edges. Especially, the defense technique should consider the deployment condition on edges, such as low power and low time consumption. Unfortunately, until now, very limited research considers the security problem under adversarial attack on edges. In this paper, we propose a low-power robust learning framework to deal with the adversarial attacks at resource-constrained edge devices. In this framework, we make a rough categorization of approaches on defending against adversarial attacks, and reveal how this edge device-based framework can be used to resist adversarial attacks. Furthermore, we propose a staged ensemble defense strategy in the framework, which achieves better defensive performance than a single defense algorithm. To verify our framework on real application, we build a Drone Search and Rescue System (DSRS) which is employed to examine the performance of the proposed framework. The results indicate that our framework achieves outstanding performance in all aspects, such as robustness, time and power consumption. Multiple evaluations of the low-power robust learning framework provide the advice that help to choose the optimal security configuration on power-constrained and performance-expected environments.
Session Chair
Xiaoliang Wang, Hunan University of Science and Technology, China
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