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IP-Pealing: A Robust Network Flow Watermarking Method Based on IP Packet Sequence

Chinese Journal of Electronics 2024年第3期33卷 694-707页

作者： Wangxin FENG Xiangyang LUO Tengyao LI Chunfang YANG State Key Laboratory of Mathematical Engineering and Advanced Computing Key Laboratory of Cyberspace Situation Awareness of Henan Province

Network flow watermarking(NFW) is usually used for flow *** actively modulating some features of the carrier traffic,NFW can establish the correspondence between different network *** the face of strict demands of network traffic tracing,current watermarking methods cannot work efficiently due to the dependence on specific protocols,demand for large quantities of packets,weakness on resisting network channel interferences and so *** this end,we propose a robust network flow watermarking method based on IP packet sequence,called as *** is designed to utilize the packet sequence as watermark carrier with IP identification field which is insensitive to time jitter and suitable for all IP based *** enhance the robustness against packet loss and packet reordering,the detection sequence set is constructed in terms of the variation range of packet sequence,correcting the possible errors caused by the network *** improve the detection accuracy,the long watermark information is divided into several short sequences to embed in turn and assembled during *** a large number of experiments on the Internet,the overall detection rate and accuracy of IP-Pealing reach 99.91% and 99.42%*** comparison with the classical network flow watermarking methods,such as PROFW,IBW,ICBW,WBIPD and SBTT,the accuracy of IP-Pealing is increased by 13.70% to 54.00%.

关键词： Network flow watermarking IP packet sequence Detection sequence set Fast detection Robustness

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IP2vec:an IP node representation model for IP geolocation

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Frontiers of Computer Science 2024年第6期18卷 189-204页

作者： Fan ZHANG Meijuan YIN Fenlin LIU Xiangyang LUO Shuodi ZU State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001China Key Laboratory of Cyberspace Situation Awareness of Henan Province Zhengzhou 450001China

IP geolocation is essential for the territorial analysis of sensitive network entities,location-based services(LBS)and network fraud *** has important theoretical significance and application ***-based IP geolocation is a hot research ***,the existing IP geolocation algorithms cannot effectively utilize the distance characteristics of the delay,and the nodes’connection relation,resulting in high geolocation *** is challenging to obtain the mapping between delay,nodes’connection relation,and geographical *** on the idea of network representation learning,we propose a representation learning model for IP nodes(IP2vec for short)and apply it to street-level IP ***2vec model vectorizes nodes according to the connection relation and delay between nodes so that the IP vectors can reflect the distance and topological proximity between IP *** steps of the street-level IP geolocation algorithm based on IP2vec model are as follows:Firstly,we measure landmarks and target IP to obtain delay and path information to construct the network ***,we use the IP2vec model to obtain the IP vectors from the network ***,we train a neural network to fit the mapping relation between vectors and locations of ***,the vector of target IP is fed into the neural network to obtain the geographical location of target *** algorithm can accurately infer geographical locations of target IPs based on delay and topological proximity embedded in the IP *** cross-validation experimental results on 10023 target IPs in New York,Beijing,Hong Kong,and Zhengzhou demonstrate that the proposed algorithm can achieve street-level *** with the existing algorithms such as Hop-Hot,IP-geolocater and SLG,the mean geolocation error of the proposed algorithm is reduced by 33%,39%,and 51%,respectively.

关键词： IP geolocation network measurement node embedding

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Integrating Neighborhood Geographic Distribution and Social Structure Influence for Social Media User Geolocation

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2513-2532页

作者： Meng Zhang Xiangyang Luo Ningbo Huang Key Laboratory of Cyberspace Situation Awareness of Henan Province Zhengzhou450001China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China

Geolocating social media users aims to discover the real geographical locations of users from their publicly available data,which can support online location-based applications such as disaster alerts and local content *** relationship-based methods represent a classical approach for geolocating social ***,geographically proximate relationships are sparse and challenging to discern within social networks,thereby affecting the accuracy of user *** address this challenge,we propose user geolocation methods that integrate neighborhood geographical distribution and social structure influence(NGSI)to improve geolocation ***,we propose a method for evaluating the homophily of locations based on the k-order neighbor-hood geographic distribution(k-NGD)similarity among *** are notable differences in the distribution of k-NGD similarity between location-proximate and non-location-proximate *** this distinction,we filter out non-location-proximate social relationships to enhance location homophily in the social *** better utilize the location-proximate relationships in social networks,we propose a graph neural network algorithm based on the social structure *** algorithm enables us to perform a weighted aggregation of the information of users’multi-hop neighborhood,thereby mitigating the over-smoothing problem of user features and improving user geolocation *** results on real social media dataset demonstrate that the neighborhood geographical distribution similarity metric can effectively filter out non-location-proximate social ***,compared with 7 existing social relationship-based user positioning methods,our proposed method can achieve multi-granularity user geolocation and improve the accuracy by 4.84%to 13.28%.

关键词： User geolocation social media neighborhood geographic distribution structure influence

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Social Media User Geolocation Based on Large Language Models 2nd

Social Media User Geolocation Based on Large Language Model...

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2nd International Conference on Data Security and Privacy Protection, DSPP 2024

作者： Zhang, Meng Luo, Xiangyang Huang, Ningbo Key Laboratory of Cyberspace Situation Awareness of Henan Province Henan Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou China

ISBN: (纸本)9789819785391

In the digital age, social media platforms have amassed a wealth of user-generated content, which contains valuable geographic information. However, the irregularities and noise in user-generated text, have led to suboptimal performance in traditional text-based user geolocation methods. We propose a unsupervised framework for user geolocation based on Large Language Models (LLMs), which utilizes the LLMs’ powerful text processing abilities to geolocate users based on user-generated text unsupervisedly. Firstly, preprocess the text using regularization rules and LLM to denoise and normalize user-generated text, thus enhancing data quality. Subsequently, appropriate prompts are designed to guide the knowledgeable LLM in understanding the user text’s geolocating mechanism, thereby profiling users. To refine user geolocation accuracy, five independent positioning iterations are conducted, with the most frequent occurrence identified as the final user location. Through a series of experiments, we have demonstrated the potential of utilizing large language models for processing noisy text and the effectiveness of geolocating users in an unsupervised setting. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Large language model Social media Unsupervised user geolocation User-generated text

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An anonymous authentication and secure data transmission scheme for the Internet of Things based on blockchain

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Frontiers of Computer Science 2024年第3期18卷 183-197页

作者： Xingxing CHEN Qingfeng CHENG Weidong YANG Xiangyang LUO State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Institute of Information Science and TechnologyZhengzhou 450001China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of TechnologyZhengzhou 450001China Henan Province Key Laboratory of Cyberspace Situation Awareness Zhengzhou Institute of Information Science and TechnologyZhengzhou 450001China

With the widespread use of network infrastructures such as 5G and low-power wide-area networks,a large number of the Internet of Things(IoT)device nodes are connected to the network,generating massive amounts of ***,it is a great challenge to achieve anonymous authentication of IoT nodes and secure data *** present,blockchain technology is widely used in authentication and s data storage due to its decentralization and ***,Fan et *** a secure and efficient blockchain-based IoT authentication and data sharing *** studied it as one of the state-of-the-art protocols and found that this scheme does not consider the resistance to ephemeral secret compromise attacks and the anonymity of IoT *** overcome these security flaws,this paper proposes an enhanced authentication and data transmission scheme,which is verified by formal security proofs and informal security ***,Scyther is applied to prove the security of the proposed ***,it is demonstrated that the proposed scheme achieves better performance in terms of communication and computational cost compared to other related schemes.

关键词： Internet of Things blockchain authentication data transmission

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LENSER: A Channel State Information Based Indoor Localization Scheme for Malicious Devices 20

LENSER: A Channel State Information Based Indoor Localizatio...

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20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Ma, Yongqiang Luo, Xiangyang Li, Ruixiang Du, Shaoyong Liu, Wenyan State Key Laboratory of Mathematical Engineering and Advanced Computing Key Laboratory of Cyberspace Situation Awareness of Henan Province Zhengzhou China

ISBN: (纸本)9798350324334

Rapid and accurate localization of illicit micro-recorders, cameras, and other wireless network devices is of great significance for ensuring public privacy and security. However, existing methods are susceptible to environmental noise, have low localization accuracy, high localization time overhead, and require extensive manual involvement. To address these issues, we propose LENSER, a channel state information based indoor localization scheme for illicit wireless network devices. First, we set detection points to capture signal amplitude distribution changes during the antenna array rotation process, thus determining whether there is a line-of-sight propagation relationship between the detection point and the malicious device. Next, we identify the room containing the malicious device based on the line-of-sight propagation relationship and calibrate the measured position and estimate the direction of the malicious device using existing methods within the room. Finally, we establish a linear space based on the direction of the malicious device and propose an iterative comparison method based on received signal strength indicator to localize malicious devices accessing public Wi-Fi networks within the room. We conducted 1200 experiments in four buildings with different layouts and building materials. The results show that LENSER's average localization time is 12.5 minutes, and the average localization accuracy is 26.4 centimeters, an improvement of 11.1% to 86.1% in average localization accuracy and a reduction of 11.1% to 58.2% in average localization time compared to existing methods, such as Spotfi, Lumos, SCamF, SNOOPDOG, and Region Division. © 2023 IEEE.

关键词： Channel state information

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Resolving Anonymous Nodes Based on "Watermelon Patterns" Towards Constructing Router-Level Topology in Mesh Network 29th

Resolving Anonymous Nodes Based on "Watermelon Patterns" Tow...

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29th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2023

作者： Li, Ruixiang Feng, Xinzhu Liu, Chong Yuan, Fuxiang Ding, Shichang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Key Laboratory of Cyberspace Situation Awareness of Henan Province Zhengzhou China Sichuan University Chengdu China

ISBN: (纸本)9783031425141

Router-level topology, portraying the connection relationship between network devices, is important for discovering key devices in network. In actual networks, there are some anonymous routers, which do not respond traceroute due to network congestion, firewall security settings, router response rate limitations, and other factors. As these anonymous routers are difficult to eliminate, it makes the constructed router-level topology huge in size. Currently, the time cost of finding key nodes is high. Resolving anonymous nodes for reducing the topology size is necessary to improve the efficiency in finding key node. Although anonymous route resolving methods have been available, they are difficult to handle two-hop anonymous route structures in mesh topologies. To this end, based on the four structures summarized in Graph Based Induction (GBI) method, we propose a method to resolve anonymous nodes based on "watermelon patterns". Combining with Kapar, an alias IP merging method, to build router-level topologies in mesh networks. We have conducted experiments in 8 cities, such as New York, Tokyo, Seoul and so on. The experimental results show that, our method can handle the local topology containing two-hop anonymous nodes. And using our method to further reduce the size of the router-level topology built using GBI and Kapar, which is conducive to improving the efficiency of discovering key nodes in the topology. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Topology

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Social User Geolocation Method Based on POI Location Feature Enhancement in Context 1

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30th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2024

作者： Liu, Yu Qiao, Yaqiong Liu, Yimin Du, Shaoyong State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Key Laboratory of Cyberspace Situation Awareness of Henan Province Zhengzhou China School of Information Engineering North China University of Water Resources and Electric Power Zhengzhou China

ISBN: (数字)9783031687754

ISBN: (纸本)9783031687747

The geolocation information in social media user generated text is one of the important bases for inferring users' location. The existing methods often rely on the mentioned Point of Interest (POI) and location Indicative word in user generated text as the inference basis, in consideration of the lack of consideration of semantic information and the inaccuracy or incompleteness of POI contribute to the inaccuracy of user geolocation based on text. To address these issues, this paper proposes Social User Geolocation Method based on POI Location Feature Enhancement in Context (UGLE). Firstly, the POI in user-generated text is removed by deep neural network. Then, through three geographical traits of POI: location orientation, word frequency and word length, the problem of ambiguous POI location orientation in user generated text is solved. Finally, by combining the semantic information of the words around POI, the user vector with enhanced location features is generated, which improves the geolocation accuracy of social users. On the Weibo dataset, the proposed Method is compared with four existing typical methods, PaQL, SeFG, MNB-PART, and MNB-LGR. The results show that the proposed method's accuracy in inferring province-level and city-level locations is 76.78% and 62.56%, respectively. These results are 6.81% and 6.66% higher than the existing best results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep neural networks

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Improving CoatNet for Spatial and JPEG Domain Steganalysis

Improving CoatNet for Spatial and JPEG Domain Steganalysis

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Hao Li Xiangyang Luo Yi Zhang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Advanced Computing and Key Laboratory of Cyberspace Situation Awareness of Henan Provinc Zhengzhou China

The covert communication technology represented by image steganography can realize the safe and reliable information transmission over an open network. Steganalysis is an important way to measure the security of steganography. The application of deep learning in the field of steganalysis has begun to emerge and achieved certain results. However, the existing steganalysis methods based on deep learning extract limited global features and the detection accuracy still needs to be improved. Therefore, this manuscript proposes an improving CoatNet for spatial and JPEG domain steganalysis (IMCoatNet). Firstly, the SKAttention structure is used to extract fine-grained features. Then, the convolution layer, Transformer layer and the pyramid squeezed attention is used to capture multi-scale spatial features. Finally, a weighted loss is designed to enhance the expression ability of the model. The results show that compared with the traditional Spatial Rich Model (SRM) method, the classical deep residual network for steganalysis (SRNet), and the latest siamese CNN for steganalysis (SiaStegNet), the detection accuracy of the proposed CoatNet method for WOW steganography is improved by 19.61%, 4.33% and 5.04%, respectively; The proposed IMCoatNet is also superior on JPEG domain detection.

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LENSER: A Channel State Information Based Indoor Localization Scheme for Malicious Devices

LENSER: A Channel State Information Based Indoor Localizatio...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Yongqiang Ma Xiangyang Luo Ruixiang Li Shaoyong Du Wenyan Liu Key Laboratory of Cyberspace Situation Awareness of Henan Province State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Rapid and accurate localization of illicit micro-recorders, cameras, and other wireless network devices is of great significance for ensuring public privacy and security. However, existing methods are susceptible to environmental noise, have low localization accuracy, high localization time overhead, and require extensive manual involvement. To address these issues, we propose LENSER, a channel state information based indoor localization scheme for illicit wireless network devices. First, we set detection points to capture signal amplitude distribution changes during the antenna array rotation process, thus determining whether there is a line-of-sight propagation relationship between the detection point and the malicious device. Next, we identify the room containing the malicious device based on the line-of-sight propagation relationship and calibrate the measured position and estimate the direction of the malicious device using existing methods within the room. Finally, we establish a linear space based on the direction of the malicious device and propose an iterative comparison method based on received signal strength indicator to localize malicious devices accessing public Wi-Fi networks within the room. We conducted 1200 experiments in four buildings with different layouts and building materials. The results show that LENSER’s average localization time is 12.5 minutes, and the average localization accuracy is 26.4 centimeters, an improvement of 11.1% to 86.1% in average localization accuracy and a reduction of 11.1% to 58.2% in average localization time compared to existing methods, such as Spotfi, Lumos, SCamF, SNOOPDOG, and Region Division.

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