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TIBS: A Deep-Learning Model for Network Intrusion Detection for SDN Environments

TIBS: A Deep-Learning Model for Network Intrusion Detection ...

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International Conference on computers, Communications, and Systems (ICCCS)

作者： Yilin Zhang Xiaoming Wu Huomin Dong Key Laboratory of Computing Power Network and Information Security Shandong Computer Science Center (National Supercomputer Center in Jinan) Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Software-Defined Networking (SDN) is a modern network strategy that replaces traditional network structures. Its management efficiency and convenience are significantly higher than traditional networks. However, it should be pointed out that the centralized control mechanism adopted in SDN may increase the risk of single points of failure, making it more susceptible to different types of network attacks, such as Distributed Denial of Service (DDoS), DoS, PortScan and Brute Force etc. The identification and defense of these attacks are complex and require effective preventive measures. Network intrusion detection technology increasingly plays an irreplaceable role in network security. Therefore, this paper proposes an intrusion detection method for SDN environments. The system covers multiple stages, each with unique functions and roles. First, it is proposes an imbalanced data oversampling method based on improved Auxiliary Classifier Generative Adversarial networks (ACGAN) to solve the problem of data imbalance. At the same time, an intrusion detection model TIBS based on Transformer and Inception-BiGRU-SA network is proposed. First, the Encoder part of Transformer is used to capture the connection globally and perform preliminary feature extraction on the input data. Secondly, the improved Inception module is used to extract multi-scale features. Extract, use the self-attention (SA) to weight the extracted spatial features of different scales, use bidirectional gated recurrent unit(BiGRU) to improve the model's ability to extract temporal features, and use the Softmax activation function for final classification. The experiment uses the CIC-IDS-2017 dataset and CIC-DDoS-2019 dataset. The experimental results show that the proposed method has high accuracy and robustness in intrusion detection tasks, and can better identify various types of attack behaviors than traditional methods. And provides more accurate prediction results. Therefore, this method has potential

关键词： Accuracy Computational modeling Network intrusion detection Network security Feature extraction Transformers Data models

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The Task Collaborative Migration Method for Marine IoT Based on Edge Computing-RESTful Architecture

The Task Collaborative Migration Method for Marine IoT Based...

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International Conference on Artificial Intelligence in information and Communication (ICAIIC)

作者： Yifan Hu Fuqiang Wang School of information and control engineering Qingdao University of Technology Qingdao China Shandong Computer Science Center and Shandong Provincial Key Laboratory of Computer Networks Qilu University of Technology (Shandong Academy of Sciences) Jinan China

Marine Internet of Things (Marine IoT) has garnered increasing interest in monitoring oceanic environments. But establishing a Marine IoT system for observing and processing marine environmental data presents various challenges. These include limited underwater communication bandwidth, and uneven distribution of tasks among data-sensing devices. This paper proposes an Edge Computing-RESTful (ECR) architecture to efficiently integrate and process the substantial volume of data gathered through marine observations. 1) The ECR method is introduced for task collaborative migration, aiming to schedule and manage the tasks within the Marine IoT system. 2) Through unified scheduling and management of tasks, the edge gateway can facilitate migration, scheduling, and interaction of task loads among various edge sensing devices. The proposed dynamic and adaptive approach matches computing tasks with edge sensing devices, minimizing energy consumption and migration delay within the Marine IoT system. Simulation results validate the superiority of the proposed method for Marine IoT, showcasing improved performance metrics such as reduced time delays and lower energy consumption during task collaborative migration.

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Collaborative Computation Model Based on Dependency Types and Constituent Trees for Aspect-Based Sentiment Analysis

Collaborative Computation Model Based on Dependency Types an...

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International Conference on computer Supported Cooperative Work in Design

作者： Jia Yi Xiaoming Wu Yunfeng Dong Bei Qi Xiangzhi Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Aspect-based sentiment analysis(ABSA) is a fine-grained sentiment classification task that aims to identify the sentiment polarity of specific aspects in a sentence. Graph convolutional networks(GCN) have recently been widely applied for modeling the associations between aspects and opinion words. However, most studies treat the relationships between all words in the graph equally through dependency analysis, without considering their dependency types, which may lead to the inability to distinguish important relationships. On the other hand, dependency trees can only reveal relationships between words and cannot simulate complex sentence relationships, such as conditional, parallel, and contrast relationships, which are crucial for capturing more accurate sentiment relationships for different aspects. To address these challenges, this paper proposes a Collaborative computation model based on Dependency Types and Constituent trees(CDTC) for aspect-based sentiment analysis. Specifically, collaborative computation of dependency type graph convolutional networks and syntax encoders enhances task efficiency. Utilizing dependency types helps distinguish important relationships, while the phrase segmentation and hierarchical structure of constituent trees provide richer syntactic information. Extensive experiments conducted on three datasets demonstrate that our proposed CDTC model achieves state-of-the-art performance.

关键词： Analytical models Sentiment analysis Graph convolutional networks Federated learning Computational modeling Semantics Collaboration

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Semantic Reconstruction Guided Missing Cross-modal Hashing

Semantic Reconstruction Guided Missing Cross-modal Hashing

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International Joint Conference on Neural networks (IJCNN)

作者： Yafang Li Chaoqun Zheng Ruifan Zuo Wenpeng Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Cross-modal hashing is favored in the field of large-scale cross-modal retrieval for its fast query and low storage cost. Existing cross-modal hashing methods are typically based on an idealized assumption that data from different modalities are fully and completely paired. However, in practical scenarios, the occurrence of missing modal data is common due to technical difficulties, hardware failures, and challenges in data collection, contradicting the aforementioned assumption. In this paper, we propose an innovative unsupervised cross-modal hashing frame-work, named Semantic Reconstruction Guided Missing Hashing (SRGMH). Specifically, we utilize Dual-Variational Autoencoders (D-VAEs) to map features of different modalities into a shared low-dimensional latent representation, better bridging the gaps between modalities. Notably, we generate pseudo-representations of other modalities corresponding to the missing modality, effectively solving the problem of missing modal data. Moreover, we construct a refined adjacency similarity matrix to align the latent representations of different modalities, enhancing the quality of completed data and ensuring semantic consistency across modalities. Finally, we embed the adjacency similarity matrices in a shared latent representation space to jointly learn hash functions and hash codes, which enriches the semantic content and maintains structural similarity between hash functions and codes. Experiments on three real-world datasets demonstrate the superior performance of the proposed method on both retrieval accuracy and efficiency.

关键词： Hash functions Codes Costs Accuracy Semantics Neural networks Data collection

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Android Malware Detection Method Based on Graph Convolutional networks

Android Malware Detection Method Based on Graph Convolutiona...

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Artificial Intelligence and Power Systems (AIPS), International Conference on

作者： Qingling Xu Shumian Yang Lijuan Xu Dawei Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) (Shandong Academy of Sciences) Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350392210

ISBN: (纸本)9798350392227

Facing the serious security threats posed by Android malware amidst the rapid development of mobile internet, there is an urgent need to develop more accurate and efficient detection technologies. This paper introduces an innovative Android malware detection solution based on graph neural networks(GCN). The solution centers around function call graphs as core features, incorporating sensitive API identification and node importance analysis to streamline redundant information and reinforce key feature representation. Leveraging graph neural network models, complex call graphs are efficiently encoded into low-dimensional features, revealing the deep structure of malicious code. This method not only significantly improves detection accuracy to 99% but also drastically reduces detection time while ensuring performance, demonstrating its strong advantage in Android malware detection and classification tasks.

关键词： Training Accuracy Costs Graph convolutional networks Biological system modeling Feature extraction Malware Power systems Complexity theory Security

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In-band Network-Wide Telemetry for Topology-Varying LEO Satellite networks

In-band Network-Wide Telemetry for Topology-Varying LEO Sate...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yan Zhang Tian Pan Qiang Fu Yan Zheng Xinyu Feng Jiang Liu Haipeng Yao Tao Huang Purple Mountain Laboratories Nanjing China Beijing University of Posts and Telecommunications Beijing China Ministry of Education Key Laboratory of Computer Network and Information Integration (Southeast University) China RMIT University Melbourne Australia

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Driven by technological advances and new business models, we have seen a renewed interest in LEO satellite constellations. The deployment of large-scale LEO satellite networks is becoming a reality. The network topology changes periodically, as satellites orbit the Earth. This imposes a great challenge to network monitoring. Meanwhile, as a new network monitoring method, In-band Network Telemetry (INT) can provide per-hop granular telemetry metadata, needed to tackle the mobile nature of LEO satellite constellations. Given this, we apply INT to LEO satellite networks for real-time fine-grained monitoring. We propose a path planning solution to identify the paths for network-wide telemetry and the paths for disseminating the telemetry data to the ground facilities. By taking advantage of the predictable satellite trajectories and topology variations, the path planning solution is designed to achieve network-wide coverage and minimize telemetry overhead. We take the LEO48 constellation as an example to visually show the detailed paths of the monitoring scheme. We conduct experiments on different sizes of networks to evaluate the original path planning algorithm and the improved balanced algorithm in this paper, demonstrating the timeliness and balance of the telemetry solution.

关键词： Satellite constellations Visualization Satellites Network topology Low earth orbit satellites Prediction algorithms Real-time systems Trajectory Telemetry Monitoring

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I Can See Your Secrets, A Way to Recognize User Pin Codes by Surveillance Camera 10

I Can See Your Secrets, A Way to Recognize User Pin Codes by...

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10th IEEE Smart World Congress, SWC 2024

作者： Guo, Zhenyang Cao, Jin Zan, Xiaoyong Yang, Jiawei Li, Ang Niu, Ben Sun, Zhe Yin, Lihua Li, Hui Xidian University School of Cyber Engineering Xi'an China The State Key Laboratory of Integrated Services Networks Xi'an China ByteDance Ltd Beijing China University of Maryland Department of Electrical & Computer Engineering College Park United States Institute of Information Engineering Chinese Academy of Sciences Beijing China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China

ISBN: (纸本)9798331520861

Surveillance cameras are currently widely used in public and private places to enhance area security and reduce crime rates. However, the extensive use of surveillance cameras also poses significant threats, particularly concerning the leakage of users' private information. Surveillance cameras are often installed in the corners of areas such as hotel front desks, supermarket checkout areas, or ticket offices to observe user behavior, which can lead to shoulder-surfing attacks. In these public or private settings, when users input their personal identification number (PIN) for unlocking or payment, this process may be intentionally or unintentionally exposed and recorded. If the PIN is compromised, it can result in severe privacy breaches and financial losses for users. Existing work primarily focuses on dedicated handheld recording devices such as cameras and smartphones, lacking discussion on public surveillance cameras. In this paper, we first present an attack model for such scenarios. Based on this model, we design and implement an automated attack system for PIN recognition. Experimental results demonstrate that our proposed attack model is both feasible and effective, achieving 90 % accuracy with our system. Finally, we urge users, smartphone manufacturers, and surveillance camera manufacturers to be aware of this attack vector to prevent further harm. © 2024 IEEE.

关键词： Smartphones

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Tourist Attraction Recommendation System Based on Django and Collaborative Filtering 1st

Tourist Attraction Recommendation System Based on Django and...

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1st International Conference on Applied Intelligence, ICAI 2023

作者： Jiang, Yufeng Zhang, Yushu Li, Zhujun Yu, Wendong Wei, Hongwei Yuan, Lin Shandong Jinan250353 China Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250353 China School of Art Nantong University Jiangsu Nantong226019 China Jinan Springs Patent and Trademark Office Shandong Jinan250014 China Shandong Tianyi Information Technology Co. Ltd. Jinan Shandong250101 China

ISBN: (纸本)9789819708260

Recently, with the development of social and economic levels and people's pursuit of quality of life, tourism has become the first choice for more and more people. However, the traditional travel agency-based tourism method has also begun to expose some problems. However, with the development of machine learning and big data technology, personalized recommendation systems have become more and more important, which also provides us with new ideas to solve this problem. This paper therefore explores the application of spatial clustering algorithm and collaborative filtering algorithm in tourist attraction recommendation. This algorithm can not only mine the geographical location information of tourist attractions, but also analyze and study the data connections between users and attractions, and use these data to make personalized recommendations, providing more considerate and convenient services for travel planning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Collaborative filtering

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Knowledge Distillation Using Global Fusion and Feature Restoration for Industrial Defect Detectors

Knowledge Distillation Using Global Fusion and Feature Resto...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zhengqian Feng Xiyao Yue Wang Li Mingle Zhou Delong Han Gang Li SHANDONG SCICOM Information and Economy Research Institute CO. . Ltd Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Deep learning technology has been widely applied in industrial quality inspection tasks to improve the accuracy of defect detection, object recognition, and classification. However, in the task of object detection, both feature-based and regression-based traditional knowledge distillation methods impose overly strict constraints on the student model. To address the above issues, this article proposes Global Fusion and Feature Restoration Knowledge Distillation(FRD). FRD integrates contextual information into the channel through a Global Fusion Module (GFM), and further utilizes attention mechanisms to adaptively focus on the distillation region after separating the foreground background. FRD also uses a Mask Feature Restoration (MFR) to mask and restore a portion of student features, improving the learnability of the model. At the same time, FRD adopts a comprehensive approach of multiple loss superposition constraints, rather than simply using MSE losses to imitate the features of teachers. Experiments have shown that FRD can effectively improve model performance in industrial defect detection tasks. On the aluminum surface defect dataset, FRD increased the mAP index of RetinaNet-Res50 from 57.7% to 61.7%. We also confirmed the effectiveness of FRD for general object detection on the Coco dataset. On a randomly selected COCO dataset containing 4000 images, FRD increased the mAP metric of RetinaNet-Res50 from 21.5% to 43.6%.

关键词： Measurement Deep learning Adaptation models Object detection Inspection Feature extraction Image restoration Object recognition Cybernetics Defect detection

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Multi-Signal Fusion of Social Diffusion Graph with Bi-Directional Semantic Consistency

Multi-Signal Fusion of Social Diffusion Graph with Bi-Direct...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Huacheng Li Chunhe Xia Tianbo Wang Wanshuang Lin Changnan Jiang Chen Chen Yuan Zhao Key Laboratory of Beijing Network Technology Beihang University Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University School of Cyber Science and Technology Beihang University Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai

Devising diffusion graph to learn user representations is a crucial step in studying information propagation prediction. However, previous works mainly focused on structural and temporal features. To better incorporate content features, we introduce the Backward Decomposition and Forward Preservation mechanisms. The former involves decomposing content features for initializing node signals in the diffusion graph, thus fusing user features with content features. The latter aims to maintain node features generated by graph encoder consistent with the original content features. A series of experiments demonstrate that our model outperforms state-of-the-art models, and both mechanisms significantly enhance the prediction performance. Furthermore, our methods enables the features generated by the diffusion graph to more effectively incorporate features from various semantic spaces, whether encoded by language models or generated by graph embedding algorithms.

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