The 17th International Conference on Mobility, Sensing and Networking (MSN 2021)
3rd International Workshop on Network Meets Intelligent Computations
Session NMIC-S1
NMIC Session I
Conference
12:00 PM
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2:20 PM GMT
Local
Dec 13 Mon, 4:00 AM
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6:20 AM PST
A DDoS Protection Method based on Traffic Scheduling and Scrubbing in SDN
Yiwei Yu, Guang Cheng, Zihan Chen, Haoxuan Ding (Southeast University, China)
0
DDoS attacks have emerged as one of the most serious network security threats in 5G, IoT, multi-cloud, and other emerging technology scenarios. The bandwidth of DDoS attacks is increasing in the new scenario, but the current network structure and security devices are inflexible. We propose a DDoS protection method based on SDN multi-dimensional scheduling method and DDoS scrubbing policy, which not only plans the scheduling path, but also blocks and redirects different kinds of attack traffic that used dynamic residual bandwidth of links, the number of flow entries in OpenFlow switches, and scheduling path length. To flexibly protect against DDoS attacks, this method combines scheduling and protection means. The experimental results indicate that the scheduling is effective. The scheduling, path produced by this method outperforms ECMP and KSP approaches in throughput, packet loss rate, and jitter, and it can block L3/L4 attack traffic and redirect L7 attack traffic.
Adaptive Distributed Beacon Congestion Control with Machine Learning in VANETs
Mahboubeh Mohammadi (Iran University of Science and Technology, Iran), Ali Balador (RISE Research Institute of Sweden, Sweden), Zaloa Fernandez (Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Spain), Iñaki Val (Ikerlan Technology Research Centre, Spain)
0
Many Intelligent Transportation System (ITS) applications rely on communication between fixed ITS stations (roadside installations) and mobile ITS stations (vehicles) to provide traffic safety. In VANETs, the Control Channel (CCH) and Service Channels (SCHs) are applied to transmit the safety-related data. The CCH used in IEEE 802.11p standard for exchanging high-priority safety messages and control information and it can be easily congested by high-frequency periodic beacons under high density scenarios. Also, employing the Dedicated-Short Range Communication (DSRC) band of IEEE 802.11p standard can hardly satisfy the requirements of high-critical safety applications. Also, transmitting safety beacons at a constant rate regardless of considering the condition of the links leads to the lack of flexibility and medium resources to support meeting the reliability requirements of these applications. In this paper, we evaluate a beacon rate control method, which assigns a higher beacon rate to nodes based on link conditions, i.e. with more surrounding nodes and better conditions to disseminate beacons. On the other hand, as IEEE 802.11p Medium Access Control (MAC) layer does not perform well under high channel load, so in this paper, we use Self-organizing Time Division Multiple Access (STDMA) in our simulations as MAC layer protocol. The results of the simulations demonstrate the rate of beacon transmission/reception effectively improves, results in better resource utilization. Also, Packet Error Rate (PER) and Packet Inter-Reception time (PIR) decrease significantly which is crucial for safety applications.
AND: Effective Coupling of Accuracy, Novelty and Diversity in the Recommender System
Di Han (Guangdong University of Finance, China), Yifan Huang, Xiaotian Jing, Junmin Liu (Xi'an Jiaotong University, China)
0
At present, most of the research on recommender system (RS) based on artificial intelligence focus on the algorithm, but the evaluation metrics being an important process to evaluate the performance of RS are usually ignored. Specifically, independent evaluation metrics cannot effectively reflect the differences between algorithms, so how to effectively couple these evaluation metrics needs further improvement. In order to reflect the difference in RS performance, this paper proposes a rational evaluation framework for RS performance, named AND, which can reflect metrics of accuracy, novelty and diversity. Through comparative experiments, it is verified that the proposed framework, on the basis of the hypothetical model and the state-of-the-art algorithms, can effectively reflect the difference between the recommendation performance with similar seemingly accuracy between different algorithms.
Attention-Based Bicomponent Synchronous Graph Convolutional Network for Traffic Flow Prediction
Cheng Shen, Kai Han, Tianyuan Bi (University of Science and Technology of China, China)
0
Traffic flow prediction is of great importance in overcoming traffic congestion and accidents, which profoundly impacts people's lives and property. However, the traffic forecasting task is difficult due to the complex interactions and spatial-temporal characteristics. Previous studies usually focus on capturing the spatial correlations and temporal dependencies separately, meanwhile, off-the-shelf studies neglect the effect of explicit differential information. What's more, there is a lack of effective methods for capturing potential interactions. In this paper, we propose a novel model for traffic flow prediction, named as Attention-based Bicomponent Synchronous Graph Convolutional Network (ABSGCN). This model is able to capture the synchronous spatial-temporal information with a fused signal matrix and potential interactions by constructing a novel edge-wise graph, which can remedy the shortcomings in traditional approaches. Extensive experiments are implemented on two real-world datasets and the results demonstrate that our model outperforms other baselines by a margin.
Intelligent IDS Chaining for Network Attack Mitigation in SDN
Mikhail Zolotukhin, Pyry Kotilainen, Timo Hämäläinen (University of Jyväskylä, Finland)
0
Recently emerging software-defined networking allows for centralized control of the network behavior enabling quick reactions to security threats, granular traffic filtering, and dynamic security policies deployment making it the most promising solution for today's networking security challenges. Software-defined networking coupled with network function virtualization extends conventional security mechanisms such as authentication and authorization, traffic filtering and firewalls, encryption protocols and anomaly-based detection with traffic isolation, centralized visibility, dynamic flow control, host and routing obfuscation, and security network programmability. Virtualized security network functions may have different effects on security benefit and service quality, thus, their composition has a great impact on performance variance. In this study, we focus on solving the problem of optimal security function chaining with the help of reinforcement machine learning. In particular, we design an intelligent defense system as a reinforcement learning agent which observes the current network state and mitigates the threat by redirecting network traffic flows and reconfiguring virtual security appliances. Furthermore, we test the resulting system prototype against a couple of network attack classes using realistic network traffic datasets.
Session Chair
Lei Yang (South China University of Technology, China)
Session NMIC-S2
NMIC Session II
Conference
3:15 PM
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5:15 PM GMT
Local
Dec 13 Mon, 7:15 AM
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9:15 AM PST
Interference and Consultation in Virtual Public Space: The Practice of Intermedia Art in Metaverse
Rongman Hong (Guangzhou Academy of Fine Arts, China), Hao He (The Chinese University of Hong Kong, Shenzhen, China)
0
Intermedia art is an art form that spans many fields such as digital art, biological art, interactive machinery, artificial intelligence, etc. Because of its multi-domain collaborative creation, it has become an inevitable choice for intermedia art creators to spread in public spaces with more complicated social relations. The public spaces can be recognized as the ones in the physical world that involve the flesh of human bodies, as well as the ones in the virtual world that called “metaverse” in recent years. In this paper, based on the traditional intervention methods of intermedia art in physical public spaces, which are ”interference” and ”consultation”, we conclude the challenges when intermedia reveals its tendency in migrating from the physical world to the virtual world as the change of society background. After that, we discuss the past, present and the future of metaverse serving as a new form of public space for the expression of intermedia art by analyzing the data collected from the two mainstream metaverse platforms called “CryptoVoxels” and “Decentraland”.
Large-Area Human Behavior Recognition with Commercial Wi-Fi Devices
Tao Liu (The University of Aizu, Japan), Shengli Pan (Beijing University of Posts and Telecommunications, China), Peng Li (The University of Aizu, Japan)
0
Human behavior recognition which is the indispensable technology for Artificial Intelligence(AI) application like smart home and other practical applications, is very challenging as the optimal recognition generally is required to be non-invasive and easy to deploy. An increasing interest has been paid on the human behavior recognition with off-the-shelf Wi-Fi devices. However, most of existing works just limit their focus on the small-scale scene while human behavior recognition will be quite different in large areas for a larger number of antennas and correspondingly a more complex antenna layout. For example, if we want to build a complete behavior awareness system using the distributed Wi-Fi equipment of the entire building, though collecting and using all antennas' data is feasible maybe, the overhead concerns of computing and bandwidth resources, and the operation complexity will be hard to lessen in practice. In this paper, we first present analyses of the signal performances between different antenna pairs. Then closely following these analyses, we propose a novel scheme for the large-area human behavior recognition. Finally, we conduct extensive confirmatory experiments to verify the validity of our proposed scheme.
Reliable Routing and Scheduling in Time-Sensitive Networks
Hongtao Li, Hao Cheng, Lei Yang (South China University of Technology, China)
0
Time-Sensitive Networking (TSN) standards were proposed to deliver real-time data with deterministic delay. TSN realizes the deterministic delivery of time-sensitive traffic by establishing virtual channels with specific cycle intervals. However, The existing work does not consider the reliable delivery of timesensitive traffic. In addition,the existing work generally considers scheduling in the ideal environment, and cannot handle random events such as network jitter and packet loss. In the paper,we introduce path redundancy and seamless redundancy as the basis of reliability, and propose reliable routing and scheduling problems with objectives to achieve good network throughput and link load balancing. We propose a routing heuristic and a scheduling heuristic to generate redundant transmission paths and schedules for time-sensitive traffic, respectively. Further, we propose a joint optimization algorithm to optimize the feasible solutions produced by routing and scheduling. In particular, we improve the scheduling mechanism of TSN, so that the our scheduling algorithm can adapt to the random and dynamic events in real network. Evaluations were carried out in several test cases with a self-developed TSN testbed. The results show our approaches can efficiently achieve good network throughput and link load balancing while ensuring time-space reliability.
Seshat: Decentralizing Oral History Text Analysis
Lin Wang (Guangzhou Academy of Fine Arts, China), Lehao Lin (The Chinese University of Hong Kong, Shenzhen, China), Xiao Wu (White Matrix Inc., China), Rongman Hong (Guangzhou Academy of Fine Arts, China)
0
Text analysis tools are often utilized to store and analyze massive texts in modern oral history research. However, the state-of-the-art centralized text analysis systems are suffering from data synchronization, maintenance, and cross-platform compatibility issues in a stand-alone environment, while the server-based ones are struggling from the lack of commitment to long-term support and unforeseen security risks, e.g. data leakage and loss. In this work, Seshat, a decentralized oral history text analysis system, employs Inter-Planetary File System (IPFS) storage, blockchain, and web technologies to address these issues. With Seshat, the text processing operations are localized in users’ terminals, while the data and analytical logics are permanently preserved on the blockchain. Experiments are conducted to validate the performance of the proposed system. By sacrificing affordable text processing time, Seshat shows better robustness and compatibility to facilitate effective digital assistance for text analysis applications like oral history studies.
Session Chair
Wei Cai (The Chinese University of Hong Kong, Shenzhen, China)
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