Currently, the Advanced Encryption Standard (AES) holds the distinction of being the most widely used symmetric cryptographic algorithm. The importance of developing AES with superior performance cannot be overstated, as it holds the potential to expand its vast range of applications. The encryption algorithm may leak some information during operation, which may be used by attackers for side channel attacks (SCA). CGRA (Coarse-Grained Reconfigurable Architecture), as a coarse-grained reconfigurable architecture, allows hardware resources to be reconfigured for different tasks. This reduces the impact of SCA during encryption and decryption. To improve the security of AES algorithm, this paper introduces an encryption and decryption framework based on open-source CGRA complier that enable domain experts to easily accelerate the plaintexts on reconfigurable processors. Firstly, we propose an improved hardware-friendly AES algorithm, which allows the processing elements (PE) of CGRA to access the data in a vectorized fashion. Secondly, a new set of CGRA instructions, based on the proposed algorithm, has been used and the performance has been improved up to 19 times when compared to the standard AES algorithm. Finally, we evaluate the proper size of CGRA to balance the performance and the area. Our experiments show that the best compromise of CGRA size is 8 * 8 for classic AES-128.
{"title":"An advanced encryption standard framework for coarse-grained reconfigurable processor","authors":"Xuetong Wu, Zhiyong Bu","doi":"10.1117/12.3031998","DOIUrl":"https://doi.org/10.1117/12.3031998","url":null,"abstract":"Currently, the Advanced Encryption Standard (AES) holds the distinction of being the most widely used symmetric cryptographic algorithm. The importance of developing AES with superior performance cannot be overstated, as it holds the potential to expand its vast range of applications. The encryption algorithm may leak some information during operation, which may be used by attackers for side channel attacks (SCA). CGRA (Coarse-Grained Reconfigurable Architecture), as a coarse-grained reconfigurable architecture, allows hardware resources to be reconfigured for different tasks. This reduces the impact of SCA during encryption and decryption. To improve the security of AES algorithm, this paper introduces an encryption and decryption framework based on open-source CGRA complier that enable domain experts to easily accelerate the plaintexts on reconfigurable processors. Firstly, we propose an improved hardware-friendly AES algorithm, which allows the processing elements (PE) of CGRA to access the data in a vectorized fashion. Secondly, a new set of CGRA instructions, based on the proposed algorithm, has been used and the performance has been improved up to 19 times when compared to the standard AES algorithm. Finally, we evaluate the proper size of CGRA to balance the performance and the area. Our experiments show that the best compromise of CGRA size is 8 * 8 for classic AES-128.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":" 32","pages":"131711C - 131711C-9"},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
At present, the application of deep learning algorithm to the scene of modulation type identification mostly focuses on single digital modulation type identification, and rarely involves the identification of mixed digital and analog modulation types. At present, the signal characteristics used in the identification network are single, and the analog signal does not have the common identification characteristics such as cyclic spectrum and constellation diagram, so the existing composition method is not suitable for the identification of mixed digital-analog signal sets. In order to solve these problems, a TCSE-ResNet50 mixed-signal recognition algorithm combining the fourth power spectrum of frequency spectrum is proposed, and a feature map with wider feature applicability is formed by combining the signal spectrum and the fourth power spectrum. According to the attention mechanism module included in the proposed TCSE-ResNet50 network, the model pays more attention to discrete spectral lines and reduces the interference of other background areas or random noise on signal recognition as much as possible. At the same time, the cross entropy and triplet loss functions are combined, and the cross entropy is used to widen the characteristic distance between different kinds of signals with similar frequency domain expressions, and the triplet is used to narrow the characteristic distance between similar signals caused by random baseband symbols or random additive noise, thus completing the identification of {FM, AM, 2ASK, BPSK, 2FSK, 16QAM, 16APSK} digital-analog mixed signal sets. When the signal-to-noise ratio is -2dB, the average recognition rate of this algorithm is over 93%, which is superior to single feature input and traditional convolutional network recognition model.
{"title":"TCSE-ResNet50 mixed-signal identification algorithm for joint spectrum and quartic spectrum","authors":"Shoubin Wang, Chunhui Hu, Ming Fang, Lei Shen","doi":"10.1117/12.3031987","DOIUrl":"https://doi.org/10.1117/12.3031987","url":null,"abstract":"At present, the application of deep learning algorithm to the scene of modulation type identification mostly focuses on single digital modulation type identification, and rarely involves the identification of mixed digital and analog modulation types. At present, the signal characteristics used in the identification network are single, and the analog signal does not have the common identification characteristics such as cyclic spectrum and constellation diagram, so the existing composition method is not suitable for the identification of mixed digital-analog signal sets. In order to solve these problems, a TCSE-ResNet50 mixed-signal recognition algorithm combining the fourth power spectrum of frequency spectrum is proposed, and a feature map with wider feature applicability is formed by combining the signal spectrum and the fourth power spectrum. According to the attention mechanism module included in the proposed TCSE-ResNet50 network, the model pays more attention to discrete spectral lines and reduces the interference of other background areas or random noise on signal recognition as much as possible. At the same time, the cross entropy and triplet loss functions are combined, and the cross entropy is used to widen the characteristic distance between different kinds of signals with similar frequency domain expressions, and the triplet is used to narrow the characteristic distance between similar signals caused by random baseband symbols or random additive noise, thus completing the identification of {FM, AM, 2ASK, BPSK, 2FSK, 16QAM, 16APSK} digital-analog mixed signal sets. When the signal-to-noise ratio is -2dB, the average recognition rate of this algorithm is over 93%, which is superior to single feature input and traditional convolutional network recognition model.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":" 24","pages":"131710H - 131710H-9"},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Considering the morning and evening peak traffic congestion caused by commuters' commuting, a HOV lane route layout method based on a heuristic algorithm was studied. The travel characteristics of commuters are summarized, and the travel time, travel comfort and travel cost of commuters are used as optimization objectives, and a heuristic algorithm is used to plan the layout of HOV lanes. By choosing the appropriate transfer station, the traffic efficiency of people can be effectively improved. It is verified by experimental results that choosing HOV lane layout can effectively improve the traffic efficiency of road sections, reduce commuting costs for commuters, and improve their commuting satisfaction. Experimental results show that the change in commuting methods has effectively reduced the commuting cost of commuters, from the original average of 69.838 yuan/time to the current average of 61.381 yuan/time. The travel cost was reduced by 12.1%, which proves that commuting costs can be effectively saved by using the transportation method in this article.
{"title":"Research on HOV lane route layout method based on heuristic algorithm","authors":"Jianxiang Wang, Junlin Sha, Zhenhua Zhang, Xu Chen, Jiabao Li, Kang Fei","doi":"10.1117/12.3032004","DOIUrl":"https://doi.org/10.1117/12.3032004","url":null,"abstract":"Considering the morning and evening peak traffic congestion caused by commuters' commuting, a HOV lane route layout method based on a heuristic algorithm was studied. The travel characteristics of commuters are summarized, and the travel time, travel comfort and travel cost of commuters are used as optimization objectives, and a heuristic algorithm is used to plan the layout of HOV lanes. By choosing the appropriate transfer station, the traffic efficiency of people can be effectively improved. It is verified by experimental results that choosing HOV lane layout can effectively improve the traffic efficiency of road sections, reduce commuting costs for commuters, and improve their commuting satisfaction. Experimental results show that the change in commuting methods has effectively reduced the commuting cost of commuters, from the original average of 69.838 yuan/time to the current average of 61.381 yuan/time. The travel cost was reduced by 12.1%, which proves that commuting costs can be effectively saved by using the transportation method in this article.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":" 40","pages":"131710B - 131710B-6"},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cong Tian, Hongyu Chu, Taiqi He, Yanhua Shao, Haode Shi
The UAV platform has limited computing resources, and the tracking algorithm needs better speed and accuracy tradeoff, a lightweight siamese network target tracking algorithm called SiamBAN-T based on SiamBAN. Firstly, to reduce the number of network parameters, mobilenetV3 was as to extract the siamese feature. Secondly, we introduce CA attention into the feature fusion module to enhance perception ability regarding target spatial-position information. Thirdly, multibranch cross correlation is incorporated into the head of the network to strengthen boundary information and scale information, thereby improving the anti-interference capability of our trackier. Finally, a feature enhancement module is designed to improve classification and regression abilities. Experimental results on UAV123 dataset demonstrate that compared with the original algorithm, our improved algorithm achieves an increase in success rate by 0.8% and accuracy by 0.8%. The running speed has been enhanced by 7.6 times for PC devices and 18.5 times for airborne mobile terminals, respectively. These experimental findings indicate that our SiamBAN-T significantly enhances tracking speed while maintaining high precision.
{"title":"Lightweight siamese object tracking algorithm based on SiamBAN","authors":"Cong Tian, Hongyu Chu, Taiqi He, Yanhua Shao, Haode Shi","doi":"10.1117/12.3032063","DOIUrl":"https://doi.org/10.1117/12.3032063","url":null,"abstract":"The UAV platform has limited computing resources, and the tracking algorithm needs better speed and accuracy tradeoff, a lightweight siamese network target tracking algorithm called SiamBAN-T based on SiamBAN. Firstly, to reduce the number of network parameters, mobilenetV3 was as to extract the siamese feature. Secondly, we introduce CA attention into the feature fusion module to enhance perception ability regarding target spatial-position information. Thirdly, multibranch cross correlation is incorporated into the head of the network to strengthen boundary information and scale information, thereby improving the anti-interference capability of our trackier. Finally, a feature enhancement module is designed to improve classification and regression abilities. Experimental results on UAV123 dataset demonstrate that compared with the original algorithm, our improved algorithm achieves an increase in success rate by 0.8% and accuracy by 0.8%. The running speed has been enhanced by 7.6 times for PC devices and 18.5 times for airborne mobile terminals, respectively. These experimental findings indicate that our SiamBAN-T significantly enhances tracking speed while maintaining high precision.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":" 2","pages":"1317107 - 1317107-7"},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jingsi Yang, Rong Yi, Yao Fu, Zhaojing Wang, Tongqing Li, Junxi Wang
Power failure in distribution network affects the quality of people's daily electricity consumption, so it is of great practical significance to quickly locate the fault location and realize power supply recovery for improving the reliability and safety of power supply. Based on this, this paper proposes an optimization method for rapid emergency recovery of distribution network power failure based on multi-agent algorithm. In the single power supply mode, the switching function is designed, and the power failure location result of distribution network based on multi-agent algorithm is determined by analyzing the power generation efficiency function of equipment. Automatic data is used to optimize the rapid emergency recovery of power failure, and multi-agent algorithm is introduced into it to realize the optimization method of rapid emergency recovery of power failure in distribution network. The experimental results show that the precision, recall and F1 score of the research method are all above 95%, and the power generation effect of the equipment is high, which can restore the normal operation of the distribution network more quickly and effectively.
配电网停电影响着人们的日常用电质量，因此快速定位故障位置并实现供电恢复对提高供电可靠性和安全性具有重要的现实意义。基于此，本文提出了一种基于多代理算法的配网停电快速应急恢复优化方法。在单电源供电模式下，设计开关功能，通过分析设备的发电效率函数，确定基于多代理算法的配电网断电定位结果。利用自动数据对停电快速应急恢复进行优化，并将多代理算法引入其中，实现配电网停电快速应急恢复的优化方法。实验结果表明，研究方法的精确度、召回率和 F1 得分均在 95% 以上，设备发电效果高，能更快速有效地恢复配电网的正常运行。
{"title":"Optimization method for rapid emergency recovery of power failure in distribution network based on multiagent algorithm","authors":"Jingsi Yang, Rong Yi, Yao Fu, Zhaojing Wang, Tongqing Li, Junxi Wang","doi":"10.1117/12.3032011","DOIUrl":"https://doi.org/10.1117/12.3032011","url":null,"abstract":"Power failure in distribution network affects the quality of people's daily electricity consumption, so it is of great practical significance to quickly locate the fault location and realize power supply recovery for improving the reliability and safety of power supply. Based on this, this paper proposes an optimization method for rapid emergency recovery of distribution network power failure based on multi-agent algorithm. In the single power supply mode, the switching function is designed, and the power failure location result of distribution network based on multi-agent algorithm is determined by analyzing the power generation efficiency function of equipment. Automatic data is used to optimize the rapid emergency recovery of power failure, and multi-agent algorithm is introduced into it to realize the optimization method of rapid emergency recovery of power failure in distribution network. The experimental results show that the precision, recall and F1 score of the research method are all above 95%, and the power generation effect of the equipment is high, which can restore the normal operation of the distribution network more quickly and effectively.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":" 22","pages":"131711G - 131711G-6"},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Clone detection of source code is one of the most fundamental software engineering techniques. Although intensive research has been conducted in the past few years, it has more often addressed syntactic code clone, and there are still a number of problems in detecting semantic code clone. In this paper, we propose an approach that uses C/C++ code to finetune the Bert pre-training model so that it better understands the syntactic and semantic features of the C/C++ code, thus enabling better source code similarity evaluation. We evaluated our approach on a large C/C++ code clone dataset and the results show that our approach achieves excellent semantic code clone detection.
{"title":"Semantic code clone detection based on BERT pre-trained model","authors":"Zekai Cheng, Jiahao Hu, Yongkang Guo, Xiaoke Li","doi":"10.1117/12.3031928","DOIUrl":"https://doi.org/10.1117/12.3031928","url":null,"abstract":"Clone detection of source code is one of the most fundamental software engineering techniques. Although intensive research has been conducted in the past few years, it has more often addressed syntactic code clone, and there are still a number of problems in detecting semantic code clone. In this paper, we propose an approach that uses C/C++ code to finetune the Bert pre-training model so that it better understands the syntactic and semantic features of the C/C++ code, thus enabling better source code similarity evaluation. We evaluated our approach on a large C/C++ code clone dataset and the results show that our approach achieves excellent semantic code clone detection.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":" 44","pages":"131711K - 131711K-7"},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Webshell is a backdoor program based on web services. Attackers can use WebShell to gain administrative privileges for web services, thereby achieving penetration and control of web applications. With the gradual development of traffic encryption technology, traditional detection methods that match text content features and network traffic features are becoming increasingly difficult to prevent complex WebShell malicious attacks in production environments, especially variant samples, adversarial samples or 0Day vulnerability samples, and the detection effect is not ideal. This article constructs a network collection environment and collects malicious Webshell traffic samples using different platforms, languages, and tools; A WebShell encrypted traffic recognition method based on Relie F feature extraction was proposed, which assigns weights to multiple features through the Relie F algorithm and selects feature groups with strong classification ability based on the size of the weights; Finally, use the LightGBM classification algorithm to identify normal encrypted traffic and WebShell encrypted traffic, and distinguish the management tools to which WebShell password traffic belongs. The experimental results indicate that this method can effectively distinguish between normal encrypted traffic and Webshell malicious traffic. The recognition accuracy and recall rate of Webshell management tool software are both higher than 92%.
{"title":"Research on WebShell encrypted communication detection based on machine learning","authors":"leiyu che, xiaodong liu","doi":"10.1117/12.3032051","DOIUrl":"https://doi.org/10.1117/12.3032051","url":null,"abstract":"Webshell is a backdoor program based on web services. Attackers can use WebShell to gain administrative privileges for web services, thereby achieving penetration and control of web applications. With the gradual development of traffic encryption technology, traditional detection methods that match text content features and network traffic features are becoming increasingly difficult to prevent complex WebShell malicious attacks in production environments, especially variant samples, adversarial samples or 0Day vulnerability samples, and the detection effect is not ideal. This article constructs a network collection environment and collects malicious Webshell traffic samples using different platforms, languages, and tools; A WebShell encrypted traffic recognition method based on Relie F feature extraction was proposed, which assigns weights to multiple features through the Relie F algorithm and selects feature groups with strong classification ability based on the size of the weights; Finally, use the LightGBM classification algorithm to identify normal encrypted traffic and WebShell encrypted traffic, and distinguish the management tools to which WebShell password traffic belongs. The experimental results indicate that this method can effectively distinguish between normal encrypted traffic and Webshell malicious traffic. The recognition accuracy and recall rate of Webshell management tool software are both higher than 92%.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":" 27","pages":"131711M - 131711M-6"},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The relationships between multi-source heterogeneous data and elements in the field of artificial intelligence security are integrated and analyzed in this paper, including attack information, data information, and other security data. Targeting the associated complex entity concepts that existed in the construction of the artificial intelligence security knowledge graph, the ontology structure is divided into theory layer, problem layer, and measure layer, making the artificial intelligence security ontology more diverse and expandable. The addition of the measure layer provides more accurate security decision-making reasoning for the subsequent knowledge inference stage.
{"title":"Research on multi-source heterogeneous data structure analysis technique based on AI security detection algorithm","authors":"Chunyan Yang, Songming Han, Jieke Lu, Shaofeng Ming, Wei Zhang","doi":"10.1117/12.3032167","DOIUrl":"https://doi.org/10.1117/12.3032167","url":null,"abstract":"The relationships between multi-source heterogeneous data and elements in the field of artificial intelligence security are integrated and analyzed in this paper, including attack information, data information, and other security data. Targeting the associated complex entity concepts that existed in the construction of the artificial intelligence security knowledge graph, the ontology structure is divided into theory layer, problem layer, and measure layer, making the artificial intelligence security ontology more diverse and expandable. The addition of the measure layer provides more accurate security decision-making reasoning for the subsequent knowledge inference stage.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":" 43","pages":"131710M - 131710M-7"},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the increasing commercialization of deep neural networks (DNN), there is a growing need for running multiple neural networks simultaneously on an accelerator. This creates a new space to explore the allocation of computing resources and the order of computation. However, the majority of current research in multi-DNN scheduling relies predominantly on newly developed accelerators or employs heuristic methods aimed primarily at reducing DRAM traffic, increasing throughput and improving Service Level Agreements (SLA) satisfaction. These approaches often lead to poor portability, incompatibility with other optimization methods, and markedly high energy consumption. In this paper, we introduce a novel scheduling framework, M-LAB, that all scheduling of data is at layer level instead of network level, which means our framework is compatible with the research of inter-layer scheduling, with significant improvement in energy consumption and speed. To facilitate layer-level scheduling, M-LAB eliminates the conventional network boundaries, transforming these dependencies into a layer-to-layer format. Subsequently, M-LAB explores the scheduling space by amalgamating inter-layer and intra-layer scheduling, which allows for a more nuanced and efficient scheduling strategy tailored to the specific needs of multiple neural networks. Compared with current works, M-LAB achieves 2.06x-4.85x speed-up and 2.27-4.12x cost reduction.
{"title":"M-LAB: scheduling space exploration of multitasks on tiled deep learning accelerators","authors":"Bingya Zhang, Sheng Zhang","doi":"10.1117/12.3032039","DOIUrl":"https://doi.org/10.1117/12.3032039","url":null,"abstract":"With the increasing commercialization of deep neural networks (DNN), there is a growing need for running multiple neural networks simultaneously on an accelerator. This creates a new space to explore the allocation of computing resources and the order of computation. However, the majority of current research in multi-DNN scheduling relies predominantly on newly developed accelerators or employs heuristic methods aimed primarily at reducing DRAM traffic, increasing throughput and improving Service Level Agreements (SLA) satisfaction. These approaches often lead to poor portability, incompatibility with other optimization methods, and markedly high energy consumption. In this paper, we introduce a novel scheduling framework, M-LAB, that all scheduling of data is at layer level instead of network level, which means our framework is compatible with the research of inter-layer scheduling, with significant improvement in energy consumption and speed. To facilitate layer-level scheduling, M-LAB eliminates the conventional network boundaries, transforming these dependencies into a layer-to-layer format. Subsequently, M-LAB explores the scheduling space by amalgamating inter-layer and intra-layer scheduling, which allows for a more nuanced and efficient scheduling strategy tailored to the specific needs of multiple neural networks. Compared with current works, M-LAB achieves 2.06x-4.85x speed-up and 2.27-4.12x cost reduction.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":" 6","pages":"131711E - 131711E-7"},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
hang zhang, liqi zhuang, dong wei, weiqing huang, Jing Li
Traffic identification is a vital technology in network security. Currently, the identification of mobile network traffic is based on the downlink data in the air interface. This is because it is difficult to synchronize uplinks and obtain uplink traffic data in real-world environments. We propose to utilize mobile communication network sideband resource occupancy for traffic identification. This method captures the uplink IQ data and draws a time-frequency resource map. In order to reduce the computational complexity, we only use the sideband portion of the time-frequency resource map for identification. Based on the different colors reflected on the time-frequency resource map by different users' uplink transmitting power, we distinguish the number of users by color and separate the different user data. The result shows that the accuracy of user number identification is up to 95%. Finally, we use Resnet18 to identify the service of the separated pictures. The F1 parameter of the Resnet18 network reaches 88%.
流量识别是网络安全的一项重要技术。目前，移动网络流量识别基于空中接口的下行链路数据。这是因为在实际环境中很难同步上行链路和获取上行链路流量数据。我们建议利用移动通信网络边带资源占用率进行流量识别。这种方法捕获上行链路 IQ 数据并绘制时频资源图。为了降低计算复杂度，我们只使用时频资源图的边带部分进行识别。根据不同用户的上行链路发射功率在时频资源图上反映出的不同颜色，我们用颜色区分用户数量，并分离出不同的用户数据。结果表明，用户号码识别的准确率高达 95%。最后，我们使用 Resnet18 来识别分离图片的服务。Resnet18 网络的 F1 参数达到 88%。
{"title":"Passive traffic analysis based on resource occupancy of mobile communication uplink control channel","authors":"hang zhang, liqi zhuang, dong wei, weiqing huang, Jing Li","doi":"10.1117/12.3031911","DOIUrl":"https://doi.org/10.1117/12.3031911","url":null,"abstract":"Traffic identification is a vital technology in network security. Currently, the identification of mobile network traffic is based on the downlink data in the air interface. This is because it is difficult to synchronize uplinks and obtain uplink traffic data in real-world environments. We propose to utilize mobile communication network sideband resource occupancy for traffic identification. This method captures the uplink IQ data and draws a time-frequency resource map. In order to reduce the computational complexity, we only use the sideband portion of the time-frequency resource map for identification. Based on the different colors reflected on the time-frequency resource map by different users' uplink transmitting power, we distinguish the number of users by color and separate the different user data. The result shows that the accuracy of user number identification is up to 95%. Finally, we use Resnet18 to identify the service of the separated pictures. The F1 parameter of the Resnet18 network reaches 88%.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":" 4","pages":"131711Y - 131711Y-6"},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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