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Convolutional Neural network with Character Embeddings for Malicious Web Request Detection

Convolutional Neural Network with Character Embeddings for M...

引用

IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Jiahong Wu Zhenguo Yang Lingni Guo Yong Li Wenyin Liu School of Computer Science and Technology Guangdong University of Technology Guangzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

In this paper, a deep learning based approach to malicious Web request detection is proposed, where a Web request is a message sent from a client to a server for communication. The characters in a request message can be considered as words in a sentence from the perspective of natural language processing. Consequently, character embeddings can be learned from a corpus of Web requests in an unsupervised manner via language models. In terms of Web requests, the URL path and request query string convey the most information, which can be extracted as the corpus to learn character-level embeddings. Furthermore, a specially designed convolutional neural network (CNN) is applied to the learned character-level embeddings for malicious and legitimate request identification. Our approach is totally data-driven, being free from hand-crafted features that require domain expertise and high computational cost. Extensive experiments conducted on HTTP DATASET CSIC 2010 dataset show significant improvement on the performance against existing state-of-the-art methods, especially for extremely imbalanced cases. The devised CNN model exploiting the embedding representation of Web requests achieves a false positive rate (FPR) of 0.02% with 98.72% on true positive rate (TPR), and 99.46% on accuracy (ACC), respectively. Codes are publicly available at https://***/acew14/CL-Embedding-CNN.

关键词： Feature extraction Computational modeling Natural language processing Vocabulary Deep learning Task analysis Semantics

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Deep Learning-based Cooperative Trail Following for Multi-Robot System

Deep Learning-based Cooperative Trail Following for Multi-Ro...

引用

International Joint conference on Neural networks

作者： Mingyang Geng Yiying Li Bo Ding Huaimin Wang National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology ChangSha China

Following trails in the wild is an essential capability of out-door autonomous mobile robots. Recently, deep learning-based approaches have made great advancements in this field. However, the existing research only focuses on the trail following with a single robot. In contrast, many robotic tasks in the reality, such as search and patrolling, are conducted by a group of robots. While these robots are grouped to move in the wild, they can cooperate to significantly promote the trail following accuracy, for example, by sharing images of different view angles or real-time decision fusion. This paper proposes such an approach named DL-Cooper that enables multi-robot vision-based trail following based on deep learning algorithms. It allows each robot to make a decision respectively with deep neural network and then fusion the decisions on the collective level with the support of back-end cloud computing infrastructure. It also takes Quality of Service (QoS) assurance, a very essential property of robotic software, into consideration. By limiting the condition to fusion decisions, the time latency can be minimally sacrificed. Experiments on the real-world dataset show that our approach has significantly improved the accuracy of the single-robot system.

关键词： Task analysis Robot kinematics Machine learning Multi-robot systems Quality of service Cloud computing

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An A3C Deep Reinforcement Learning FPGA Accelerator based on Heterogeneous Compute Units

An A3C Deep Reinforcement Learning FPGA Accelerator based on...

引用

International conference on Communication Technology (ICCT)

作者： Ziyu Li Fen Ge Fang Zhou Ning Wu College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

Deep reinforcement leaning (DRL) has a great performance in intelligent decision-making question. As one of well-performance DRL algorithms, Asynchronous Advantage Actor-Critic (A3C) can make distributed agents work simultaneously to infer and train without experience pool. However, the multiple agents also bring complex deep neural network (DNN) computations in each agent which costs the almost time in A3C algorithm. In this paper, we propose a DRL FPGA accelerator based on heterogeneous compute units for A3C algorithm to reduce the DNN computing periods. According to A3C algorithm computing flow, we implement both inference and training steps on accelerator and pipeline their computations. In DNN forward propagation (FP) and backward propagation (BP) computing, the Multiply-ADDer-tree (MADD) structure with high input parallelism and Multiply-Accumulation (MAC) structure with high output parallelism are proposed to be employed, respectively. Heterogeneous computing method makes the weight parameters layout of inference and training consistent, and improves the access efficiency of on-chip memory. Experimental results show that our design achieves 40-times speedup in inference-training speedup compared to CPU, and 54-times speedup compared to GPU. Moreover, our heterogeneous computing method lead to better efficiency in memory accessing compared to exiting approaches.

关键词： Training Deep learning Accelerated aging Memory management Graphics processing units parallel processing Inference algorithms

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A Batch Acquisition of Model Data for Aircraft Heat and Surface Treatment

A Batch Acquisition of Model Data for Aircraft Heat and Surf...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Ye Hu Liang Zhao Xin Zhou Network Information Center AVIC Shenyang Aircraft Company Limited Shenyang China School of Computer Science Shenyang Aerospace University Shenyang China

ISBN: (纸本)9781728152103

In order to meet the requirement of rapid process preparation for new aircraft in heat and surface treatment, a method of batch acquisition of model data is proposed based on the principle of centralized batch production in heat and surface treatment workshop. The batch acquisition of MBD information, surface area, and preview are realized with the secondary development of CATIA/CAA, which provides a fast and accurate method for extracting the information needed by heat and surface process. This method improves the quality and efficiency of heat and surface treatment process and is of some practical value in aircraft manufacturing.

关键词： aircraft manufacture batch processing (industrial) CAD data acquisition heat treatment production engineering computing quality control surface treatment Surface treatment Aircraft manufacture production engineering computing heat Data Collection Methods Quality Control heat treatment batch production surface process

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ScalScheduling: A Scalable Scheduling Architecture for MPI-based interactive analysis programs

ScalScheduling: A Scalable Scheduling Architecture for MPI-b...

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International conference on Computer Communications and networks (ICCCN)

作者： Jiangling Yin Andrew Foran Xuhong Zhang Jun Wang EECS University of Central Florida Orlando Florida

In today's large scale clusters, running tasks with high degrees of parallelism allows interactive data visualization/analysis to complete in seconds. However, conventional, centralized scheduling poses significant challenges for these interactive applications. As the amount of data to be processed grows, it becomes too heavy to move across the network. Thus, data processing tasks should be scheduled such that the amount of transferred data is minimized, i.e., realizing data locality computation. To implement this, a scheduler process should collect and analyze data distribution metadata prior to making scheduling decisions, which usually causes milliseconds or seconds of latency. Such scheduling delay is unacceptable for interactive data applications. In this paper, we present a Scalable Scheduling Architecture for conventional interactive data programs and refer to it as ScalScheduling. ScalScheduling is proposed to reduce task scheduling latency, while ensuring the worker processes achieve a high degree of data locality computation and load balance in heterogeneous environments. In our proposed architecture, each worker process uses a novel Modulo-based priority method to schedule its local tasks independently. Multiple scheduler processes are employed according to the number of worker processes to resolve the issue of concurrent requests and assign remote tasks with respect to load balance. We perform experiments using thousands of parallel processes, and the experimental results show the benefits of our proposed scheduling architecture as well as its potential for future oversize task scheduling problems on large-scale clusters.

关键词： Processor scheduling Scheduling Computer architecture Data processing Schedules distributed databases Process control

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Agricultural Product Traceability System based on Blockchain Technology

Agricultural Product Traceability System Based on Blockchain...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Weipeng Xie Xianghan Zheng Xiaoliang Lu Xiaowei Lin Xionghui Fan Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China MingByte Technology (Qingdao) Co. Ltd Qingdao China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

The quantity of agricultural products produced in China is huge. How to ensure the quality of agricultural products from farmland to table is a problem that has been studied all the time. From the perspective of decentralization, this paper uses the blockchain technology to combine the characteristics of agricultural products traceability, and the alliance chain hyperledge recology. As the underlying technology of the blockchain, the Fabric1.4.2 project under the application, the factory, quality inspection, logistics, distribution and supervision as the alliance nodes in the alliance chain, combined with the Internet of Things technology to automatically collect data to achieve agricultural products from planting, processing, quality Traceability of inspection, logistics, distribution and other processes. Through the scanning of the two-dimensional code, the matching of the traceable data and the data uplink are realized, and the products produced are guaranteed to be one yard and one code. Finally, the trace data of the production and circulation process of the product is realized through the scanning of the code of the e-commerce platform of the WeChat mini program.

关键词： Blockchain Organizations Peer-to-peer computing Agricultural products Inspection Production facilities

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Predicting Future Alleviation of Mental Illness in Social Media: An Empathy-based Social network Perspective

Predicting Future Alleviation of Mental Illness in Social Me...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Yibo Chai Fengyang Wu Rui Sun Zhongliang Zhang Jie Bao Runxin Ma Qizhou Peng Danqin Wu Yexing Wan Keyu Li School of Information Central University of Finance and Economics Beijing China School of Culture and Communication Central University of Finance and Economics Beijing China School of Insurance Central University of Finance and Economics Beijing China Shenzhen Corerain Technologies Co. Ltd Shenzhen China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Numerous studies have shown that users' posts on social media can explicitly or implicitly reflect various human psychological characteristics. Through mining these data, predictive models can be built to forecast and analyze potential mental illness, which can facilitate therapeutic decision making and offer the best hope for early interventions and treatments. However, most existing approaches face severe information loss and ignore the time dynamics of user behaviour. To fill the research gaps, we use time-aware social networks to combine various information sources in social media posts. Also, machine learning detectors are trained to automatically identify empathic interactions, filter out irrelevant information and construct empathy-based networks. Finally, we devise a hybrid deep learning algorithm to learn embeddings from the dynamic feature-rich networks and predict future alleviation of mental illness. Compared with strong baselines, our approach achieves the best-performing results with efficient computation speed.

关键词： Electromagnetic interference Erbium distributed processing Big Data Cloud computing Social computing

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Design of No-Go Game Algorithm based on Reinforcement Learning

Design of No-Go Game Algorithm Based on Reinforcement Learni...

引用

IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Jianbo Bo Shuqin Li Rui Geng Jiaming Tong Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781728152103

The research focus of computer chess is how to use computer to abstract and formalize the logical thinking activities of human beings in chess game. This paper designs and implements a No-Go algorithm based on reinforcement learning. During the game, MCTS algorithm is used to explore the legal situation under the current situation. In this process, the convolutional neural network is used to evaluate the explored situation. Then, the neural network is trained to evaluate the situation with the results of the match, so as to achieve the effect of self-improving algorithm without human experience. In addition, the author used the Union-Find to find the legal landing point on the chessboard. Finally, the experiment shows that this type of No-Go algorithm based on reinforcement learning can be improved by itself and reach a certain level of chess ability, which improves the efficiency of searching legal moves compared with the traditional No-Go chessboard traversal method.

关键词： computer games convolutional neural nets learning (artificial intelligence) Monte Carlo methods tree searching Tree searching Computer Games learning (artificial intelligence) Monte Carlo technique Human Learning algorithms automatic game Video Games Hunting Game theory Game animals Computers

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Effect of Scheduling Discipline on CPU-MEM Load Sharing System

Effect of Scheduling Discipline on CPU-MEM Load Sharing Syst...

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第六届网格与协同计算国际会议(The Sixth International conference on Grid and Cooperative Computing GCC 2007)

作者： Lei Shi Yuyan Sun Lin Wei School of Information Engineering Zhengzhou University Zhengzhou 450052 China Henan Provincial Key Lab on Information Network Zhengzhou 450052 China

Scheduling schemes have important effects on the performance of load sharing in distributed *** the load sharing policy based on CPU-memory has made the system memory an important role in effecting the system performance, it can reduce the paging fault and enhance the usage of the system resource. In light of the characteristics of the load sharing based on the CPU-memory and the variation of the jobs in executing, new CPU local scheduling schemes named more memory-request more CPU Slice based on Round Robin (RR-MMMCS) mechanism and more memory-request more CPU slice based on predicting (MMMCS-P) mechanism, are ***, the effects on the load sharing policy based on the CPU-memory of the variance of the interarrival time and service time are discussed. The tracedriven simulations show that the load sharing policy based on the CPU-memory and RR-MMMCS,MMMCS-P scheduling schemes are effective and have better performance in average response time for both CPU-memory and memory-bound jobs.

关键词： Processor scheduling System performance Round robin Delay Costs Job design Scheduling algorithm parallel processing Throughput Grid computing

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An enhanced message exchange mechanism in cluster-based mobile ad hoc networks, 04

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Proceedings of the Second international conference on parallel and distributed processing and Applications

作者： Wei Lou Jie Wu Dept. of Computing Hong Kong Polytechnic University Kowloon Hong Kong Dept. of Computer Science and Engineering Florida Atlantic University Boca Raton Florida

ISBN: (纸本)9783540241287

In mobile ad hoc networks (MANETs), networks can be partitioned into clusters. Clustering algorithms are localized algorithms that have the property of creating an upper bounded clusterheads in any networks even in the worst case. Generally, clusterheads and selected gateways form a connected dominating set (CDS) of the network. This CDS forms the backbone of the network and can be used for routings (broadcasting/multicasting/unicating). As clusterheads need to determine the selected gateways to connect their adjacent clusterheads within the coverage set, they periodically collect 2-hop and 3-hop clusterhead information. These information can be gathered when they hear their non-clusterhead neighbors sending out messages that contain neighboring clusterhead information. These extra maintenance cost can be reduced when some enhanced mechanism is applied. In this paper, an enhanced mechanism is proposed to reduce the total length of the messages when non-clusterhead nodes exchange their 1-hop and 2-hop neighboring clusterhead information. Simulation shows that over 50% of message overhead can be saved for dense networks.

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