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A Novel distributed Machine Learning Framework for Semi-Supervised Detection of Botnet Attacks 11

A Novel Distributed Machine Learning Framework for Semi-Supe...

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11th international conference on Contemporary computing (IC3)

作者： Kaur, Gagandeep Jaypee Inst Informat Technol Dept CSE&IT Noida UP India

ISBN: (纸本)9781538668351

In today's Internet world where everything is interconnected, the misuse of the shared communication channels and the service providers by the malicious users requires all time monitoring. Amongst the various methods being adopted by network attackers, like distributed Denial of Service (DDoS) attacks, spams, phishing attacks, etc. botnet threats are increasing day-by-day. Detecting botnet attacks is a challenging task. Firstly, botnets are difficult to detect because of stealthy nature of Command & Control protocol. Secondly, different types of bots have varied characteristics and combined with large size of the network traffic their detection becomes a very challenging task. Lastly, network traffic is unlabeled and classification techniques like decision trees cannot be used directly. Moreover with the success of distributed frameworks like Hadoop and Apache Spark it is feasible to handle very large data. In this paper we have used distributed framework to apply semi-supervised machine learning techniques of KMeans clustering for labeling a large dataset and decision trees as classifiers. High accuracy was achieved in prediction of the classes. Novelty of our work is labeling of unlabeled network traffic and classification using efficient distributed frameworks.

关键词： distributed framework Botnet Detection Semi-supervised learning

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DEARS: A Deep Learning Based Elastic and Automatic Resource Scheduling Framework for Cloud Applications 16

DEARS: A Deep Learning Based Elastic and Automatic Resource ...

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Hassan, Muhammad Chen, Haopeng Liu, Yutong Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781728111414

Cloud computing paradigm supports more enterprises to provide satisfactory web services to their clients. However, the bursty and fluctuation of requests challenge the traditional resource scheduling framework. Previous strategies manage the jobs in each virtual machines (VMs) according to the derived historical utilization patterns, where the misalignment on the utilization curves may cause the resource over-prediction and over-provisioning. To better reduce the service latency and the above mentioned problem, we propose DEARS, a Deep learning based Elastic and Automatic Resource Scheduling framework for cloud applications. It gives a proactive and reactive strategy, where the LSTM model is pro-applied to predict the future request demand based on historical workload. the corresponding VM allocation is separately managed by restriction assessment, VM provision, and dynamic consolidation modules. then the SLAs feedback are iteratively applied to reactively improve the performance of resource allocation. Experiments based on real-life collected data shows the feasibility and efficiency of our framework. the high accuracy of prediction contributes to a more suitable allocation. And a better trade-off between QoS and SLAs in server side is achieved compared with the baselines.

关键词： Cloud computing resource scheduling deep learning SLAs

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Multi Agents to Search and Rescue Based on Group Intelligent Algorithm and Edge computing 11

Multi Agents to Search and Rescue Based on Group Intelligent...

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11th IEEE international Congress on conferences on Internet of things, 14th IEEE international conference on Green computing and Communications, 11th IEEE international conference on Cyber, Physical and Social computing, 4th IEEE international conference on Smart Data, 1st IEEE international conference on Blockchain and 18th IEEE international conference on Computer and Information Technology, ithings/GreenCom/CPSCom/SmartData/Blockchain/CIT 2018

作者： Yang, Tingting Jiang, Zhi Dong, Jie Feng, Hailong Yang, Chengming Dalian Maritime University Navigation College China School of Software Dalian University of Technology China

ISBN: (纸本)9781538679753

With the increasing number of maritime trade activities, carrying out rapid and effective maritime search and rescue operation has become an urgent need for research in order to ensure the safety of navigation at sea. Now, we mainly rely on artificial decision making, artificial distribution of search and rescue area, and artificial driving ship to complete search and rescue operation. the distribution, synergy, parallelism, robustness and intelligence of unmanned boat and UAV provide a new idea for the development of search and rescue work at sea. the marine search and rescue system under the new situation can be regarded as a hierarchical control network system composed of multi agents. And in this paper, a new fast search and rescue system is proposed by using the improved ant colony algorithm and the independent calculation decision of the agents. the system adopts the edge computing, relies on the information sharing and the cooperative decision between the search and rescue agent groups. And finally it achieves the independent synchronous search and rescue. distributed task allocation solutions break through the limitations of centralized hierarchical task allocation based on manpower. Edge computing requires all distributed control nodes to make autonomous decisions in the task process. Based on the distributed cluster control of unmanned boat and UAVs, this paper combines edge computing, cooperative communication, centralized task allocation and decision making together. © 2018 IEEE.

关键词： Edge computing

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Federated Galaxy: Biomedical computing at the Frontier 11

Federated Galaxy: Biomedical Computing at the Frontier

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11th IEEE international conference on Cloud computing (CLOUD) Part of the IEEE World Congress on Services

作者： Afgan, Enis Jalili, Vahid Goonasekera, Nuwan Taylor, James Goecks, Jeremy Johns Hopkins Univ Dept Biol Baltimore MD 21218 USA Oregon Hlth & Sci Univ Dept Biomed Engn Portland OR 97201 USA Univ Melbourne Melbourne Bioinformat Melbourne Vic Australia Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA

ISBN: (纸本)9781538672358

Biomedical data exploration requires integrative analyses of large datasets using a diverse ecosystem of tools. For more than a decade, the Galaxy project (https://***) has provided researchers with a web-based, user-friendly, scalable data analysis framework complemented by a rich ecosystem of tools (https://***/toolshed) used to perform genomic, proteomic, metabolomic, and imaging experiments. Galaxy can be deployed on the cloud (https://***), institutional computing clusters, and personal computers, or readily used on a number of public servers (e.g., https://***). In this paper, we present our plan and progress towards creating Galaxy-as-a-Service-a federation of distributed data and computing resources into a panoptic analysis platform. Users can leverage a pool of public and institutional resources, in addition to plugging-in their private resources, helping answer the challenge of resource divergence across various Galaxy instances and enabling seamless analysis of biomedical data.

关键词： cloud bursting data federation service computing

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An Energy Efficient Smartphone Pedometer based on An Auto-correlation Analysis 16

An Energy Efficient Smartphone Pedometer based on An Auto-co...

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Yang, Runze Huang, Baoqi Song, Jian Jia, Bing Li, Wuyungerile Inner Mongolia AR Key Lab Wireless Networking & M Hohhot Peoples R China Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China Baoshang Bank Ltd Dept Informat Technol Beijing Peoples R China

ISBN: (纸本)9781728111414

Smartphone pedometer is currently applied in various domains including health monitoring, context aware computing and Pedestrian Dead Reckoning (PDR). However, the limited and slowly increasing battery capacity of current smartphones results in severe contradictions with the fast development of various mobile applications and embedded sensors, which are usually hungry for energy. In terms of pedometer which has been available in every smartphone, most existing researches and practices focus on how to improve the step counting accuracy without considering its power consumptions. In this paper, we tackle with the well known auto-correlation based pedometer, and propose to substantially reduce its power consumptions based on a theoretical analysis. Firstly, we establish an error model for the traditional auto-correlation based step counting algorithm to analyze the influences of different factors on its performance. Secondly, in light of this model, we present an improved algorithm by simply calculating the acceleration variance instead of the more complicated auto-correlation coefficient to reduce its computational costs. thirdly, an Android APP is implemented to count steps based on the presented algorithms. Finally, extensive experiments are conducted by involving different smartphones and different users under various scenarios. Experimental results reveal that, the proposed algorithm is able to achieve comparative and even superior step counting accuracy, but reduces power consumptions by 39.5% on average.

关键词： pedometer power consumptions auto correlation smartphone

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On Scheduling of High-throughput Scientific Workflows under Budget Constraints in Multi-Cloud Environments 16

On Scheduling of High-Throughput Scientific Workflows under ...

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Li, Ruxia Wu, Chase Q. Hou, Aiqin Wang, Yongqiang Gao, Tianyu Xu, Mingrui Northwest Univ Sch Informat Sci & Technol Xian 710127 Shaanxi Peoples R China New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA

ISBN: (纸本)9781728111414

With the rapid development and deployment of cloud computing infrastructures, many applications in various scientific domains are increasingly utilizing cloud resources for big data storage and analysis. Particularly, it has become a significant challenge to manage and execute big data scientific workflows in multi-cloud environments to process streaming datasets. In this paper, within a three-layer workflow architecture with inter- and intra-cloud data transfer, we formulate a scientific workflow mapping problem under budget constraints to achieve the maximum throughput of streaming workflow applications. We propose a scheduling algorithm to identify the global bottleneck and maximize the throughput under budget constraints. Extensive simulation results show that the proposed algorithm exhibits superior performance over existing heuristic algorithms in scheduling streaming workflow applications.

关键词： Scientific workflow workflow mapping cloud computing throughput

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Maximizing influence propagation in networks by user-relational iterative ranking algorithm 16

Maximizing influence propagation in networks by user-relatio...

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Yao, Wenbin Zhang, Lijuan Li, Xiaoyong Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing Peoples R China Beijing Univ Posts & Telecommun Minist Educ Key Lab Trustworthy Distributed Comp & Serv Beijing Peoples R China

ISBN: (纸本)9781728111414

the issue of maximizing the influence is a hot topic in the research of social network. Many researchers have studied from the perspective of the structure of the network such as the LeaderRank algorithm. However, the algorithm lacks semantic interpretability and explanations for user behavior. therefore, we propose a novel URI (user-relational iterative) rank to address the above issues. the URI rank is divided into two parts to obtain the values of user influence. the first part is the forwarding probability based on the user relationship. We introduce the relationship between users to the user transition probability and use the random forest to quantify the value of the forwarding probability. the second part is the random transition probability based on the ground node. We optimize the weight assigning of the random transition probability by combining static decentralization and dynamic decentralization. thus, we more accurately represent the user's random transfer behavior. the experiments performed on the Sina Micro-Blog Dataset show that our algorithm outperforms the existing algorithms.

关键词： Social Network User Relationship Random Forest Iterative Ranking Independent Cascade Model

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Using Osmotic Services Composition for Dynamic Load Balancing of Smart City Applications 11

Using Osmotic Services Composition for Dynamic Load Balancin...

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11th IEEE international conference on Service-Oriented computing and Applications (SOCA)

作者： Souza, Arthur Wen, Zhenyu Cacho, Nelio Romanovsky, Alexander James, Philip Ranjan, Rajiv Univ Fed Rio Grande do Norte Natal RN Brazil Newcastle Univ Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9781538691335

Edge computing takes computation away from the Cloud closer to the physical world. therefore, it reduces the cost of communication bandwidth between IoT devices and the Cloud. However, Edge computing imposes certain limitations in computation power because due to poor hardware capacity of the devices. this restriction may significantly affect the performance of the deployed applications, especially Smart City applications. this limitations also could be aggravated by unpredictable human behaviors wich will easily make the Edge computation node overloaded. Osmotic computing is a new IoT application programming paradigm that provides an opportunity to balance the workload between Edge and Cloud therefore to overcome the load imbalance problem of Smart City applications. To this end, we propose an Osmotic Execution Framework that leverages state-of-the-art microservices techniques to deploy and execute a Smart City application in a distributed environment including Edge and Cloud. Finally, we evaluate load balancing through latency time analysis of our framework with a real-world smart parking application.

关键词： services framework microservices osmotic computing scalability

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Deep Learning for Phishing Detection 16

Deep Learning for Phishing Detection

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Yao, Wenbin Ding, Yuanhao Li, Xiaoyong Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing Peoples R China Beijing Univ Posts & Telecommun Minist Educ Key Lab Trustworthy Distributed Comp & Serv BUPT Beijing Peoples R China

ISBN: (纸本)9781728111414

Phishing attacks have caused significant damage to society. With the popularity of two-dimensional codes, a new type of phishing method called two-dimensional code phishing attacks has been created, which targets mobile users. In recent years, with the popularity of mobile devices, the threat of this new type of phishing attack is increasing, and may even exceed the traditional browser-based phishing attacks. In response to this threat, this paper proposes a relative detection method. the legitimacy of the URL in the two-dimensional code is evaluated by using a legal logo embedded in the two-dimensional code. the difficulty of this method is that the logo image is small, and the recognition rate of the small object is reduced by the traditional object recognition method. this paper uses the improved Faster R-CNN for small-scale identification and measures the impact of this method on the FlickrLogos-32 dataset. the experimental results show the effectiveness of the method in logo recognition, which can be used for two-dimensional code phishing attack detection.

关键词： Phishing Attack Faster R-CNN Logo Recognition FlickrLogos-32

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On Building Efficient Temporal Indexes on Hyperledger Fabric 11

On Building Efficient Temporal Indexes on Hyperledger Fabric

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11th IEEE international conference on Cloud computing (CLOUD) Part of the IEEE World Congress on Services

作者： Gupta, Himanshu Hans, Sandeep Mehta, Sameep Jayachandran, Praveen IBM Res Bombay Maharashtra India

ISBN: (纸本)9781538672358

We discuss the problem of constructing efficient temporal indexes on Hyperledger Fabric, a popular Blockchain platform. the temporal nature of the data inserted by Fabric transactions can be leveraged to support various use-cases. this requires that temporal queries be processed efficiently on this data. Currently this presents significant challenges as this data is organized on file-system, is exposed via limited API and does not support temporal indexes. In a prior work [1], we presented two models for creating temporal indexes on Fabric which overcome these limitations and improve the performance of temporal queries on Fabric. the first model creates a copy of each event inserted and stores temporally close events together on Fabric. the second model keeps the event count intact but tags metadata to each event s.t. temporally close events share the same metadata. In this paper, we present variants on these two models which are better able to handle the skew present in Fabric data. We discuss the details and show that these variants significantly outperform the approaches presented in [1] when Fabric data contains skew. We also discuss the performance tradeoffs among these variants across various dimensions - data storage, query performance, event insertion time etc.

关键词： Fabrics Containers distributed databases Indexing Data models"

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