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Performance Improvement of DDoS Intrusion Detection Model Using Hybrid Deep Learning Method in the SDN Environment

Performance Improvement of DDoS Intrusion Detection Model Us...

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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： Ameni Chetouane Kamel Karoui RIADI Laboratory National School of Computer Science Manouba Tunisia National Institute of Applied Sciences and Technologies Tunis Tunisia

Software-Defined Networking (SDN) is a networking paradigm that has the potential to revolutionize the way we develop and operate network infrastructures. SDN enables network engineers to quickly monitor networks, centrally manage networks, and quickly and accurately detect malicious traffic and link failures. In addition to its flexibility, SDN is also susceptible to various attacks like distributed Denial of Service (DDoS) that can bring down the entire network. In recent years, Deep Learning (DL) approaches have been applied for reliable and highly accurate traffic anomaly detection. Therefore, this paper proposes a method based on DL to classify the benign traffic from the DDoS attack traffic. The major contribution of this paper is to propose a novel hybrid DL method to perform the classification. In addition, we compare different deep learning methods using attack severity, which is integrated into the standard metrics to differentiate the quality of the results of DL methods. The attack severity will be calculated using an appropriate weighting of undiscovered intrusions (FN and FP) discovered during the testing phase. Besides, various DDoS attack detection research projects employ public datasets that are not specific to the SDN environment. We propose a method to determine the adequacy of a selected dataset using two proposed metrics based on quality and quantity conformity evaluation.

关键词： Deep learning Measurement Denial-of-service attack Ubiquitous computing Virtual machining Reliability Computer crime

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Workload Prediction in Edge computing based on Graph Neural Network

Workload Prediction in Edge Computing based on Graph Neural ...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： WeiWei Miao Zeng Zeng Mingxuan Zhang Siping Quan Zhen Zhang Shihao Li Li Zhang Qi Sun Information and Telecommunication Branch State Grid Jiangsu Electric Power CO.LTD Nanjing China State Grid Taizhou Power Supply Company Nanjing China

In a typical edge computing paradigm, multiple edge servers are located near the end users to provide augmented computation and bandwidth. As the resources of the edge servers are limited, precisely predicting the workload of different edge servers can be of great importance in efficiently utilizing the edges. However, due to the dynamics of end users, the workload in the edge servers have lots of spikes. In this paper, we consider a deep learning method to predict the resource usage of edge servers. The main idea is to use graph neural network (GNN) to capture the interconnected topology of the edge servers. The edge servers that are close in proximity often have similar resource load patterns. The GNN is then concatenated with a LSTM layer to output the prediction value. The algorithm is evaluated in a real edge computing data set. The results show that our algorithm have high accuracy in predicting the resource usage. In addition, it outperforms other SOTA algorithms significantly.

关键词： Deep learning Network topology Aggregates Bandwidth Prediction algorithms Graph neural networks Topology

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Power Quality Analysis of a distributed Generation System Using Unified Power Quality Conditioner 4th

Power Quality Analysis of a Distributed Generation System Us...

引用

4th international conference on Advanced computing and Intelligent Engineering, ICACIE 2019

作者： Samal, Sarita Hota, Akansha Hota, Prakash Kumar Barik, Prasanta Kumar School of EE KIIT Deemed to be University BhubaneswarOdisha India Department of EE VSSUT Burla India Department of MEE CAET OUAT Bhubaneswar India

ISBN: (纸本)9789811565830

This paper deals with power quality profile analysis of distributed generation (DG) system using unified power quality conditioner (UPQC). Despite the several benefits of DG like excellent energy supply, reducing the expansion of power distribution system, environmentally friendly, and so on, there are several challenges existing due to the integration of DG with the grid or operating it in stand-alone mode. Power quality (PQ) issue is one of the main technical challenges in DG power system. In order to provide improved PQ of energy supply, it is necessary to analyze the harmonics distortion of the system as well as the voltage sag and swell. The UPQC has been extensively useful and it is verified to be the best solution to diminish this PQ issue. This paper explores the detail of PQ impacts in a DG (comprising of Solar PV and Fuel cell) system operates in stand-alone mode. The voltage sag compensation with current and voltage harmonics are estimated at varying load conditions with different control scheme like the synchronous reference frame (SRF) and modified SRF technique. The proposed model is developed in MATLAB/SIMULINKR and the result obtained validates the superiority of the proposed technique over others in terms of harmonics elimination and sag compensation. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Power quality

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Performance Evaluation of Python parallel Programming Models: And mpi4py 6

Performance Evaluation of Python Parallel Programming Models...

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6th international IEEE/ACM Workshop on Extreme Scale Programming Models and Middleware, ESPM2 2021

作者： Fink, Zane Liu, Simeng Choi, Jaemin Diener, Matthias Kale, Laxmikant V. University of Illinois at Urbana-Champaign Department of Computer Science UrbanaIL United States

ISBN: (纸本)9781665411400

Python is rapidly becoming the lingua franca of machine learning and scientific computing. With the broad use of frameworks such as Numpy, SciPy, and TensorFlow, scientific computing and machine learning are seeing a productivity boost on systems without a requisite loss in performance. While high-performance libraries often provide adequate performance within a node, distributed computing is required to scale Python across nodes and make it genuinely competitive in large-scale high-performance computing. Many frameworks, such as Charm4Py, DaCe, Dask, Legate Numpy, mpi4py, and Ray, scale Python across nodes. However, little is known about these frameworks' relative strengths and weaknesses, leaving practitioners and scientists without enough information about which frameworks are suitable for their requirements. In this paper, we seek to narrow this knowledge gap by studying the relative performance of two such frameworks: Charm4Py and *** perform a comparative performance analysis of Charm4Py and mpi4py using CPU and GPU-based microbenchmarks other representative mini-apps for scientific computing. © 2021 IEEE.

关键词： Python

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Multiple Balance Subsets Stacking for Imbalanced Healthcare Datasets 26

Multiple Balance Subsets Stacking for Imbalanced Healthcare ...

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26th IEEE international conference on parallel and distributed Systems (IEEE ICPADS)

作者： Shao, Yachao Zhao, Tao Wang, Xiaoning Zou, Xiaofeng Fu, Xiaoming Univ Gottingen Inst Comp Sci D-37077 Gottingen Germany Natl Univ Def Technol Changsha 410073 Hunan Peoples R China Gannan Med Univ Affiliated Hosp 1 Ganzhou 341000 Jiangxi Peoples R China

ISBN: (纸本)9781728190747

Accurate prediction is highly important for clinical decision making and early treatment. In this paper, we study the imbalanced data problem in prediction, a key challenge existing in the healthcare area. Imbalanced datasets bias classifiers towards the majority class, leading to an unsatisfied classification prediction performance on the minority class, which is known as imbalance problem. Existing imbalance learning methods may suffer from issues like information loss, overfitting, and high training time cost. To tackle these issues, we propose a novel ensemble learning method called Multiple bAlance Subsets Stacking (MASS) by exploiting a multiple balance subsets construction strategy. Furthermore, we improve MASS with introducing parallelism (parallel MASS) to reduce the training time cost. We evaluate MASS on three real-world healthcare datasets, and experimental results demonstrate that its prediction performance outperforms the state-of-art methods in terms of AUC, F1-score and MCC. Through the speedup analysis, parallel MASS reduces the training time cost greatly on large dataset, and its speedup increases as the data size grows.

关键词： Imbalance Problem Ensemble Learning parallel computing Data Mining in Healthcare Multiple Balance Subsets Stacking

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A Reactive Power Supportive PV Based grid Tied distributed System Meeting Ancillary Objectives 5

A Reactive Power Supportive PV Based Grid Tied Distributed S...

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5th IEEE international conference on computing Communication and Automation, ICCCA 2020

作者： Pradhan, Subarni Singh, Bhim Panigrahi, Bijaya Ketan Indian Institute of Technology Delhi Department of Electrical Engineering New Delhi India

ISBN: (数字)9781728163246

ISBN: (纸本)9781728163246

Disconnection of photovoltaic (PV) farm from the grid, is the common practice on the occurrence of grid faults. Obeying the modern updated grid codes, developing a control algorithm for distributed converters for riding through the low voltage condition and continuing renewable power injection to the grid are the core objective of this paper. Along with the reactive power support, ancillary objectives such as stabilization of DC link voltage, supply of harmonics free balanced positive sequence grid currents synchronized with grid under the presence of nonlinear loads, reactive power compensation, power factor correction and flow of active power from solar PV array to grid are met using a cross feedback filter based control structure. Moreover, safe operation of the converter is assured by restraining the converter current to a safe limit. The developed control logic is simulated using MATLAB software tool and results are presented for verification. © 2020 IEEE.

关键词： Solar energy

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DISCO: distributed Control Plane Architecture for Resource Sharing in Heterogeneous Mobile Edge Cloud Scenarios 40

DISCO: Distributed Control Plane Architecture for Resource S...

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40th IEEE international conference on distributed computing Systems (ICDCS)

作者： Maheshwari, Sumit Netalkar, Prasad Raychaudhuri, Dipankar Rutgers State Univ WINLAB North Brunswick NJ 08902 USA

ISBN: (纸本)9781728170022

This paper presents a novel control plane protocol designed to enable cooperative resource sharing in heterogeneous edge cloud scenarios. While edge clouds offer the advantage of potentially lower latency for time critical applications, computing load generated by mobile users at the network edge can be very bursty as compared with aggregated traffic served by a data center. This motivates the design of a shared control plane which enables dynamic resource sharing between edge clouds in a region. The proposed control plane is designed to exchange key compute and network parameters (such as CPU GIPS, % utilization, and network bandwidth) needed for cooperation between heterogeneous edge clouds across network domains. The protocol thus enables sharing mechanisms such as dynamic resource assignment, compute offloading, load balancing, multi-node orchestration, and service migration. A specific distributed control plane (DISCO) based on overlay neighbor distribution with hop-count limit is described and evaluated in terms of control overhead and performance using an experimental prototype running on the ORBIT radio grid testbed. The prototype system implements a heterogeneous network with 18 autonomous systems each with a compute cluster that participates in the control plane protocol and executes specified resource sharing algorithms. Experimental results are given comparing the performance of the baseline with no cooperation to that of cooperative algorithms for compute offloading, cluster computing and service chaining. An application level evaluation of latency vs. offered load is also carried out for an example time-critical application (image analysis for traffic lane detection). The results show significant performance gains (as much as 45% for the cluster computing example) vs. the no cooperation baseline in each case at the cost of relatively modest complexity and overhead.

关键词： Cluster computing

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A Robust Aggregate Model of Virtual Power Plant Considering Uncertainty and Acceleration Strategies

A Robust Aggregate Model of Virtual Power Plant Considering ...

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Electrical Engineering and Control Science (IC2ECS), international conference on

作者： Jingchun Luan Baoshan Liu Hongwu Guo Ziyao Wang Tao Huang Huishi Liang Kui Zhou State Grid Inner Mongolia Eastern Electric Powe Co. Ltd. Chifeng Power Supply Company Chifeng China Shenzhen International Graduate School Tsinghua University Shenzhen China Sichuan Energy Internet Research Institute Tsinghua University Chengdu China

Virtual power plants can help active distribution networks fully utilize and efficiently manage large amounts of distributed energy resources by calculating the aggregate flexibility of VPPs at the point of common coupling, which is usually described as a two-stage robust optimization problem. Column and constraint generation algorithm has become a mature method for solving two-stage robust optimization problem, which decomposes the original problem into master problem and sub-problem. However, solving sub-problem is usually time-consuming. In order to improve the solving efficiency of sub-problem, two acceleration strategies are proposed in this paper. Firstly, we linearize the bilinear term in the SP objective function using a binary expansion. Then, an extreme scenarios method is adopted to deal with the randomness of renewables, which allows sub-problem to be solved in parallel and reduces unnecessary conservativeness. In addition, a max-min model is constructed to obtain an upper bound of the actual operating cost of virtual power plants. The cost functions of virtual power plants can be expressed as a group of piecewise-linear functions based on convex hull. Finally, the correctness and effectiveness of the proposed method are verified by numerical test results of three different distribution systems.

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A Customized Reinforcement Learning based Binary Offloading in Edge Cloud 26

A Customized Reinforcement Learning based Binary Offloading ...

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26th IEEE international conference on parallel and distributed Systems (IEEE ICPADS)

作者： Li, Yuepeng Chen, Lvhao Zeng, Deze Gu, Lin China Univ Geosci Sch Comp Sci Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9781728190747

To tackle the computation, resource poorness on the end devices, task offloading is developed to reduce the task completion time and improve the Quality-of-Service (QoS). Edge cloud facilitates such offloading by provisioning resources at the proximity of the end devices. Modern applications are usually deployed as a chain of subtasks (e.g., microservices) where a special offloading strategy, referred as binary of loading, shall be applied. Binary offloading divides the chain into two parts, which will be executed on end device and the edge cloud, respectively. The offloading point in the chain therefore is critical to the QoS in terms of task completion time. Considering the system dynamics and algorithm sensitivity, we apply Q-learning to address this problem. In order to deal with the late feedback problem, a reward rewind match strategy is proposed to customize Q-learning. Trace-driven simulation results show that our customized Q-learning based approach is able to achieve significant reduction on the total execution time, outperforming traditional offloading strategies and non-customized Q-learning.

关键词： Binary offloading edge computing reinforcement learning request scheduling

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Computation Task Scheduling and Offloading Optimization for Collaborative Mobile Edge computing 26

Computation Task Scheduling and Offloading Optimization for ...

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26th IEEE international conference on parallel and distributed Systems (IEEE ICPADS)

作者： Lin, Bin Lin, Xiaohui Zhang, Shengli Wang, Hui Bi, Suzhi Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China

ISBN: (纸本)9781728190747

Mobile edge computing (MEC) platform allows its subscribers to utilize computational resource in close proximity to reduce the computation latency. In this paper, we consider two users each has a set of computation tasks to execute. In particular, one user is a registered subscriber that can access the computation service of MEC platform, while the other unregistered user cannot directly access the MEC service. In this case, we allow the registered user to receive computation offloading from the unregistered user, compute the received task(s) locally or further offload to the MEC platform, and charge a fee that is proportional to the computation workload. We study from the registered user's perspective to maximize its total utility that balances the monetary income and the cost on execution delay and energy consumption. We formulate a mixed integer non-linear programming (MINLP) problem that jointly decides the execution scheduling of the computation tasks (i.e., the device where each task is executed) and the computation/communication resource allocation. To tackle the problem, we first derive the closed-form solution of the optimal resource allocation given the integer task scheduling decisions. We then propose a reduced-complexity approximate algorithm to optimize the combinatorial computation scheduling decisions. Simulation results show that the proposed collaborative computation scheme effectively improves the utility of the helper user compared with other benchmark methods, and the proposed solution method approaches the optimal solution within 0.1% average performance gap with significantly reduced complexity.

关键词： Mobile edge computing resource allocation user cooperation convex optimization

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