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An Efficient Sybil Attack Detection Approach for Vehicular Ad-Hoc networks

An Efficient Sybil Attack Detection Approach for Vehicular A...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Durgesh Lohar Sanjaya Kumar Panda Slokashree Padhi Sanjib Kumar Nayak Department of Computer Science and Engineering National Institute of Technology Warangal Warangal Telangana India Department of Computer Application Veer Surendra Sai University of Technology Burla Burla Odisha India

ISBN: (纸本)9781665454025

Vehicular ad-hoc networks (VANETs) play a vital role in next-generation wireless communication. It consists of a set of moving vehicles, stationary vehicles, and roadside units (RSUs) that are connected through a wireless network. However, security and privacy issues are always challenging in VANETs as they lead to attacks. One such attack is the detection of the Sybil attack. This attack makes use of a vehicle or node to create multiple fake identities, at a time, to take advantage of a particular network. This attack leads to traffic congestion, rerouting, and accident. Many researchers have proposed solutions to identify the Sybil attack. Recently, Ayaida et al. [1] have presented an approach to detect the Sybil attack based on a macroscopic traffic flow model. This approach calculates the estimated speed and real speed, and their difference. If the difference is more than a predetermined threshold, then it suspects an attack and sends a message to nearby vehicles. This paper modifies their approach to provide a better solution by estimating the speed of vehicles and by RSU, thereby averaging their speed. The proposed approach is simulated using network simulator (ns) 3 and simulation of urban mobility (SUMO) to show its effectiveness.

关键词： Privacy Wireless networks Computational modeling Grid computing Ad hoc networks Security Traffic congestion

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Convolutional graph neural network training scalability for molecular docking

Convolutional graph neural network training scalability for ...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Kevin Crampon Alexis Giorkallos Stephanie Baud Luiz Angelo Steffenel Center for Excellence in Advanced Computing Atos SE Echirolles France CNRS MEDyC Université de Reims Champagne-Ardenne Reims France Université de Reims Champagne-Ardenne Reims France

Deep learning use is growing in many numerical simulation fields, and drug discovery does not escape this trend. Indeed, before proceeding with in vitro and then in vivo experiments, drug discovery now relies on in silico techniques such as molecular docking to narrow the number of experiments and identify the best candidates. This method explores the receptor surface and the ligand's conformational space, providing numerous ligand-receptor poses. All these poses are then scored and ranked by a scoring function allowing to predict the best poses among all, then compare different ligands regarding a given receptor or different targets regarding a given ligand. Since the 2010s, numerous deep learning methods have been used to tackle this problem. Nowadays, there are two significant trends in deep learning for molecular docking: (i) the augmentation of available structural data and (ii) the use of a new kind of neural network: the graph convolutional neural networks (GCNs). In this paper, we propose the study of training scalability of a GCN-a molecular complex scoring function-on an increasing number of GPUs and with a variety of batch sizes. After a hyperparameter analysis, we achieve an 80% reduction in the training time, but this improvement sometimes involves a performance metrics degradation that the final users must ponder.

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Instructions with Complex Control-Flow Entailing Machine Learning

Instructions with Complex Control-Flow Entailing Machine Lea...

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed computing (SNPD)

作者： Saurabh Shinde Harneet Singh Bali Development Accelya Solutions India Limited Pune India Electrical and Electronics Engineering Manipal Institute of Technology Udupi India

Reinforcement learning is when the system is allowed to make its own decisions based on what it learns. There are 2 types of observations, formative and summative. These observations have been identified as crucially important for neural network training of complicated tasks with conditional control flow. The central theme of this paper is applying reinforcement learning to follow instructions with complex control-flow. The authors study a special but important subset of multi- task reinforcement learning problems, namely instructions with complex control-flow in this work. They develop an encoding and attention architecture to achieve the research objective.

关键词： Training Neural networks Natural languages Reinforcement learning Learning (artificial intelligence) Multitasking Encoding

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Dynamic Tracing of DoS Attack Over Software-Defined networks Using Machine Learning

Dynamic Tracing of DoS Attack Over Software-Defined Networks...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Amit Chopra Dinesh Chander Verma PEC-CCE Chandigarh India Department of Computer Applications Panipat Institute of Engineering & Technology Panipat India

ISBN: (纸本)9781665454025

In this paper, a machine learning approach to detect the Denial-of-Service attack over software-defined networks (SDN) is introduced. It builds the training model using NS-3 traces to detect the Denial-of-Service (DoS) attack that can be launched by an intruder to block access to network resources. This work represents a machine learning approach to detect and prevent the SDN from DoS threat by examining the abnormalities in a given traffic pattern using classifiers. A machine learning model is developed based on the dynamic tracing of log files and traffic classification has been carried out to identify the DoS threat. A dynamic trace log is maintained to filter out the identified nodes from the SDN. Various classifiers viz K-Nearest Neighbors (KNN), Decision tree (DT), Logistic regression(LG), and Support Vector Machine (SVM) are used for traffic classification and their performance is also measured using various parameters Precision, Recall, F1-score, and Accuracy. SDN performance is also analyzed using different simulation scenarios with respect to node densities 50, 100, and 200.

关键词： Support vector machines Training Machine learning Traffic control Denial-of-service attack Grid computing Security

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A Survey on VANET attacks and its Security Mechanisms

A Survey on VANET attacks and its Security Mechanisms

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international conference on parallel distributed and Grid computing (PDGC)

作者： Tejaswi Sapala Rama Chandra Suresh Reddy Penumallu Reddy Sai Kiran M.V. Rajesh B.S.Kiruthika Devi Department of Computer Science and Engineering Aditya Engineering College Surampalem A.P. India CL Educate Ltd New Delhi India

ISBN: (纸本)9781665454025

There has been a recent surge in interest in wireless related VANETs, which are swiftly rising to the top of the priority research list for the smart-transportation system. Such system offers protection and safety measures for both driver and traveler. VANETs differ greatly from mobile ad-hoc-networks in terms of uniqueness, issues, system-design, and application. We address the architecture, security, and problems of VANETs in this study, which summarizes recent state-of-the-art solutions. Second, we go over a detailed study of security systems as well as potential solutions for ensuring secure communication in VANETs. Then, we go over all of the authentication systems that can safeguard the vehicular network from malicious nodes and other threats. This paper discusses about various attacks in VANETS and the security methods used to detect the attacks in real time. The attacks that are considered are collision attacks, sybil attacks, black-hole attacks, and jamming attacks. The security of the network is assured by detecting the threats in a timely manner.

关键词： Wireless communication Vehicular ad hoc networks Surge protection Grid computing Real-time systems Safety Security

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Efficient Control of Unscheduled Packets for Credit-based Proactive Transport

Efficient Control of Unscheduled Packets for Credit-based Pr...

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international conference on parallel and distributed Systems (ICPADS)

作者： Xin He Wenxin Li Song Zhang Keqiu Li College of Intelligence and Computing Tianjin University China

Proactive transport has been attractive in modern high-speed and shallow buffered datacenter networks. At its heart, the link capacity is proactively allocated as credit, and then following scheduled packets are triggered by active senders according to credits, providing (near) zero loss rate and extremely low latency. Despite being promising, when waiting for credits in the first RTT, called the “pre-credit phase a substantial amount of spare bandwidth is being underutilized. To bridge this gap, current practices send a Bandwidth-Delay-Product (BDP) worth of unscheduled packets with line rate in the pre-credit phase, however, degrading throughput or tail latency. One key insight is that they consider the spare bandwidth in the pre-credit phase as a fixed value but actually changes across time and space. In this paper, we present Schef, a novel switch-based bandwidth calculation mechanism to address the pre-credit phase challenge without introducing network congestion or throughput degradation. The main idea is to calculate the spare bandwidth and apply efficient control of unscheduled packets at switches. Schef is compatible with existing proactive transports. We integrate it into a representative proposal NDP and evaluate its performance through large-scale simulations. Compared with the state-of-art, Schef reduces the average and 99th flow completion time (FCT) of short flows by up to 23% and 31%, and achieves 13% higher goodput simultaneously.

关键词： Heart Degradation Bandwidth Switches Tail Aerospace electronics Throughput

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Enhancing the Performance of an Autonomous distributed Storage System in a Large-Scale Network

Enhancing the Performance of an Autonomous Distributed Stora...

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Summer Virtual conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing (SNPD-Summer), ACIS international

作者： Atsushi Nunome Hiroaki Hirata Faculty of Information and Human Sciences Kyoto Institute of Technology Kyoto Japan

ISBN: (纸本)9798350396386

We are developing an autonomous distributed storage system that autonomously relocates data blocks according to their utilization and the effective performance of storage. This paper proposes an enhancement to the distributed storage system to address large network environments where many network nodes are connected to multiple network switches. In the proposed method, the latency time when a network frame travels on the network is evaluated together with the effective performance of the storage, and this is used to determine the destination of the data block. Simulation evaluation results show that in the networks with cascading multiple network switches and high I/O load on storage, the performance improvement is significantly greater than that of the previous method. This showed that our storage system can provide sufficient performance improvement even in large network environments.

关键词： distributed databases Artificial intelligence Software engineering Load modeling

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5G Edge computing Enhanced Mobile Augmented Reality

5G Edge Computing Enhanced Mobile Augmented Reality

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19th IEEE international conference on Pervasive computing and Communications (IEEE PerCom)

作者： Cao, Jacky Univ Oulu Oulu Finland

ISBN: (纸本)9781665404242

Physical world enhancements through virtually drawn annotations on mobile devices are a core component of mobile augmented reality (MAR) experiences. However, resource intensive computer vision tasks typically limit the full deployment of MAR pipelines on-device due to a lack of required computation resources. As an alternative, a distributed scheme between client devices and edge servers enables offloading computation intensive tasks to powerful machines. This introduces challenges, such as maintaining low communication latency and high bandwidth channels between client and edge server. Current LTE and WiFi infrastructure do not fulfil such requirements. 5G networks are an ideal candidate to provide seamless MAR experiences. This research investigates the enhancement of MAR with both 5G and edge computing and aims to address two particular challenges: 1) the creation of a new 5G-based edge computing infrastructure to support reliable MAR, and 2) investigating which particular transport protocols allow for optimised quality of experience (QoE) for MAR applications within 5G infrastructure.

关键词： mobile augmented reality edge computing 5g

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The Adaptive Fault-tolerant Routing Based on an Improved Local Security Information Model of the Exchanged Hypercube

The Adaptive Fault-tolerant Routing Based on an Improved Loc...

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

作者： Yazhi Zhang Chuanwen Luo Guijuan Wang Li Zhang Mengjie Lv Jiguo Yu School of Computer Science and Technology Qilu University of Technology Jinan China School of Information Science and Technology Beijing Forestry University Beijing China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China College of Computer Nanjing University of Posts and Telecommunications Nanjing China Big Data Research Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China

With the increasing amount of computation in high-performance computing, the scale of interconnection networks is becoming larger and larger. It is inevitable that processors or links in the network become faulty. Therefore, it is very important for a processor to understand the security status of itself and neighbors, and then adaptively and reliably communicate. However, the existing security information models consider faulty processors or faulty links separately, and do not consider the situation where both processors and links fail simultaneously. The Exchanged Hypercube is an excellent interconnection network structure which achieves a balance between network functionality and hardware overhead while preserving many excellent properties of the Hypercube. In this paper, we propose an adaptive fault-tolerant routing algorithm of the Exchanged Hypercube based on an improved local security information model, which considers the faulty processors and faulty links simultaneously. This algorithm always can select suitable processors for routing based on the local security information and unsafe coefficients of processors, which greatly reduces the impact of the faulty of networks. The adaptive fault-tolerant routing algorithm of the Exchanged Hypercube network is not only suitable for regular networks but also for heterogeneous networks since the Exchanged Hypercube is a semi regular network. Sufficient experimental results show that the algorithm proposed in this paper has a lower packet loss rate, a higher success rate, and a shorter average path length.

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A parallelizable Counterfactual Generation Method Based on Gradient Optimization

A Parallelizable Counterfactual Generation Method Based on G...

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international conference on parallel and distributed Systems (ICPADS)

作者： Haorun Ding Xuesong Jiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

Post-hoc explanations are important for people to understand the predictions of explanation models. One class of methods in post-hoc explanation is the generation of counterfactuals, where a hypothetical example is obtained by perturbing the inputs to show how one could obtain a different prediction from the decision model. Counter-factual explanations should satisfy several properties: One is that counterfactuals generated under specific scenarios and constraints should be feasible for users, i.e., they should accommodate different causal constraints. The other is that it is more desirable for users to have a wider variety of viable examples, i.e., counterfactual diversity. To this end, we propose a parallelizable method based on gradient optimization. We partition the input feasible domains, perform counterfactual generation independently for each feasible domain, and then parallelize the counterfactual generation process for each feasible domain. Experimental results show that our approach effectively improves the diversity, sparsity, and proximity of the generated counterfactual instances on the public datasets Adult-Income, Lending-Club, German-Credit, and COMPAS compared to other models.

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