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Security Analyses of an Anonymous Two Factor Authentication Protocol for Roaming Service in Global Mobile networks

Security Analyses of an Anonymous Two Factor Authentication ...

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22nd IEEE/ACIS international conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing (SNPD-Fall)

作者： Chang, Ya-Fen Chen, Huan-Wen Chang, Ting-Yu Tai, Wei-Liang Natl Taichung Univ Sci & Technol Dept Comp Sci & Informat Engn Taichung Taiwan Chinese Culture Univ Dept Informat Commun Taipei Taiwan

ISBN: (纸本)9781665404037

Recently, Gupta and Chaudhari proposed an anonymous two factor authentication protocol for roaming service in global mobile networks. They claimed that their scheme could not only ensure strong user anonymity, mutual authentication and perfect forward secrecy but also resist desynchronization attack, password guessing attack, replay attack, and insider attack After analyzing their scheme, we find that it suffers from some flaws. First, the foreign agent cannot determine who the home agent is and whether the received request is for itself or not. Second, some operation cannot be executed by the home agent to record the number of authentication failure. Third, the foreign agent cannot determine whether the message received sent by the home agent is for itself or not. Fourth, a malicious user can mount parallel attack to obtain the unauthorized service. In this paper, we will show how these flaws threaten Gupta and Chaudhari's protocol.

关键词： authentication global roaming mobility networks user anonymity parallel attack

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21st international conference on parallel and distributed computing, Applications, and Technologies, PDCAT 2020

21st International Conference on Parallel and Distributed Co...

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21st international conference on parallel and distributed computing, Applications, and Technologies, PDCAT 2020

ISBN: (纸本)9783030692438

The proceedings contain 35 papers. The special focus in this conference is on parallel and distributed computing, Applications, and Technologies. The topics include: On the Non-ergodic Convergence Rate of the Directed Nonsmooth Composite Optimization;6D Pose Estimation Based on the Adaptive Weight of RGB-D Feature;blockchain-Based Secure Outsourcing of Fully Homomorphic Encryption Using Hidden Ideal Lattice;multiple Projections Learning for Dimensional Reduction;Preventing DDoS Attacks on Bitcoin Memory Pool by the Dynamic Fee Threshold Mechanism;The Compiler of DFC: A Source Code Converter that Transform the Dataflow Code to the Multi-threaded C Code;online Learning-Based Co-task Dispatching with Function Configuration in Edge computing;System-Level FPGA Routing for Logic Verification with Time-Division Multiplexing;protein Interresidue Contact Prediction Based on Deep Learning and Massive Features from Multi-sequence Alignment;heterogeneous Software Effort Estimation via Cascaded Adversarial Auto-Encoder;see Fine Color from the Rough Black-and-White;data Aggregation Aware Routing for distributed Training;a New Integer Programming Model for the File Transfer Scheduling Problem;approximation Algorithms for the General Cluster Routing Problem;maximizing Group Coverage in Social networks;lightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification;the Hybrid Navigation Method in Face of Dynamic Obstacles;a Relaxed Balanced Lock-Free Binary Search Tree;A Dynamic Parameter Tuning Method for High Performance SpMM;data Caching Based Transfer Optimization in Large Scale networks;a Novel distributed Reinforcement Learning Method for Classical Chinese Poetry Generation;second-Order Convolutional Neural Network Based on Cholesky Compression Strategy;submodular Maximization with Bounded Marginal Values;the Prize-Collecting k-Steiner Tree Problem.

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FC+: Near-optimal Deadlock-free Expander Data Center networks

FC+: Near-optimal Deadlock-free Expander Data Center Network...

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

作者： Xiao Zhang Peirui Cao Yongxi Lyu Qizhou Zhang Shizhen Zhao Xinbing Wang Chenghu Zhou Shanghai Jiao Tong university Chinese Academy of Sciences

Expander networks have gained attention as a cost-efficient alternative to expensive Clos networks in data centers. However, they face challenges with deadlocks caused by the widespread deployment of PFC-enabled RoCE networks. Unfortunately, current methods to address deadlocks in expander networks often come with drawbacks that either compromise performance or fail to completely eliminate deadlocks. After identifying path diversity as the performance bottleneck in FC (Flatten Clos), we present FC+ (Flatten Clos Plus), a topology-routing co-design to eliminate deadlocks and achieve near-optimal performance. Similar to FC, FC+ also maps its topology to a multi-layered virtual topology and performs up-down routing to eliminate deadlocks. Based on this, FC+ introduces 2 new designs that can effectively improve path diversity. First, FC+ adopts a non-uniform virtual multi-layer design, which greatly increases the number of deadlock-free paths. Second, FC+ uses deadlock-free K-Shortest Paths (DFKSP) for routing, utilizing the path diversity better. We perform throughput evaluation under different traffic patterns. With 1 lossless priority, FC+ consistently outperforms FC and the performance enhancement reaches 1.4x to 2x under near-worst case. Another advantage of FC+ over FC is that FC+’s DF-KSP routing allows using more than 1 lossless priorities to further improve performance. Compared to Tagger, the state-of-the-art lossless priority management method to avoid deadlocks, FC+ reduces the number of lossless priorities from 3 to 2 in order to achieve near-optimal performance.

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PACTS: Power-Aware Charging Time Scheduling in Wireless Rechargeable UAV networks

PACTS: Power-Aware Charging Time Scheduling in Wireless Rech...

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40th IEEE international Performance, computing, and Communications conference (IPCCC)

作者： Li, Mengqi Liu, Liang Wang, Yanlin Xi, Jie Wang, Lisong Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781665443319

Recently, wireless power transmission technology (WPT) is used in wireless rechargeable unmanned aerial vehicle (UAV) networks (WRUNs) to replenish energy for UAVs. In WRUN, if UAVs cannot replenish energy in time, the task will fail or the task completion time will be greatly prolonged. So, when and how long should the wireless static chargers (WSCs) be scheduled to replenish energy for UAVs while the energy utilization rate of WSCs can be optimized is very critical. However, the existing methods only optimize the charging time periods, that is the time durations during which WSCs release energy, ignoring the impact of the adjustable source power of WSCs. In fact, inappropriate source power will lead to the low energy utilization rate of WSCs. This paper takes both the charging time periods and the adjustable source power of WSCs into account and formulates the power-aware charging time scheduling (PACTS) problem as an optimization problem. Namely, how to decide the optimal charging time periods and the appropriate source power in each charging time period simultaneously to improve the energy utilization rate of WSCs. To solve PACTS, we first discretize the flight paths of UAVs spatiotemporally and then present a novel method to reformulate PACTS as a binary integer programming (BIP) problem that can be solved by existing algorithms. Finally, experiments are conducted to evaluate the performance of our scheme.

关键词： UAV networks wireless charging UAV charging binary integer programming power-aware

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Scheduling tasks based on branch and bound algorithm in cloud computing environment 8

Scheduling tasks based on branch and bound algorithm in clou...

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8th international conference on Signal Processing and Integrated networks, SPIN 2021

作者： Singh, Pardeep SoCS University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9781665435642

Cloud computing is an ideal way for large-scale distributed computing and parallel processing. Cloud computing supports a vast number of services which covers a large number of consumer services like the cloud backup of the images, videos in the smartphone, etc. The performance and efficiency of services provided by cloud computing are dependent on the execution time of user tasks presented to the cloud system. Efficient scheduling of user tasks plays a significant role in managing the physical and virtual resources with a better performance in cloud services. Task Scheduling is one of the main types of scheduling performed in a cloud environment that aim to minimize the makespan for the task processing. Makespan means the total time taken by the virtual machines to complete the tasks allocated to them. In heterogeneous system scheduling the different size tasks of different significance is a complex problem that has been tried to resolve with various approaches e.g. FCFS, SJF, Min-Min, Max-Min, etc. In this work, the Branch and Bound (B&B) algorithm has been implemented and tested for assigning these heterogeneous tasks to virtual machines to reduce the makespan. It has configured the environment in the CloudSim simulator and obtained the results mainly about makespan. Results are compared with other general scheduling algorithms i.e. FCFS, MIN-MIN, MAX-MIN, and SJF. © 2021 IEEE

关键词： Cloud computing

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DCNN-based load balancing and computation offloading of Intelligent Video Surveillance with Edge computing

DCNN-based load balancing and computation offloading of Inte...

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Evolutionary Algorithms and Soft computing Techniques (EASCT), international conference on

作者： Shweta Koparde G. Sujatha Srinivas Jangirala P. Santhosh Kumar Computer Engineering Dr. DY Patil Institute of Technology Pune India Computer Science and Engineering Kommuri Pratap Reddy Institute of Technology Ghatkeshar India Jindal Global Business School O.P. Jindal Global University Sonipat India Information Technology College of Engineering and Technology SRM Institute of Science and Technology Ramapuram Chennai India

In this study, we offer a distributed Intelligent Video Surveillance (DIVS) system that is set up in an environment of edge computing and is based on Deep Learning (DL). For the DIVS system, we developed a distributed DL training model and a multi-layer edge computing architecture. In order to lower the high network connection overhead and offer precise and low-latency video analysis solutions, the DIVS system may move computing processes from the network core to the network edges. We implement the proposed DIVS system to address the workload balance, model synchronization, and concurrent training problems. We offer task-level parallel and model-level parallel training approaches to further accelerate the video analysis process. Deep Convolutional Neural networks with Load Balancing and Computation Offloading (DCNN-LBCO) is a technique for creating a scalable cloud-based online video platform. In order to minimize security concerns, we provide a particular security layer for MEC systems in this study along with a load balancing and computation offloading technique that work together. A load balancing and computation offloading (LBCO) technique is first recommended in order to distribute MDU among sBSs effectively. A specific advanced encryption standard (AES) cryptographic method is also provided, together with an encryption and decryption key based on electrocardiogram (ECG) signals, to prevent data vulnerability during transmission. A load balancing, compute offloading, and security model that is integrated is provided to lessen the system's time and energy requirements. Extensive testing results show that, as compared to local execution, our technique may save between 68.2% and 72.4% of system utilisation, both with and without extra security layers.

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JointCloud Cross-chain Verification Model of Decentralized Identifiers

JointCloud Cross-chain Verification Model of Decentralized I...

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40th IEEE international Performance, computing, and Communications conference (IPCCC)

作者： Zhong, Tao Shi, Peichang Chang, Junsheng Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Proc Lab PDL Changsha Peoples R China Natl Univ Def Technol Coll Comp Inst Comp Technol Changsha Peoples R China

ISBN: (纸本)9781665443319

When multiple entities communicate or collaborate in JointCloud, identities are the very prior basis to build trust with each other. Decentralized identifier (DID) can provide a trusted identity with blockchain technology and a complete method of identity verification based on verifiable credentials, which solves problems of conventional centralized identity. However, current DIDs can only conduct verification within a single blockchain, which limits the interoperability of DIDs on different blockchains. Network isolation hinders the verification of DIDs on different blockchains and thus there is a need to break the barrier between blockchains. In this paper, we propose a model to conduct cross-chain verification of DIDs. We build a system of credit evaluation to describe the credibility of DIDs in a unified way and deploy smart contracts to implement cross-chain verification of DIDs. Experimental results verifies the feasibility of the model, which realizes cross-chain verification of DIDs in the networks of blockchain.

关键词： Decentralized identifier cross-chain verification blockchain

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GraVAC: Adaptive Compression for Communication-Efficient distributed DL Training

GraVAC: Adaptive Compression for Communication-Efficient Dis...

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IEEE international conference on Cloud computing, CLOUD

作者： Sahil Tyagi Martin Swany Indiana University Bloomington USA

distributed data-parallel (DDP) training improves overall application throughput as multiple devices train on a subset of data and aggregate updates to produce a globally shared model. The periodic synchronization at each iteration incurs considerable overhead, exacerbated by the increasing size and complexity of state-of-the-art neural networks. Although many gradient compression techniques propose to reduce communication cost, the ideal compression factor that leads to maximum speedup or minimum data exchange remains an open-ended problem since it varies with the quality of compression, model size and structure, hardware, network topology and bandwidth. We propose GraVAC, a framework to dynamically adjust compression factor throughout training by evaluating model progress and assessing gradient information loss associated with compression. GraVAC works in an online, black-box manner without any prior assumptions about a model or its hyperparameters, while achieving the same or better accuracy than dense SGD (i.e., no compression) in the same number of iterations/epochs. As opposed to using a static compression factor, GraVAC reduces end-to-end training time for ResNet101, VGG16 and LSTM by 4.32×, 1.95× and 6.67× respectively. Compared to other adaptive schemes, our framework provides 1.94× to 5.63× overall speedup.

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On Developing Smart IoT-Based distributed System for Remote Detection of Slope Movements

On Developing Smart IoT-Based Distributed System for Remote ...

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international conference on computing, Networking and Communications (ICNC)

作者： Rahul Debnath Ahmed Abdelmoamen Ahmed Md Jobair Bin Alam Department of Computer Science Prairie View A&M University Prairie View TX USA Department of Civil Engineering Prairie View A&M University Prairie View TX USA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

Landslides or slope failures cause significant damage to the transportation infrastructure worldwide, resulting in billions of dollars of property damage. The initial slope movement of highway slopes, caused by environmental factors and man-made activities, must be detected early to prevent significant losses in infrastructure damages and human lives. However, traditional monitoring approaches for slope movement, such as periodic human inspection and surveying, are labor-intensive and time-consuming. With the advent of the Internet of Things (IoT) and high-speed internet, the potential to use such technologies for monitoring remote earth movement of highway embankments is becoming increasingly important. This paper presents an IoT-based distributed system for the early detection of slope movements to assist transportation authorities in immediately repairing the slope before a major failure occurs. We built a laboratory prototype embankment, equipped with various IoT sensors (e.g., tilt, moisture, and tensiometer sensors), that simulate a failure-prone slope to evaluate the effectiveness of our system. A user-friendly GUI is developed to enable users to utilize the system remotely. The collected sensory data for four months allowed us to analyze the soil hydrologic dynamics and the slope performance by detecting changes in the slope tilting both parallel (pitch) and perpendicular (roll). The experimental results showed that our system could detect negligible movement (1mm) and rotation (0.02°) of slopes.

关键词： Road transportation Working environment noise Transportation Artificial neural networks Sensor systems Vectors Internet of Things Remote monitoring Intelligent sensors Graphical user interfaces

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PRACTICAL MASSIVELY parallel MONTE-CARLO TREE SEARCH APPLIED TO MOLECULAR DESIGN 9

PRACTICAL MASSIVELY PARALLEL MONTE-CARLO TREE SEARCH APPLIED...

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9th international conference on Learning Representations, ICLR 2021

作者： Yang, Xiufeng Aasawat, Tanuj Kr Yoshizoe, Kazuki Chugai Pharmaceutical Co. Ltd Japan Parallel Computing Lab - India Intel Labs India RIKEN Center for Advanced Intelligence Project Japan

It is common practice to use large computational resources to train neural networks, known from many examples, such as reinforcement learning applications. However, while massively parallel computing is often used for training models, it is rarely used to search solutions for combinatorial optimization problems. This paper proposes a novel massively parallel Monte-Carlo Tree Search (MP-MCTS) algorithm that works efficiently for a 1,000 worker scale on a distributed memory environment using multiple compute nodes and applies it to molecular design. This paper is the first work that applies distributed MCTS to a real-world and non-game problem. Existing works on large-scale parallel MCTS show efficient scalability in terms of the number of rollouts up to 100 workers. Still, they suffer from the degradation in the quality of the solutions. MP-MCTS maintains the search quality at a larger scale. By running MP-MCTS on 256 CPU cores for only 10 minutes, we obtained candidate molecules with similar scores to non-parallel MCTS running for 42 hours. Moreover, our results based on parallel MCTS (combined with a simple RNN model) significantly outperform existing state-of-the-art work. Our method is generic and is expected to speed up other applications of MCTS. © 2021 ICLR 2021 - 9th international conference on Learning Representations. All rights reserved.

关键词： Reinforcement learning

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