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Articles 241 - 270 of 322
Full-Text Articles in OS and Networks
Proteus: Computing Disjunctive Loop Summary Via Path Dependency Analysis, Xiaofei Xie, Bihuan Chen, Yang Liu, Wei Le, Xiaohong Li
Proteus: Computing Disjunctive Loop Summary Via Path Dependency Analysis, Xiaofei Xie, Bihuan Chen, Yang Liu, Wei Le, Xiaohong Li
Research Collection School Of Computing and Information Systems
Loops are challenging structures for program analysis, especially when loops contain multiple paths with complex interleaving executions among these paths. In this paper, we first propose a classification of multi-path loops to understand the complexity of the loop execution, which is based on the variable updates on the loop conditions and the execution order of the loop paths. Secondly, we propose a loop analysis framework, named Proteus, which takes a loop program and a set of variables of interest as inputs and summarizes path-sensitive loop effects on the variables. The key contribution is to use a path dependency automaton (PDA) …
Ra2: Predicting Simulation Execution Time For Cloud-Based Design Space Explorations, Nguyen Binh Duong Ta, Wentong Cai, Zengxiang Li, Suiping Zhou
Ra2: Predicting Simulation Execution Time For Cloud-Based Design Space Explorations, Nguyen Binh Duong Ta, Wentong Cai, Zengxiang Li, Suiping Zhou
Research Collection School Of Computing and Information Systems
Design space exploration refers to the evaluation of implementation alternatives for many engineering and design problems. A popular exploration approach is to run a large number of simulations of the actual system with varying sets of configuration parameters to search for the optimal ones. Due to the potentially huge resource requirements, cloud-based simulation execution strategies should be considered in many cases. In this paper, we look at the issue of running largescale simulation-based design space exploration problems on commercial Infrastructure-as-a-Service clouds, namely Amazon EC2, Microsoft Azure and Google Compute Engine. To efficiently manage cloud resources used for execution, the key …
Towards Autonomous Behavior Learning Of Non-Player Characters In Games, Shu Feng, Ah-Hwee Tan
Towards Autonomous Behavior Learning Of Non-Player Characters In Games, Shu Feng, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Non-Player-Characters (NPCs), as found in computer games, can be modelled as intelligent systems, which serve to improve the interactivity and playability of the games. Although reinforcement learning (RL) has been a promising approach to creating the behavior models of non-player characters (NPC), an initial stage of exploration and low performance is typically required. On the other hand, imitative learning (IL) is an effective approach to pre-building a NPC’s behavior model by observing the opponent’s actions, but learning by imitation limits the agent’s performance to that of its opponents. In view of their complementary strengths, this paper proposes a computational model …
Edit Distance Based Encryption And Its Application, Tran Viet Xuan Phuong, Guomin Yang, Willy Susilo, Kaitai Liang
Edit Distance Based Encryption And Its Application, Tran Viet Xuan Phuong, Guomin Yang, Willy Susilo, Kaitai Liang
Research Collection School Of Computing and Information Systems
Edit distance, also known as Levenshtein distance, is a very useful tool to measure the similarity between two strings. It has been widely used in many applications such as natural language processing and bioinformatics. In this paper, we introduce a new type of fuzzy public key encryption called Edit Distance-based Encryption (EDE). In EDE, the encryptor can specify an alphabet string and a threshold when encrypting a message, and a decryptor can obtain a decryption key generated from another alphabet string, and the decryption will be successful if and only if the edit distance between the two strings is within …
Self-Regulated Incremental Clustering With Focused Preferences, Di Wang, Ah-Hwee Tan
Self-Regulated Incremental Clustering With Focused Preferences, Di Wang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Due to their online learning nature, incremental clustering techniques can handle a continuous stream of data. In particular, various incremental clustering techniques based on Adaptive Resonance Theory (ART) have been shown to have low computational complexity in adaptive learning and are less sensitive to noisy information. However, parameter regularization in existing ART clustering techniques is applied either on different features or on different clusters exclusively. In this paper, we introduce Interest-Focused Clustering based on Adaptive Resonance Theory (IFC-ART), which self-regulates the vigilance parameter associated with each feature and each cluster. As such, we can incorporate the domain knowledge of the …
Passively Testing Routing Protocols In Wireless Sensor Networks, Xiaoping Che, Stephane Maag, Hwee-Xian Tan, Hwee-Pink Tan
Passively Testing Routing Protocols In Wireless Sensor Networks, Xiaoping Che, Stephane Maag, Hwee-Xian Tan, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
Smart systems are today increasingly developed with the number of wireless sensor devices that drastically increases. They are implemented within several contexts through our environment. Thus, sensed data transported in ubiquitous systems are important and the way to carry them must be efficient and reliable. For that purpose, several routing protocols have been proposed to wireless sensor networks (WSN). However, one stage that is often neglected before their deployment, is the conformance testing process, a crucial and challenging step. Active testing techniques commonly used in wired networks are not suitable to WSN and passive approaches are needed. While some works …
Semantic Memory Modeling And Memory Interaction In Learning Agents, Wenwen Wang, Ah-Hwee Tan, Loo-Nin Teow
Semantic Memory Modeling And Memory Interaction In Learning Agents, Wenwen Wang, Ah-Hwee Tan, Loo-Nin Teow
Research Collection School Of Computing and Information Systems
Semantic memory plays a critical role in reasoning and decision making. It enables an agent to abstract useful knowledge learned from its past experience. Based on an extension of fusion adaptive resonance theory network, this paper presents a novel self-organizing memory model to represent and learn various types of semantic knowledge in a unified manner. The proposed model, called fusion adaptive resonance theory for multimemory learning, incorporates a set of neural processes, through which it may transfer knowledge and cooperate with other long-term memory systems, including episodic memory and procedural memory. Specifically, we present a generic learning process, under which …
Robust Decision Making For Stochastic Network Design, Akshat Kumar, Arambam James Singh, Pradeep Varakantham, Daniel Sheldon
Robust Decision Making For Stochastic Network Design, Akshat Kumar, Arambam James Singh, Pradeep Varakantham, Daniel Sheldon
Research Collection School Of Computing and Information Systems
We address the problem of robust decision making for stochastic network design. Our work is motivated by spatial conservation planning where the goal is to take management decisions within a fixed budget to maximize the expected spread of a population of species over a network of land parcels. Most previous work for this problem assumes that accurate estimates of different network parameters (edge activation probabilities, habitat suitability scores) are available, which is an unrealistic assumption. To address this shortcoming, we assume that network parameters are only partially known, specified via interval bounds. We then develop a decision making approach that …
Nlu Framework For Voice Enabling Non-Native Applications On Smart Devices, Soujanya Lanka, Deepika Panthania, Pooja Kushalappa, Pradeep Varakantham
Nlu Framework For Voice Enabling Non-Native Applications On Smart Devices, Soujanya Lanka, Deepika Panthania, Pooja Kushalappa, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Voice is a critical user interface on smart devices (wearables, phones, speakers, televisions) to access applications (or services) available on them. Unfortunately, only a few native applications (provided by the OS developer) are typically voice enabled in devices of today. Since, the utility of a smart device is determined more by the strength of external applications developed for the device, voice enabling non-native applications in a scalable, seamless manner within the device is a critical use case and is the focus of our work. We have developed a Natural Language Understanding (NLU) framework that uses templates supported by the application …
A Misspecification Test For Logit Based Route Choice Models, Tien Mai, Emma Frejinger, Fabian Bastin
A Misspecification Test For Logit Based Route Choice Models, Tien Mai, Emma Frejinger, Fabian Bastin
Research Collection School Of Computing and Information Systems
The multinomial logit (MNL) model is often used for analyzing route choices in real networks in spite of the fact that path utilities are believed to be correlated. Yet, statistical tests for model misspecification are rarely used. This paper shows how the information matrix test for model misspecification proposed byWhite (1982) can be applied to test path-based and link-based MNL route choice models.We present a Monte Carlo experiment using simulated data to assess the size and the power of the test and to compare its performance with the IIA (Hausman and McFadden, 1984) and McFadden–Train Lagrange multiplier (McFadden and Train, …
Adaptive Scaling Of Cluster Boundaries For Large-Scale Social Media Data Clustering, Lei Meng, Ah-Hwee Tan, Donald C. Wunsch
Adaptive Scaling Of Cluster Boundaries For Large-Scale Social Media Data Clustering, Lei Meng, Ah-Hwee Tan, Donald C. Wunsch
Research Collection School Of Computing and Information Systems
The large scale and complex nature of social media data raises the need to scale clustering techniques to big data and make them capable of automatically identifying data clusters with few empirical settings. In this paper, we present our investigation and three algorithms based on the fuzzy adaptive resonance theory (Fuzzy ART) that have linear computational complexity, use a single parameter, i.e., the vigilance parameter to identify data clusters, and are robust to modest parameter settings. The contribution of this paper lies in two aspects. First, we theoretically demonstrate how complement coding, commonly known as a normalization method, changes the …
Bring-Your-Own-Application (Byoa): Optimal Stochastic Application Migration In Mobile Cloud Computing, Jonathan David Chase, Dusit Niyato, Sivadon Chaisiri
Bring-Your-Own-Application (Byoa): Optimal Stochastic Application Migration In Mobile Cloud Computing, Jonathan David Chase, Dusit Niyato, Sivadon Chaisiri
Research Collection School Of Computing and Information Systems
The increasing popularity of using mobile devices in a work context, has led to the need to be able to support more powerful computation. Users no longer remain in an office or at home to conduct their activities, preferring libraries and cafes. In this paper, we consider a mobile cloud computing scenario in which users bring their own mobile devices and are offered a variety of equipment, e.g., desktop computer, smart- TV, or projector, to migrate their applications to, so as to save battery life, improve usability and performance. We formulate a stochastic optimization problem to optimize the allocation of …
Fast Reinforcement Learning Under Uncertainties With Self-Organizing Neural Networks, Teck-Hou Teng, Ah-Hwee Tan
Fast Reinforcement Learning Under Uncertainties With Self-Organizing Neural Networks, Teck-Hou Teng, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Using feedback signals from the environment, a reinforcement learning (RL) system typically discovers action policies that recommend actions effective to the states based on a Q-value function. However, uncertainties over the estimation of the Q-values can delay the convergence of RL. For fast RL convergence by accounting for such uncertainties, this paper proposes several enhancements to the estimation and learning of the Q-value using a self-organizing neural network. Specifically, a temporal difference method known as Q-learning is complemented by a Q-value Polarization procedure, which contrasts the Q-values using feedback signals on the effect of the recommended actions. The polarized Q-values …
Learning And Controlling Network Diffusion In Dependent Cascade Models, Jiali Du, Pradeep Varakantham, Akshat Kumar, Shih-Fen Cheng
Learning And Controlling Network Diffusion In Dependent Cascade Models, Jiali Du, Pradeep Varakantham, Akshat Kumar, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
Diffusion processes have increasingly been used to represent flow of ideas, traffic and diseases in networks. Learning and controlling the diffusion dynamics through management actions has been studied extensively in the context of independent cascade models, where diffusion on outgoing edges from a node are independent of each other. Our work, in contrast, addresses (a) learning diffusion taking management actions to alter the diffusion dynamics to achieve a desired outcome in dependent cascade models. A key characteristic of such dependent cascade models is the flow preservation at all nodes in the network. For example, traffic and people flow is preserved …
Shopminer: Mining Customer Shopping Behavior In Physical Clothing Stores With Passive Rfids, Longfei Shangguan, Zimu Zhou, Xiaolong Zheng, Lei Yang, Yunhao Liu, Jinsong Han
Shopminer: Mining Customer Shopping Behavior In Physical Clothing Stores With Passive Rfids, Longfei Shangguan, Zimu Zhou, Xiaolong Zheng, Lei Yang, Yunhao Liu, Jinsong Han
Research Collection School Of Computing and Information Systems
Shopping behavior data are of great importance to understand the effectiveness of marketing and merchandising efforts. Online clothing stores are capable capturing customer shopping behavior by analyzing the click stream and customer shopping carts. Retailers with physical clothing stores, however, still lack effective methods to identify comprehensive shopping behaviors. In this paper, we show that backscatter signals of passive RFID tags can be exploited to detect and record how customers browse stores, which items of clothes they pay attention to, and which items of clothes they usually match with. The intuition is that the phase readings of tags attached on …
Endogenous Network Effects, Platform Pricing And Market Liquidity, Mei Lin, Ruhai Wu, Wen Zhou
Endogenous Network Effects, Platform Pricing And Market Liquidity, Mei Lin, Ruhai Wu, Wen Zhou
Research Collection School Of Computing and Information Systems
This paper examines a monopoly platform's two-sided pricing strategies in a setting with seller competition, which gives rise to not only positive cross-side network effects between buyers and sellers, but also a negative same-side network effect among sellers. We show that platform pricing depends crucially on the characteristics associated with market liquidity, which contrasts with the previous studies that point to the two sides' relative demand elasticities and/or network effects. A market is said to be more liquid when it has less friction, resulting in a larger total surplus for the platform economy and hence greater equilibrium entry on both …
Towards A Robust Sparse Data Representation In Wireless Sensor Networks, Abu Alsheik Mohammad, Shaowei Lin, Hwee-Pink Tan, Dusit Niyato
Towards A Robust Sparse Data Representation In Wireless Sensor Networks, Abu Alsheik Mohammad, Shaowei Lin, Hwee-Pink Tan, Dusit Niyato
Research Collection School Of Computing and Information Systems
Compressive sensing has been successfully used for optimized operations in wireless sensor networks. However, raw data collected by sensors may be neither originally sparse nor easily transformed into a sparse data representation. This paper addresses the problem of transforming source data collected by sensor nodes into sparse representation with a few nonzero elements. Our contributions that address three major issues include: 1) an effective method that extracts population sparsity of the data, 2) a sparsity ratio guarantee scheme, and 3) a customized leaerning algorithm of the sparsifying dictionary. We introduce an unsupervised neural network to extract an intrinsic sparse coding …
Replica Placement For Availability In The Worst Case, Peng Li, Debin Gao, Mike Reiter
Replica Placement For Availability In The Worst Case, Peng Li, Debin Gao, Mike Reiter
Research Collection School Of Computing and Information Systems
We explore the problem of placing object replicas on nodes in a distributed system to maximize the number of objects that remain available when node failures occur. In our model, failing (the nodes hosting) a given threshold of replicas is sufficient to disable each object, and the adversary selects which nodes to fail to minimize the number of objects that remain available. We specifically explore placement strategies based on combinatorial structures called t-packings; provide a lower bound for the object availability they offer; show that these placements offer availability that is c-competitive with optimal; propose an efficient algorithm for computing …
Self-Organizing Neural Networks Integrating Domain Knowledge And Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Jacek M. Zurada
Self-Organizing Neural Networks Integrating Domain Knowledge And Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Jacek M. Zurada
Research Collection School Of Computing and Information Systems
The use of domain knowledge in learning systems is expected to improve learning efficiency and reduce model complexity. However, due to the incompatibility with knowledge structure of the learning systems and real-time exploratory nature of reinforcement learning (RL), domain knowledge cannot be inserted directly. In this paper, we show how self-organizing neural networks designed for online and incremental adaptation can integrate domain knowledge and RL. Specifically, symbol-based domain knowledge is translated into numeric patterns before inserting into the self-organizing neural networks. To ensure effective use of domain knowledge, we present an analysis of how the inserted knowledge is used by …
A Nested Recursive Logit Model For Route Choice Analysis, Tien Mai, Mogens Fosgerau, Emma Frejinger
A Nested Recursive Logit Model For Route Choice Analysis, Tien Mai, Mogens Fosgerau, Emma Frejinger
Research Collection School Of Computing and Information Systems
We propose a route choice model that relaxes the independence from irrelevant alternatives property of the logit model by allowing scale parameters to be link specific. Similar to the recursive logit (RL) model proposed by Fosgerau et al. (2013), the choice of path is modeled as a sequence of link choices and the model does not require any sampling of choice sets. Furthermore, the model can be consistently estimated and efficiently used for prediction.A key challenge lies in the computation of the value functions, i.e. the expected maximum utility from any position in the network to a destination. The value …
Measuring Centralities For Transportation Networks Beyond Structures, Yew-Yih Cheng, Lee Ka Wei, Roy, Ee-Peng Lim, Feida Zhu
Measuring Centralities For Transportation Networks Beyond Structures, Yew-Yih Cheng, Lee Ka Wei, Roy, Ee-Peng Lim, Feida Zhu
Research Collection School Of Computing and Information Systems
In an urban city, its transportation network supports efficient flow of people between different parts of the city. Failures in the network can cause major disruptions to commuter and business activities which can result in both significant economic and time losses. In this paper, we investigate the use of centrality measures to determine critical nodes in a transportation network so as to improve the design of the network as well as to devise plans for coping with the network failures. Most centrality measures in social network analysis research unfortunately consider only topological structure of the network and are oblivious of …
Memory Dynamics In Attractor Networks, Guoqi Li, Kiruthika Ramanathan, Ning Ning, Luping Shi, Changyun Wen
Memory Dynamics In Attractor Networks, Guoqi Li, Kiruthika Ramanathan, Ning Ning, Luping Shi, Changyun Wen
Research Collection School Of Computing and Information Systems
As can be represented by neurons and their synaptic connections, attractor networks are widely believed to underlie biological memory systems and have been used extensively in recent years to model the storage and retrieval process of memory. In this paper, we propose a new energy function, which is nonnegative and attains zero values only at the desired memory patterns. An attractor network is designed based on the proposed energy function. It is shown that the desired memory patterns are stored as the stable equilibrium points of the attractor network. To retrieve a memory pattern, an initial stimulus input is presented …
Modeling Neuromorphic Persistent Firing Networks, Ning Ning, Guoqi Li, Wei He, Kejie Huang, Li Pan, Kiruthika Ramanathan, Rong Zhao, Luping Shi
Modeling Neuromorphic Persistent Firing Networks, Ning Ning, Guoqi Li, Wei He, Kejie Huang, Li Pan, Kiruthika Ramanathan, Rong Zhao, Luping Shi
Research Collection School Of Computing and Information Systems
Neurons are believed to be the brain computational engines of the brain. A recent discovery in neurophysiology reveals that interneurons can slowly integrate spiking, share the output across a coupled network of axons and respond with persistent firing even in the absence of input to the soma or dendrites, which has not been understood and could be very important for exploring the mechanism of human cognition. The conventional models are incapable of simulating the important newly-discovered phenomenon of persistent firing induced by axonal slow integration. In this paper, we propose a computationally efficient model of neurons through modeling the axon …
Stability Of Transportation Networks Under Adaptive Routing Policies, Sebastien Boyer, Sebastien Blandin, Laura Wynter
Stability Of Transportation Networks Under Adaptive Routing Policies, Sebastien Boyer, Sebastien Blandin, Laura Wynter
Research Collection School Of Computing and Information Systems
Growing concerns regarding urban congestion, and the recent explosion of mobile devices able to provide real-time information to traffic users have motivated increasing reliance on real-time route guidance for the online management of traffic networks. However, while the theory of traffic equilibria is very well-known, much fewer results exist on the stability of such equilibria, especially in the context of adaptive routing policy. In this work, we consider the problem of characterizing the stability properties of traffic equilibria in the context of online adaptive route choice induced by GPS-based decision making. We first extend the recent framework of “Markovian Traffic …
Efficient Data Compression With Error Bound Guarantee In Wireless Sensor Networks, Mohammad Abu Alsheikh, P. K. Poh, S. Lin, Hwee-Pink Tan, D. Niyato
Efficient Data Compression With Error Bound Guarantee In Wireless Sensor Networks, Mohammad Abu Alsheikh, P. K. Poh, S. Lin, Hwee-Pink Tan, D. Niyato
Research Collection School Of Computing and Information Systems
We present a data compression and dimensionality reduction scheme for data fusion and aggregation applications to prevent data congestion and reduce energy consumption at network connecting points such as cluster heads and gateways. Our in-network approach can be easily tuned to analyze the data temporal or spatial correlation using an unsupervised neural network scheme, namely the autoencoders. In particular, our algorithm extracts intrinsic data features from previously collected historical samples to transform the raw data into a low dimensional representation. Moreover, the proposed framework provides an error bound guarantee mechanism. We evaluate the proposed solution using real-world data sets and …
Integrating Self-Organizing Neural Network And Motivated Learning For Coordinated Multi-Agent Reinforcement Learning In Multi-Stage Stochastic Game, Teck-Hou Teng, Ah-Hwee Tan, Janusz A. Starzyk, Yuan-Sin Tan, Loo-Nin Teow
Integrating Self-Organizing Neural Network And Motivated Learning For Coordinated Multi-Agent Reinforcement Learning In Multi-Stage Stochastic Game, Teck-Hou Teng, Ah-Hwee Tan, Janusz A. Starzyk, Yuan-Sin Tan, Loo-Nin Teow
Research Collection School Of Computing and Information Systems
Most non-trivial problems require the coordinated performance of multiple goal-oriented and time-critical tasks. Coordinating the performance of the tasks is required due to the dependencies among the tasks and the sharing of resources. In this work, an agent learns to perform a task using reinforcement learning with a self-organizing neural network as the function approximator. We propose a novel coordination strategy integrating Motivated Learning (ML) and a self-organizing neural network for multi-agent reinforcement learning (MARL). Specifically, we adapt the ML idea of using pain signal to overcome the resource competition issue. Dependency among the agents is resolved using domain knowledge …
Joint Virtual Machine And Bandwidth Allocation In Software Defined Network (Sdn) And Cloud Computing Environments, Jonathan David Chase, Rakpong Kaewpuang, Wen Yonggang, Dusit Niyato
Joint Virtual Machine And Bandwidth Allocation In Software Defined Network (Sdn) And Cloud Computing Environments, Jonathan David Chase, Rakpong Kaewpuang, Wen Yonggang, Dusit Niyato
Research Collection School Of Computing and Information Systems
Cloud computing provides users with great flexibility when provisioning resources, with cloud providers offering a choice of reservation and on-demand purchasing options. Reservation plans offer cheaper prices, but must be chosen in advance, and therefore must be appropriate to users' requirements. If demand is uncertain, the reservation plan may not be sufficient and on-demand resources have to be provisioned. Previous work focused on optimally placing virtual machines with cloud providers to minimize total cost. However, many applications require large amounts of network bandwidth. Therefore, considering only virtual machines offers an incomplete view of the system. Exploiting recent developments in software …
Optimal Performance Trade-Offs In Mac For Wireless Sensor Networks Powered By Heterogeneous Ambient Energy Harvesting, Jin Yunye, Hwee-Pink Tan
Optimal Performance Trade-Offs In Mac For Wireless Sensor Networks Powered By Heterogeneous Ambient Energy Harvesting, Jin Yunye, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
In wireless sensor networks powered by ambient energy harvesting (WSNs-HEAP), sensor nodes' energy harvesting rates are spatially heterogeneous and temporally variant, which impose difficulties for medium access control (MAC). In this paper, we first derive the necessary conditions under which channel utilization and fairness are optimal in a WSN-HEAP, respectively. Based on the analysis, we propose an earliest deadline first (EDF) polling MAC protocol, which regulates transmission sequence of the sensor nodes based on the spatially heterogeneous energy harvesting rates. It also mitigates temporal variations in energy harvesting rates by a prediction and update mechanism. Simulation results verify the performance …
Ubiquitous Data-Centric Sensor Networks, Ting Yang, Peng Yung Woo, Zhaoxia Wang, Javid Taheri, Hoong Chor Chin, Guaqiang Hu
Ubiquitous Data-Centric Sensor Networks, Ting Yang, Peng Yung Woo, Zhaoxia Wang, Javid Taheri, Hoong Chor Chin, Guaqiang Hu
Research Collection School Of Computing and Information Systems
Ubiquitous data-centric sensor networks (U-DCSN) are a new integrated science and technology, which focus on data instead of individual sensor nodes. The network, as a dynamic database system, can accurately acquire data, perform high-performance processing of big data, and effectively access data from different users/actuators. Due this characteristic, U-DCSN hold huge potentials on service improvement in a wide range of applications and have attracted significant attention in recent years, for example, mobile cloud and consumer electronics. The modern mobile cloud, comprised of mobile devices (smart phones, tablets, and embedded sensor nodes), provides unlimited information resources, putting “cloud into a pocket.” …
A Robust Smart Card-Based Anonymous User Authentication Protocol For Wireless Communications, Fengton Wen, Willy Susilo, Guomin Yang
A Robust Smart Card-Based Anonymous User Authentication Protocol For Wireless Communications, Fengton Wen, Willy Susilo, Guomin Yang
Research Collection School Of Computing and Information Systems
Anonymous user authentication is an important but challenging task for wireless communications. In a recent paper, Das proposed a smart cardï based anonymous user authentication protocol for wireless communications. The scheme can protect user privacy and is believed to be secure against a range of network attacks even if the secret information stored in the smart card is compromised. In this paper, we reanalyze the security of Das' scheme, and show that the scheme is in fact insecure against impersonation attacks. We then propose a new smart cardï based anonymous user authentication protocol for wireless communications. Compared with the existing …