Proof-of-Authorization (PoA) consensus algorithms are widely used in permissioned blockchain networks due to their high throughput , security, and efficiency. However, PoA is susceptible to cloning attacks, where attackers copy the authenticator identity and key, thereby compromising the consensus integrity. This study proposes a novel randomized authenticator within the PoA framework to mitigate cloning attacks and solve the leader selection bottleneck. The main contributions include 1) Introducing unpredictability in leader selection through Verifiable Random Functions (VRFs) to prevent identity duplication.2) Dynamic group management using a hierarchical decentralized architecture of distributed ledgers that balances security and performance.3) Using threshold signatures to avoid a single point of failure among validators.4) Comprehensively analyzing attacks, security, randomness, and availability.5) Evaluating the effectiveness of a randomized authenticator by means of OMNET++ simulations to assess efficiency. By integrating randomness into leader selection and robust consensus design, the approach enables reliable and secure dynamic group management in decentralized networks.

Malicious binaries have caused data and monetary loss to people, and these binaries keep evolving rapidly nowadays. With tons of new unknown attack binaries, one essential daily task for security analysts and researchers is to analyze and effectively identify malicious parts and report the critical behaviors within the binaries. While manual analysis is slow and ineffective, automated malware report generation is a long-term goal for malware analysts and researchers. This study moves one step toward the goal by identifying essential functions in malicious binaries to accelerate and even automate the analyzing process. We design and implement an expert system based on our proposed graph neural network called MalwareExpert. The system pinpoints the essential functions of an analyzed sample and visualizes the relationships between involved parts. We evaluate our proposed approach using executable binaries in the Windows operating system. The evaluation results show that our approach has a competitive detection performance (97.3% accuracy and 96.5% recall rate) compared to existing malware detection models. Moreover, it gives an intuitive and easy-to-understand explanation of the model predictions by visualizing and correlating essential functions. We compare the identified essential functions reported by our system against several expert-made malware analysis reports from multiple sources. Our qualitative and quantitative analyses show that the pinpointed functions indicate accurate directions. In the best case, the top 2% of functions reported from the system can cover all expert-annotated functions in three steps. We believe that the MalwareExpert system has shed light on automated program behavior analysis.

Few-shot learning (FSL) refers to the learning task that generalizes from base to novel concepts with only few examples observed during training. One intuitive FSL approach is to hallucinate additional training samples for novel categories. While this is typically done by learning from a disjoint set of base categories with sufficient amount of training data, most existing works did not fully exploit the intra-class information from base categories, and thus there is no guarantee that the hallucinated data would represent the class of interest accordingly. In this paper, we propose Feature Disentanglement and Hallucination Network (FDH-Net), which jointly performs feature disentanglement and hallucination for FSL purposes. More specifically, our FDH-Net is able to disentangle input visual data into class-specific and appearance-specific features. With both data recovery and classification constraints, hallucination of image features for novel categories using appearance information extracted from base categories can be achieved. We perform extensive experiments on two fine-grained datasets (CUB and FLO) and two coarse-grained ones (mini-ImageNet and CIFAR-100). The results confirm that our framework performs favorably against state-of-the-art metric-learning and hallucination-based FSL models.

More and more malicious apps and APT attacks now target macOS, making it crucial for researchers to develop threat countermeasures on macOS. In this paper, we attempt to construct a macOS cyber range for the evaluation of red team and blue team performances. Our proposed system is composed of three fundamental components: an attack-defense association graph, a Go language-based red team emulation tool, and a toolkit for blue team performance evaluation. We demonstrate the effectiveness of our proposed cyber range with real-world scenarios, and believe it will stimulate more research innovations on threat analysis for macOS.

Few-shot learning (FSL) addresses learning tasks in which only few samples are available for selected object categories. In this paper, we propose a deep learning framework for data hallucination, which overcomes the above limitation and alleviate possible overfitting problems. In particular, our method exploits semantic information into the hallucination process, and thus the augmented data would be able to exhibit semantics-oriented modes of variation for improved FSL performances. Very promising performances on CIFAR-100 and AwA datasets confirm the effectiveness of our proposed method for FSL.

Although live video communication is widely used, it is generally less engaging than face-to-face communication because of limitations on social, emotional, and haptic feedback. Missing eye contact is one such problem caused by the physical deviation between the screen and camera on a device. Manipulating video frames to correct eye gaze is a solution to this problem. In this article, we introduce a system to rotate the eyeball of a local participant before the video frame is sent to the remote side. It adopts a warping-based convolutional neural network to relocate pixels in eye regions. To improve visual quality, we minimize the L2 distance between the ground truths and warped eyes. We also present several newly designed loss functions to help network training. These new loss functions are designed to preserve the shape of eye structures and minimize color changes around the periphery of eye regions. To evaluate the presented network and loss functions, we objectively and subjectively compared results generated by our system and the state-of-the-art, DeepWarp, in relation to two datasets. The experimental results demonstrated the effectiveness of our system. In addition, we showed that our system can perform eye-gaze correction in real time on a consumer-level laptop. Because of the quality and efficiency of the system, gaze correction by postprocessing through this system is a feasible solution to the problem of missing eye contact in video communication.

Retaining eye contact of remote users is a critical issue in video conferencing systems because of parallax caused by the physical distance between a screen and a camera. To achieve this objective, we present a real-time gaze redirection system called Flx-gaze to post-process each video frame before sending it to the remote end. Specifically, we relocate and relight the pixels representing eyes by using a convolutional neural network (CNN). To prevent visual artifacts during manipulation, we minimize not only the L2 loss function but also four novel loss functions when training the network. Two of them retain the rigidity of eyeballs and eyelids; and the other two prevent color discontinuity on the eye peripheries. By leveraging the CPU and the GPU resources, our implementation achieves real-time performance (i.e., 31 frames per second). Experimental results show that the gazes redirected by our system are of high quality under this restrict time constraint. We also conducted an objective evaluation of our system by measuring the peak signal-to-noise ratio (PSNR) between the real and the synthesized images.

Cloud storage services have become increasingly popular. Because of the importance of privacy, many cloud storage encryption schemes have been proposed to protect data from those who do not have access. All such schemes assumed that cloud storage providers are safe and cannot be hacked; however, in practice, some authorities (i.e., coercers) may force cloud storage providers to reveal user secrets or confidential data on the cloud, thus altogether circumventing storage encryption schemes. In this paper, we present our design for a new cloud storage encryption scheme that enables cloud storage providers to create convincing fake user secrets to protect user privacy. Since coercers cannot tell if obtained secrets are true or not, the cloud storage providers ensure that user privacy is still securely protected.

Foveated rendering leverages human visual system to increase video quality under limited computing resources for Virtual Reality (VR). More specifically, it increases the frame rate and the video quality of the foveal vision via lowering the resolution of the peripheral vision. Optimizing foveated rendering systems is, however, not an easy task, because there are numerous parameters that need to be carefully chosen, such as the number of layers, the eccentricity degrees, and the resolution of the peripheral region. Furthermore, there is no standard and efficient way to evaluate the Quality of Experiment (QoE) of foveated rendering systems. In this paper, we propose a framework to compare the performance of different subjective assessment methods on foveated rendering systems. We consider two performance metrics: efficiency and consistency, using the perceptual ratio, which is the probability of the foveated rendering is perceivable by users. A regression model is proposed to model the relationship between the human perceived quality and foveated rendering parameters. Our comprehensive study and analysis reveal several insights: 1) there is no absolute superior subjective assessment method, 2) subjects need to make more observations to confirm the foveated rendering is imperceptible than perceptible, 3) subjects barely notice the foveated rendering with an eccentricity degree of 7.5 degrees+ and peripheral region of a resolution of 540p+, and 4) QoE levels are highly dependent on the individuals and scenes. Our findings are crucial for optimizing the foveated rendering systems for future VR applications.

With the emergence of social media sites such as Twitter and Facebook, channels have opened for citizens to publish an increasing and varied amount of personalized political information. In addition, social media has enabled users to broaden their political knowledge base through online interactions with individuals or groups. This study examines the ideological score of >0.2 million Facebook pages in Taiwan, where politics has long been polarized between the two major parties the Kuomintang (KMT) and the Democratic Progressive Party (DPP). This paper first measures users ideological scores along a Pro-KMT and Pro-DPP spectrum on the basis of their endorsements of political celebrities’ Facebook pages. Second, due to the one-dimensional polarization in Taiwanese politics, we assume that a user’s ideological position may lead them to endorse other Facebook pages that publish and share similar ideological content. Finally, based on this assumption, we estimate the ideological scores of the collected Facebook pages. Our results do not only present a political ideology estimation of pages with different perspectives (popularity, politicalness and category), they also propose applications of identifying potential political and conflicting/sarcastic pages.

With the rapid development of the Internet, several emerging technologies are adopted to construct fancy, interactive, and user-friendly websites. Among these technologies, HTML5 is a popular one and is widely used in establishing modern sites. However, the security issues in the new web technologies are also raised and are worthy of investigation. For vulnerability investigation, many previous studies used fuzzing and focused on generation-based approaches to produce test cases for fuzzing; however, these methods require a significant amount of knowledge and mental efforts to develop test patterns for generating test cases. To decrease the entry barrier of conducting fuzzing, in this study, we propose a test pattern generation algorithm based on the concept of finite state machines. We apply graph analysis techniques to extract paths from finite state machines and use these paths to construct test patterns automatically. According to the proposal, fuzzing can be completed through inputting a regular expression corresponding to the test target. To evaluate the performance of our proposal, we conduct an experiment in identifying vulnerabilities of the input attributes in HTML5. According to the results, our approach is not only efficient but also effective for identifying weak validators in HTML5.

Recent years, with the rapid development of the online social services, people are able to publish and share their opinions, activities, and feelings of their everyday lives as well as public issues on the Internet. By the increasingly large amount of data among online social services, researchers explore approaches to understand how human beings demonstrate their political tendency from the data. In this paper, we focus on boosting election prediction accuracy by leveraging the power of crowd wisdom among social media. In addition to previous proposed approaches, we conduct Chinese sentiment analysis and use peer-to-peer rating records among the social network to establish a better prediction model. To verify our proposal, we validate our methodology by the 2014 Taiwanese local elections. We use the data collected from the largest forum in Taiwan during the last three months election campaign. From the results, our proposal outperforms previous approaches in predicting the final vote winners of the elections as well as the final vote shares.

NFV is a new network concept that moves network functions from network appliances to virtual machines. Users can customize their own services by setting ordered traffic paths through network function virtual machines they want. Though this idea is flexible and scalable, it brings additional growth in east-west traffic. In this paper, we focus on NFV deployment in data center networks. An user has multiple VMs in a data center and wants to have some network functions for these VMs. According to the user's requirements and traffic flows, we design a heuristic NFV deployment algorithm for VNF allocation, VNF placement and traffic dispatching. We set a traffic model and evaluate our algorithm by simulation. We also give some further research directions in this paper.

SDN is a concept of programmable networking. A network manager can process each network flow through software programs. There is a hypothesis that all switches are trusted and follow programmed commands to handle packets. That is, once a switch is compromised by an attacker and does not follow the order of the network manager, this will bring a huge network disaster. In this paper, we define some attack models through compromised switches and design a detection mechanism to find these compromised devices. We evaluate our mechanism and discuss some future works.

Sunflower Movement is one of the most influential social movements in Taiwan over the past few decades. In order to protest the review process at the legislature, the protesters entered and occupied the building of the Legislative Yuan (the parliament) of Taiwan on March 18, 2014, without any sign in advance. This action shook the government and caught a high level of attention in Taiwan. People discussed the action from different viewpoints and showed their supporting and opposition on the movement in daily life as well as on social media sites. However, the information is in chaos since a large number of articles had been published during the movement. In order to realize the social discussions in a comprehensive way, major issues such as extracting notable threads and finding important authors among social sites need to be addressed. In this paper, we provide methodologies to quantify and predict the polarity of each article, we also present a consensus-based approach to identify influential authors on the social forum. From our results, our proposal is effective and efficient for identifying information and authors that are worthy of attention through the social movement.

In computer networking, broadcasting means to transmit a packet to every device in one broadcast domain. Broadcasting is widely used in many protocols, like ARP [1], DHCP [2] and so on. The main reason is that in a distributed network, each node has no knowledge about the whole network. Therefore, the node has to use broadcast ways to build its knowledge. However, for those who do not need to get or answer the broadcast message, the broadcast message is nothing but a noise packet. In this paper, we propose a broadcast handling framework in software defined networking. With this framework, a network manager can minimize the broadcast behavior in the network without impacting hosts. We also implement some broadcast handling modules in this work. We evaluate our framework's performance by comparing our framework with legacy networks and other SDN network platforms. We also discuss the impact of our framework to the spanning tree protocol.

Modern Massively Multiplayer Online Role-Playing Games (MMORPGs) provide lifelike virtual environments in which players can conduct a variety of activities including combat, trade, and chat with other players. While the game world and the available actions therein are inspired by their offline counterparts, the games' popularity and dedicated fan base are testaments to the allure of novel social interactions granted to people by allowing them an alternative life as a new character and persona. In this paper we investigate the phenomenon of "gender swapping," which refers to players choosing avatars of genders opposite to their natural ones. We report the behavioral patterns observed in players of Fairyland Online, a globally serviced MMORPG, during social interactions when playing as in-game avatars of their own real gender or gender-swapped. We also discuss the effect of gender role and self-image in virtual social situations and the potential of our study for improving MMORPG quality and detecting online identity frauds.

Crowdsourcing has emerged in recent years as a potential strategy to enlist the general public to solve a wide variety of tasks. With the advent of ubiquitous Internet access, it is now feasible to ask an Internet crowd to conduct QoE (Quality of Experience) experiments on their personal computers in their own residences rather than in a laboratory. The considerable size of the Internet crowd allows researchers to crowdsource their experiments to a more diverse set of participant pool at a relatively low economic cost. However, as participants carry out experiments without supervision, the uncertainty of the quality of their experiment results is a challenging problem. In this paper, we propose a crowdsourceable framework to quantify the QoE of multimedia content. To overcome the aforementioned quality problem, we employ a paired comparison method in our framework. The advantages of our framework are: 1) trustworthiness due to the support for cheat detection; 2) a simpler rating procedure than that of the commonly-used but more difficult mean opinion score (MOS), which places less burden on participants; 3) economic feasibility since reliable QoE measures can be acquired with less effort compared with MOS; and 4) generalizability across a variety of multimedia content. We demonstrate the effectiveness and efficiency of the proposed framework by a comparison with MOS. Moreover, the results of four case studies support our assertion that the framework can provide reliable QoE evaluation at a lower cost.

This study considers all the possible actions a borrower may have, i.e., to default, to prepay, and to maintain the mortgage, during mortgage horizon. Then, we provide an effective and accurate pricing formula, which not only considers the effect that default might affect the mortgage value, but also more accurately explores the impact due to prepayment risk. In our model, we define prepayment value of the mortgage as the amount of outstanding principle. In contrast, previous literature defines prepayment value as a constant proportion of maintaining value of the mortgage. Finally, based on closed-form pricing formula, we analyze the yield, duration and convexity of risky mortgage loan, providing a better framework for risk management.

Cloud gaming, i.e., real-time game playing via thin clients, relieves players from the need to constantly upgrade their computers and deal with compatibility issues when playing games. As a result, cloud gaming is generating a great deal of interest among entrepreneurs and the public. However, given the large design space, it is not yet known which platforms deliver the best quality of service and which design elements constitute a good cloud gaming system.

Despite all the efforts devoted to improving the QoS of networked multimedia services, the baseline for such improvements has yet to be defined. In other words, although it is well recognized that better network conditions generally yield better service quality, the exact minimum level of network QoS required to ensure satisfactory user experience remains an open question.

From the perspective of game system designers, players' behavior is one of the most important factors they must consider when designing game systems. To gain a fundamental understanding of the game play behavior of online gamers, exploring users' game play time provides a good starting point. This is because the concept of game play time is applicable to all genres of games and it enables us to model the system workload as well as the impact of system and network QoS on users' behavior. It can even help us predict players' loyalty to specific games. In this paper, we present the World of Warcraft Avatar History (WoWAH) dataset, which comprises the records of 91,065 avatars. The data includes the avatars' game play times and a number of attributes, such as their race, profession, current level, and in-game locations, during a 1,107-day period between Jan. 2006 and Jan. 2009. We believe the WOWAH dataset could be used for various creative purposes, now that it is a public asset of the research community. It is available for free download at http://mmnet.iis.sinica.edu.tw/dl/wowah/.

The vertex-betweenness centrality index is an essential measurement for analyzing social networks, but the computation time is excessive. At present, the fastest algorithm, proposed by Brandes in 2001, requires O(|V| |E|) time, which is computationally intractable for real-world social networks that usually contain millions of nodes and edges. In this paper, we propose a fast and accurate algorithm for estimating vertex-betweenness centrality values for social networks. It only requires O(b2|V|) time, where b is the average degree in the network. Significantly, we demonstrate that the local dynamic information about the vertices is highly relevant to the global betweenness values. The experiment results show that the vertex-betweenness values estimated by the proposed model are close to the real values and their rank is fairly accurate. Furthermore, using data from online role-playing games, we present a new type of dynamic social network constructed from in-game chatting activity. Besides using such online game networks to evaluate our betweenness estimation model, we report several interesting findings derived from conducting static and dynamic social network analysis on game networks.

Lightweight en-route authentication is a challenging task in wireless multi-hop networks. Without efficient defense mechanism, an adversary can inject false data into the system, incurring redundant message forwarding, consuming node power, and degrading network performance. Unlike conventional approaches directly using message authentication codes (MAC), we utilize Bloom filter techniques to build an authentication manifest, which is called en-route authentication bitmap (EAB). EAB helps nodes on the routing path to filter out false data in high success rate, thus confine the injection attacks within the one or two hops from the adversary. EAB can spend a few bytes of bandwidth, whereas effectively protect the forwarding path of tens of hops. Our scheme is suitable for resource-constrained networks like WSN, PAN, MANET, etc.

For current Web 2.0 services, manual examination of user uploaded content is normally required to ensure its legitimacy and appropriateness, which is a substantial burden to service providers. To reduce labor costs and the delays caused by content censoring, social moderation has been proposed as a front-line mechanism, whereby user moderators are encouraged to examine content before system moderation is required. Given the immerse amount of new content added to the Web each day, there is a need for automation schemes to facilitate rear system moderation. This kind of mechanism is expected to automatically summarize reports from user moderators and ban misbehaving users or remove inappropriate content whenever possible. However, the accuracy of such schemes may be reduced by collusion attacks, where some work together to mislead the automatic summarization in order to obtain shared benefits. In this paper, we propose a collusion-resistant automation scheme for social moderation systems. Because some user moderators may collude and dishonestly claim that a user misbehaves, our scheme detects whether an accusation from a user moderator is fair or malicious based on the structure of mutual accusations of all users in the system. Through simulations we show that collusion attacks are likely to succeed if an intuitive count- based automation scheme is used. The proposed scheme, which is based on the community structure of the user accusation graph, achieves a decent performance in most scenarios.

Many existing pairwise key establishment in sensor networks were developed based on the polynomial-based method. However, this method cannot offer the basic authentication function of security. In this paper, we develop a novel tame-based approach to achieve the goal of authentication. We then present a general framework , for pairwise key establishment, in the basis of this tame-based approach. It turns out that this tame map can substitute the polynomial in any existing polynomial pool-based scheme to offer the authentication service. In particular, we further introduce a new scheme, local symmetric-tame maps predistribution scheme, based on this framework. The analysis demonstrates that, in addition to being able to provide authentication service, this scheme not only has significantly better performance, but also can achieve greater resilience on security than existing schemes.

Because of the enormous growth in the number of peer-to-peer (P2P) applications, P2P traffic now constitutes a substantial proportion of Internet traffic. The ability to accurately identify different P2P applications from the network traffic is essential for managing a number of network traffic issues, such as service differentiation and capacity planning. However, modern P2P applications often use proprietary protocols, dynamic port numbers, and packet encryptions, which make traditional identification approaches like port-based or signature- based identification less effective. In this paper, we propose an approach for accurately recognizing P2P applications running on monitored hosts based on signaling behavior, which is regulated by the underlying P2P protocol; therefore, each application possesses a distinguishing characteristic. We consider that the signaling behavior of each P2P application can serve as a unique signature for application identification. Our approach is particularly useful for three reasons: 1) it does not need to access the packet payload; 2) it recognizes applications based purely on their signaling behavior; and 3) it can identify particular P2P applications. The performance evaluation shows that 92% of a real-life traffic trace can be correctly recognized within a 5-minute monitoring period.

Until recently, QoE (Quality of Experience) experiments had to be conducted in academic laboratories; however, with the advent of ubiquitous Internet access, it is now possible to ask an Internet crowd to conduct experiments on their personal computers. Since such a crowd can be quite large, crowdsourcing enables researchers to conduct experiments with a more diverse set of participants at a lower economic cost than would be possible under laboratory conditions. However, because participants carry out experiments without supervision, they may give erroneous feedback perfunctorily, carelessly, or dishonestly, even if they receive a reward for each experiment.

VoIP playout buffer dimensioning has long been a challenging optimization problem, as the buffer size must maintain a balance between conversational interactivity and speech quality. The conversational quality may be affected by a number of factors, some of which may change over time. Although a great deal of research effort has been expended in trying to solve the problem, how the research results are applied in practice is unclear.

WiMAX is a telecommunication technology. It is based on IEEE802.16 family and provides the network service with large coverage, high data rate and mobility. Since IEEE802.16 series are connection-based wireless technologies, WiMAX is exposed to a special kind of DoS attacks which can target a specific victim connection without influencing the others. This paper will introduce this scrambling attack in WiMAX networks in detail and will provide a prevention approach.

Understanding the impact of network conditions on player satisfaction, which is one of the major concerns of network game designers, is a popular research topic. Of the various ways to gauge user satisfaction, in this paper, we focus on how network quality affects a player's decision to leave a game prematurely. To answer this question, we analyze a 1,356-million-packet trace from a large commercial massively multiplayer online role-playing game (MMORPG) called ShenZhou Online. We show that both network delay and network loss significantly affect a player's decision to leave a game prematurely. It is feasible to predict whether players will quit prematurely based on the network conditions they experience. The proposed model can determine the relative impact of different types of network impairment. For our traces, the degrees of player intolerance to network delay, delay jitter, client packet loss, and server packet loss are in the proportion of 1:2:4:3 approximately. The model can also be used to make system design decisions. Through simulations, we show that by prioritizing server processing according to the goodness of network conditions, employing dejitter buffers, or replacing TCP with a more lightweight transport protocol, the probability of premature departure can be significantly reduced. In this way, we demonstrate how our model of players' network experience provides feedback for the design of online games.

Normally, voice activity detection (VAD) refers to speech processing algorithms for detecting the presence or absence of human speech in segments of audio signals. In this paper, however, we focus on speech detection algorithms that take VoIP traffic instead of audio signals as input. We call this category of algorithms network-level VAD. Traditional VAD usually plays a fundamental role in speech processing systems because of its ability to delimit speech segments. Network-level VAD, on the other hand, can be quite helpful in network management, which is the motivation for our study. We propose the first real-time network-level VAD algorithm that can extract voice activity from encrypted and non-silence-suppressed Skype traffic. We evaluate the speech detection accuracy of the proposed algorithm with extensive real-life traces. The results show that our scheme achieve reasonably good performance even high degree of randomness has been injected into the network traffic.

Peer-to-peer technique has now become one of the major techniques to exchange digital content between peers of the same interest. However, as the amount of peer-to-peer traffic increases, a network administrator would like to control the network resources consumed by peer-to--peer applications. Due to the use of random ports and protocol encryption, it is hard to identify and apply proper control policies to peer-to-peer traffic. How do we properly bound the peer-to-peer traffic and prevent it from consuming all the available network resources? In this paper, we propose an algorithm that tries to approximately bound the network resources consumed by peer-to-peer traffic without examining packet payloads. Our methodology especially focuses on upload traffic for that the upload bandwidth for an ISP are usually more precious than download bandwidth. The method is constructed in two stages. First, we observe several traffic characteristics of peer-to-peer applications and traditional clientserver based Internet services. We also observe the generic traffic properties in a client network. Then, based on the symmetry of network traffic in both temporal and spatial domains, we propose to use a bitmap filter to bound the network resources consumed by peer-to-peer applications. The proposed algorithm takes only constant storage and computation time. The evaluation also shows that with a small amount of memory, the peer-to-peer traffic can be properly bounded close to a predefined amount.

A secure multicast framework should only allow authorized members of a group to decrypt received messages; usually one "group key" is shared by all approved members. However, this raises the problem of "one affects all," whereby the actions of one member affect the whole group. Many researchers solve the problem by dividing a group into several subgroups, but most existing solutions require a centralized trusted controller to coordinate cryptographic keys for subgroups. We believe this is a constraint on network scalability. In this paper, we propose a novel framework to solve key management problems in multicast networks. Our contribution is three-fold: 1) We exploit the ElGamal cryptosystem and propose the idea of key composition; 2) A distributed key assignment protocol is proposed to eliminate the need for a centralized trust controller in a secure multicast network that leverages proxy cryptography; and 3) We adopt a hybrid encryption technique that makes our framework more efficient and practical. Comparison with similar frameworks shows the proposed scheme is efficient in both time and space complexity. In addition, costs of most protocol operations are bounded by constants regardless of a group's size and the degree of transit nodes.

With the emergence of active worms, the targets of attacks have been moved from well-known Internet servers to generic Internet hosts, and since the rate at which patches can be applied is always much slower than the spread of a worm, an Internet worm can usually attack or infect millions of hosts in a short time. It is difficult to eliminate Internet attacks globally; thus, protecting client networks from being attacked or infected is a relatively critical issue. In this paper, we propose a method that protects client networks from being attacked by people who try to scan, attack, or infect hosts in local networks via unpatched vulnerabilities. Based on the symmetry of network traffic in both temporal and spatial domains, a bitmap filter is installed at the entry point of a client network to filter out possible attack traffic. Our evaluation shows that with a small amount of memory (less than 1 megabyte), more than 95% of attack traffic can be filtered out in a small- or medium-scale client network.

Abstract — Online gaming is one of the most profitable busi-nesses on the Internet. Among various threats to continuous player subscriptions, network lags are particularly notorious. It is widely known that frequent and long lags frustrate game players, but whether the players actually take action and leave a game is unclear. Motivated to answer this question, we apply survival analysis to a 1, 356-million-packet trace from a sizeable MMORPG, called ShenZhou Online. We find that both network delay and network loss significantly affect a player’s willingness to continue a game. For ShenZhou Online, the degrees of player “intolerance ” of minimum RTT, RTT jitter, client loss rate, and server loss rate are in the proportion of 1:2:11:6. This indicates that 1) while many network games provide “ping time, ” i.e., the RTT, to players to facilitate server selection, it would be more useful to provide information about delay jitters; and 2) players are much less tolerant of network loss than delay. This is due to the game designer’s decision to transfer data in TCP, where packet loss not only results in additional packet delays due to in-order delivery and retransmission, but also a lower sending rate.

The success of Skype has inspired a generation of peer-to-peer-based solutions for satisfactory real-time multimedia services over the Internet. However, fundamental questions, such as whether VoIP services like Skype are good enough in terms of user satisfaction,have not been formally addressed. One of the major challenges lies in the lack of an easily accessible and objective index to quantify the degree of user satisfaction.In this work, we propose a model, geared to Skype, but generalizable to other VoIP services, to quantify VoIP user satisfaction based on a rigorous analysis of the call duration from actual Skype traces. The User Satisfaction Index (USI) derived from the model is unique in that 1) it is composed by objective source-and network-level metrics, such as the bit rate, bit rate jitter, and round-trip time, 2) unlike speech quality measures based on voice signals, such as the PESQ model standardized by ITU-T, the metrics are easily accessible and computable for real-time adaptation, and 3) the model development only requires network measurements, i.e., no user surveys or voice signals are necessary. Our model is validated by an independent set of metrics that quantifies the degree of user interaction from the actual traces.

TCP employs a self-clocking scheme that times the sending of packets. In that, the data packets are sent in a burst when the returning acknowledgement packets are received. This self-clocking scheme (also known as ack-clocking) is deemed a key factor to the the burstiness of TCP traffic and the source of various performance problems-high packet loss, long delay, and high delay jitter. Previous work has suggested contradictively the effectiveness of TCP pacing as a remedy to alleviate the traffic burstiness. In this paper, we analyze systematically, and in more robust experiments the impact of network variabilities on the behavior of TCP clocking schemes. We find that 1) aggregated pacing traffic could be burstier than aggregated ack-clocking traffic. Physical explanation and experimental simulations are provided to support this argument. 2) The round-trip time heterogeneity and flow multiplexing significantly influence the behaviors of both ack-clocking and pacing schemes. Evaluating the performance of clocking schemes without considering these effects is prone to inconsistent results. 3) Pacing outperforms ack-clocking in more realistic settings from the traffic burstiness point of view.

In this paper, we propose a novel framework for secure multicast on overlay networks. Our contributions are three-fold: 1) a technique key composition is proposed to cope with the secure multicast problems, 2) the proposed framework is totally distributed, i.e., no centralized control is required for subgroup configurations, and 3) a comparison of similar frameworks is provided, in which we show the proposed framework is more efficient in that its time and space complexity are bounded by constants, regardless of the number of coexisting groups, the group size, and the degree of transit nodes.

Online gaming is one of the most profitable businesses over the Internet. Among all genres of the online games, the popularity of the MMORPG (Massive Multiplayer Online Role Playing Games) is especially prominent in Asia. Opting for a better understanding of the game traffic and the economic well being of the Internet, we analyze a 1,356-million-packet trace from a sizeable MMORPG, ShenZhou Online. This work is, as far as we know, the first formal analysis on the MMORPG server traces.We find that the MMORPG and FPS (First-Person Shooting) games are similar in that they both generate small packets and require low bandwidths. In particular, the bandwidth requirement of MMORPG is even lower due to the less real-time game play. More distinctive are the strong periodicity, temporal locality, and irregularity observed in the MMORPG traffic. The periodicity is due to a common practice in game implementation, where the game state updates are accumulated within a fixed time window before transmission. The temporal locality in the game traffic is largely due to the game nature where one action leads to another. The irregularity, particular unique in MMORPG traffic, is due to the diversity of game design where the user behavior can be drastically different depending on the quest at hand.

Virtual router redundancy protocol (VRRP) is designed to eliminate the single point of failure in the static default routing environment in LAN. The original VRRP protocol does not support load balancing for both incoming and outgoing traffic. This paper describes EVRRP, i.e. enhanced VRRP. EVRRP supports an efficient multiple-node cluster and symmetric load balancing among routers. Each router periodically exchanges information to determine the status of the master and backups. The master router distributes and redirects the traffic to one of the backup routers by ICMP redirect message. Backup routers accept the traffic from the master and one of the backup routers takes over the master traffic using a gratuitous ARP message when the master fails. The improved election protocol speeds up the original VRRP election protocol and shortens the failover time by adding a new state in the previous VRRP state diagram and a new protocol type. An extensive evaluation of the EVRRP protocol is described in the paper.

For the purpose of deterring unauthorized duplication and distribution of multimedia contents, a seller may insert a unique digital watermark into each copy of the multimedia contents to be sold. When an illegal replica is found in the market sometime later, the seller can determine the responsible distributor by examining the watermark embedded. However, the accusation against the charged distributor, who was the buyer in some earlier transaction, is objectionable because the seller also has access to the watermarked copies and, hence, is able to release such a replica on her own. In this paper, a watermarking protocol is proposed to avoid such difficulties, known as the customer's right problem, in the phase of arbitration. The proposed watermarking protocol also provides a fix to Memon and Wong's scheme by solving the unbinding problem. In addition, the buyer is no longer required to contact the watermark certification authority during transactions, and the anonymity of the buyer can be retained through a trusted third party. The result is an efficient and anonymous buyer-seller watermarking protocol.

In this paper, we propose a robust and verifiable multi-authority secret voting scheme which meets the requirements of large-scale general elections. This scheme uses a uniquely blind threshold signature scheme to get blind threshold electronic votes such that any voter can abstain from voting after the registration phase. It also uses the threshold cryptosystem to guarantee fairness among the candidates' campaigns and to provide a mechanism for ensuring that any voter can make an open objection to the tally if his vote has not been published. In this scheme, the computations among voters are independent and voters only have to send an anonymous message to the counter after the registration phase. This scheme preserves the privacy of a voter from the counter, administrators, scrutineers and other voters. Completeness, robustness and verifiability of the voting process are ensured and hence no one can produce a false tally or corrupt or disrupt the election.

Network bandwidth is a limited and precious resource in distributed computing environments. Insufficient bandwidth will severely degrade the performance of a distributed computing task in exchanging massive amounts of data among the networked hosts. A feasible solution to save bandwidth is to incorporate data compression during transmission. However blind, or unconditional, compression may only result in waste of CPU power and even slow down the overall network transfer rate, if the data to be transmitted are hard to compress. We present a prediction-capable lossless data compression algorithm to address this problem. By adapting to the compression speed of a host CPU, current system load, and network speed, our algorithm can accurately estimate the compression time of each data block given, and decide whether it should be compressed or not. Experimental results indicate that our prediction mechanism is both efficient and effective, achieving 93% of prediction accuracy at the cost of only 3.2% of the execution time of unconditional compression.

Article Modalities for model checking (extended abstract): branching time strikes back Share on Authors: E. Allen Emerson Department of Computer Sciences, University of Texas at Austin, Austin, Texas Department of Computer Sciences, University of Texas at Austin, Austin, TexasView Profile , Chin-Laung Lei Department of Computer Sciences, University of Texas at Austin, Austin, Texas Department of Computer Sciences, University of Texas at Austin, Austin, TexasView Profile Authors Info & Claims POPL '85: Proceedings of the 12th ACM SIGACT-SIGPLAN symposium on Principles of programming languagesJanuary 1985 Pages 84–96https://doi.org/10.1145/318593.318620Online:01 January 1985Publication History 110citation612DownloadsMetricsTotal Citations110Total Downloads612Last 12 Months13Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access