State estimation plays a critical role in power dis-tribution systems. However, the conventional state estimation commonly used in transmission systems cannot be applied to the distribution systems because of insufficient measurements. Even though machine learning methods have begun to demonstrate their capabilities to overcome this challenge, they are not capa-ble of explicitly incorporating distribution systems' topological information. This paper proposes EleGNN, an electrical-model-guided graph neural network (GNN), to perform power distri-bution system state estimation (DSSE). By explicitly considering physical topology, EleGNN enhances GNN with original node and edge feature propagation methods, allowing us to obtain accurate estimation results despite insufficient measurements and various topologies. Evaluations of six different power systems demonstrate significant improvement in state estimation accuracy than the method relying on general neural networks. Specifically, node-level mean square errors introduced by EleGNN are at least one order of magnitude smaller, even with up to 50% of missing measurements.

In this article, we investigate the coordinated topology attacks in smart grid, which combine a physical topology attack and a cyber-topology attack. The physical attack first trips a transmission line. In order to deceive the control center, the attacker masks the outage signal of the tripped line in the cyber layer and then creates a fake outage signal for another transmission line. The goal of coordinated topology attacks is to overload a critical line (different from the physical tripped line and the fake outage line) by misleading the control center into making improper dispatch. In order to determine the attack strategy, we propose a deep-reinforcement-learning-based method to identify the physical tripped line and the fake outage line. Besides, in order to block the outage signal of the tripped line and create the fake outage signal with limited attack resources, we propose a deep-reinforcement-learning-based approach to determine the minimal attack resources. Numerical simulations verify the effectiveness of the proposed method.

In recent years, unmanned aerial vehicles (UAVs) are more widely applied due to low cost and high flexibility. Facing the suddenness of emergency events and the uncertainty of user service requests, the rational deployment of the UAV aerial base stations (ABSs) has become an effective solution. However, how to deploy multiple UAVs with the variety of properties (power, service radius, and so on) is a challenge problem. There is a mean-field-type game (MFTG) to obtian optimal startegies which has been applied in practical applications. The arbitrariness of the number, the distinguishability of agents and the non-negligible effect on system are considered in MFTG. Because the homogeneity of multiple UAVs cannot be guaranteed in the practical communication model, this paper formulates the deployment of multiple UAV ABSs with various properties as a MFTG. In this scenario, each UAV decides the flight strategy at the next moment to minimize its own cost function by analyzing the communication requests of the ground users. In addition, the existence of the Nash equilibrium of the MFTG problem is proved. And the direct square complement method is used to solve the problem to minimize the cost function of each UAV. The simulation results show the correctness of the proposed method and the rationality of the deployment.

This paper proposes PRIMA: Probabilistic Ranking with Inter-item competition and Multi-Attribute utility function, which ranks items based on their probabilities of being a user's best choice. This framework is particularly important in E-commerce applications for making recommendations, predicting sales, and developing pricing strategies. To achieve mathematical tractability, it uses the weight-based multi-attribute utility function to address the inter-attribute tradeoff, where the weight reflects a user's personal preference for each attribute. The proposed work updates the weight from a user's past transactions using the concept of marginal rate of substitution from microeconomics, addresses the interitem competition, and computes the items' probabilities of being a user's best choice. Real user test results show that the proposed framework achieves comparable ranking accuracy to the state-of-the-art work with significant improvements in model simplicity and mathematical tractability.

Recently, the sequential attack, where multiple malignant contingencies are launched by attackers sequentially, has revealed power grid vulnerability under cascading failures. This paper systematically analyzes properties and features of N-k cascaded- based sequential line-switching attacks using a DC power flow based cascading failure simulator (DC- CFS). This paper first explains the key factors behind cascade-based attacks, then compares three adopted metrics with an original line-margin metric to compute vulnerability indexes and design sequential attacks. Two target search schemes, i.e., offline and online target search in sequential attacks, are also presented. Simulation results of N-2 to N-4 line-switching attacks have suggested that the proposed line margin metric produces stronger sequential attacks, and online target search is more effective than offline search. Reasons behind counter-intuitive load loss resulting from different metrics are also analyzed to facilitate future study on the risk of sequential attacks.

Power grids are often run near the operational limits because of increasing electricity demand, where even small disturbances could possibly trigger major blackouts. The attacks are the potential threats to trigger large-scale cascading failures in the power grid. In particular, the attacks mean to make substations/transmission lines lose functionality by either physical sabotages or cyber attacks. Previously, the attacks were investigated from substation-only/transmission-line-only perspectives, assuming attacks can occur only on substations/transmission lines. In this paper, we introduce the joint substation-transmission line perspective, which assumes attacks can happen on substations, transmission lines, or both. The introduced perspective is a nature extension to substation-only and transmission-line-only perspectives. Such extension leads to discovering many joint substation-transmission line vulnerabilities. Furthermore, we investigate the joint substation-transmission line attack strategies. In particular, we design a new metric, the component interdependency graph (CIG), and propose the CIG-based attack strategy. In simulations, we adopt IEEE 30 bus system, IEEE 118 bus system, and Bay Area power grid as test benchmarks, and use the extended degree-based and load attack strategies as comparison schemes. Simulation results show the CIG-based attack strategy has stronger attack performance.

A trust management scheme can be used to aid an automated decision-making process for an access control policy. Since unintentional temporary errors are possible, the trust management solution must provide a redemption scheme to allow nodes to recover trust. However, if a malicious node tries to disguise its malicious behaviors as unintentional temporary errors, the malicious node may be given more opportunities to attack the system by disturbing the redemption scheme. Existing trust management schemes that employ redemption schemes fail to discriminate between temporary errors and disguised malicious behaviors in which the attacker cleverly behaves well and badly alternatively. In this paper, we present the vulnerabilities of existing redemption schemes, and describe a new trust management and redemption scheme that can discriminate between temporary errors and disguised malicious behaviors with a flexible design. We show the analytical results of the trust management scheme, and demonstrate the advantages of the proposed scheme with simulation conducted in a Wireless Sensor Network.

Vulnerability analysis on the power grid has been widely conducted from the substation-only and transmission-line-only perspectives. In order words, it is considered that attacks can occur on substations or transmission lines separately. In this paper, we naturally extend existing two perspectives and introduce the joint-substation-transmission-line's perspective, which means attacks can concurrently occur on substations and transmission lines. Vulnerabilities are referred to as these multiple-component combinations that can yield large damage to the power grid. One such combination consists of substations, transmission lines, or both. The new perspective is promising to discover more power grid vulnerabilities. In particular, we conduct the vulnerability analysis on the IEEE 39 bus system. Compared with known substation-only/transmission-line-only vulnerabilities, joint-substation-transmission-line vulnerabilities account for the largest percentage. Referring to three-component vulnerabilities, for instance, joint-substation-transmission-line vulnerabilities account for 76.06%; substation-only and transmission-line-only vulnerabilities account for 10.96% and 12.98%, respectively. In addition, we adopt two existing metrics, degree and load, to study the joint-substation-transmission-line attack strategy. Generally speaking, the joint-substation-transmission-line attack strategy based on the load metric has better attack performance than comparison schemes.

This work proposes a novel probabilistic multi-attribute item ranking framework to estimate the probability of an item being a user's best choice and rank items accordingly. It uses indifference curve from microeconomics to model users' personal preference, and addresses the inter-attribute tradeoff and inter-item competition issues at the same time with little information loss. The proposed framework also considers the fact that a user can only compare a few items at the same time, and models the user's selection process as a two-step process, where the user first selects a few candidates, and then makes detailed comparison. Simulation results show that the proposed framework significantly outperforms existing multiattribute ranking algorithms in terms of ranking quality.

While personal data privacy is threatened by online social networks, researchers are seeking for privacy protection tools and methods to assist online social network providers and users. In this paper, we aim to address this problem by investigating how to quantitatively evaluate the privacy risk, as a function of people's awareness of privacy risks as well as whether their friends can be trusted to protect their personal data. We present a trust-aware privacy evaluation framework, called TAPE. Simulations are performed to illustrate the key concepts and calculations in TAPE, as well as demonstrate the advantages of TAPE.

The vulnerability analysis is vital for safely running power grids. The simultaneous attack, which applies multiple failures simultaneously, does not consider the time domain in applying failures, and is limited to find unknown vulnerabilities of power grid networks. In this paper, we discover a new attack scenario, called the sequential attack, in which the failures of multiple network components (i.e., links/nodes) occur at different time. The sequence of such failures can be carefully arranged by attackers in order to maximize attack performances. This attack scenario leads to a new angle to analyze and discover vulnerabilities of grid networks. The IEEE 39 bus system is adopted as test benchmark to compare the proposed attack scenario with the existing simultaneous attack scenario. New vulnerabilities are found. For example, the sequential failure of two links, e.g., links 26 and 39 in the test benchmark, can cause 80% power loss, whereas the simultaneous failure of them causes less than 10% power loss. In addition, the sequential attack is demonstrated to be statistically stronger than the simultaneous attack. Finally, several metrics are compared and discussed in terms of whether they can be used to sharply reduce the search space for identifying strong sequential attacks.

Social connections often have a significant influence on personal decision making. Researchers have proposed novel recommender systems that take advantage of social relationship information to improve recommendations. These systems, while promising, are often hindered in practice. Existing social networks such as Facebook are not designed for recommendations and thus contain many irrelevant relationships. Many recommendation platforms such as Amazon often do not permit users to establish explicit social relationships. And direct integration of social and commercial systems raises privacy concerns.

The security issue of complex networks has drawn significant concerns recently. While pure topological analyzes from a network security perspective provide some effective techniques, their inability to characterize the physical principles requires a more comprehensive model to approximate failure behavior of a complex network in reality. In this paper, based on an extended topological metric, we proposed an approach to examine the vulnerability of a specific type of complex network, i.e., the power system, against cascading failure threats. The proposed approach adopts a model called extended betweenness that combines network structure with electrical characteristics to define the load of power grid components. By using this power transfer distribution factor-based model, we simulated attacks on different components (buses and branches) in the grid and evaluated the vulnerability of the system components with an extended topological cascading failure simulator. Influence of different loading and overloading situations on cascading failures was also evaluated by testing different tolerance factors. Simulation results from a standard IEEE 118-bus test system revealed the vulnerability of network components, which was then validated on a dc power flow simulator with comparisons to other topological measurements. Finally, potential extensions of the approach were also discussed to exhibit both utility and challenge in more complex scenarios and applications.

The modern society increasingly relies on electrical service, which also brings risks of catastrophic consequences, e.g., large-scale blackouts. In the current literature, researchers reveal the vulnerability of power grids under the assumption that substations/transmission lines are removed or attacked synchronously. In reality, however, it is highly possible that such removals can be conducted sequentially. Motivated by this idea, we discover a new attack scenario, called the sequential attack, which assumes that substations/transmission lines can be removed sequentially, not synchronously. In particular, we find that the sequential attack can discover many combinations of substation whose failures can cause large blackout size. Previously, these combinations are ignored by the synchronous attack. In addition, we propose a new metric, called the sequential attack graph (SAG), and a practical attack strategy based on SAG. In simulations, we adopt three test benchmarks and five comparison schemes. Referring to simulation results and complexity analysis, we find that the proposed scheme has strong performance and low complexity.

Security issues related to power grid networks have attracted the attention of researchers in many fields. Recently, a new network model that combines complex network theories with power flow models was proposed. This model, referred to as the extended model, is suitable for investigating vulnerabilities in power grid networks. In this paper, we study cascading failures of power grids under the extended model. Particularly, we discover that attack strategies that select target nodes (TNs) based on load and degree do not yield the strongest attacks. Instead, we propose a novel metric, called the risk graph, and develop novel attack strategies that are much stronger than the load-based and degree-based attack strategies. The proposed approaches and the comparison approaches are tested on IEEE 57 and 118 bus systems and Polish transmission system. The results demonstrate that the proposed approaches can reveal the power grid vulnerability in terms of causing cascading failures more effectively than the comparison approaches.

Application communication in wireless sensor networks (WSN) depends on two important factors: acquisition of sensed data from a specific area, and network connectivity that concerns the reliable delivery of the observed data from sensor nodes to the sink node. In this paper, we consider the application communication reliability (ACR) of WSN supporting K-coverage in the presence of shadowing for a specific monitored area. The analytical evaluation of ACR involves two steps. We first identify all the K-coverage sets. Then, we evaluate the communication reliability of delivering the observed data from sensor nodes within the identified K-coverage sets to the sink node. Two single-path routing algorithms, shortest-path distance algorithm and shortest-path hop algorithm, are considered for evaluating the communication reliability during the second step; their performances in terms of ACR and energy consumptions are compared through an empirical analysis of several examples. Different scenarios are considered to evaluate the impact of node density, channel condition and different monitored areas on ACR. Simulation and analytical results show that WSN using the shortest-path distance algorithm is more reliable than that using the shortest-path hop algorithm in most cases, but WSN using the shortest-path hop algorithm consumes less energy for delivering the sensed data to the sink node.

With the big success of the mobile application (app) sales, attackers are also attracted by the potential profits in the app market. In this paper, we survey current app ranking schemes as well as existing app reputation manipulation schemes and raise some interesting while arguable questions. Based on an app installation data set collected from a university campus community, we quantitatively investigate the answers to two questions: (1) what is the impact of app reputation and download number on app sales and (2) will attackers make profits from the manipulation of app reputation or download number. Although the results may not be generalized to the global app market, they provide a new view point for further investigations.

We offer algorithms and visualization techniques for cyber trust in a Smart Grid system. Cyber trust is evaluated in terms of a mathematical model consisting of availability, detection and false alarm trust values, as well as a model of predictability. We develop a prototype Cyber Situational Awareness Visualization (CyberSAVe) tool to visualize cyber trust. We provide Operational Decision Aids (ODAs) displayed in context with a SCADA management information. We define cyber trust metrics, which are calculated and displayed in real-time in the Metric Assessment System (MAS) of CyberSAVe. We demonstrate the use of trust combined with visualization of trust to detect various types of attacks on the Smart Grid.

With the rapid development of reputation systems in various online social networks, manipulations against such systems are evolving quickly. In this paper, we propose scheme TATA, the abbreviation of joint Temporal And Trust Analysis, which protects reputation systems from a new angle: the combination of time domain anomaly detection and Dempster–Shafer theory-based trust computation. Real user attack data collected from a cyber competition is used to construct the testing data set. Compared with two representative reputation schemes and our previous scheme, TATA achieves a significantly better performance in terms of identifying items under attack, detecting malicious users who insert dishonest ratings, and recovering reputation scores.

In the study of power grid security, the cascading failure analysis in multi-contingency scenarios has been a challenge due to its topological complexity and computational cost. Both network analyses and load ranking methods have their own limitations. In this paper, based on self-organizing map (SOM), we propose an integrated approach combining spatial feature (distance)-based clustering with electrical characteristics (load) to assess the vulnerability and cascading effect of multiple component sets in the power grid. Using the clustering result from SOM, we choose sets of heavy-loaded initial victims to perform attack schemes and evaluate the subsequent cascading effect of their failures, and this SOM-based approach effectively identifies the more vulnerable sets of substations than those from the traditional load ranking and other clustering methods. As a result, this new approach provides an efficient and reliable technique to study the power system failure behavior in cascading effect of critical component failure.

Cognitive radio (CR) networks have attracted many attentions recently, while the security issues are not fully studied yet. In this paper, we propose a new and powerful network layer attack, routing-toward-primary-user (RPU) attack in CR networks. In this attack, malicious nodes intentionally route a large amount of packets toward the primary users (PUs), aiming to cause interference to the PUs and to increase delay in the data transmission among the secondary users. In the RPU attack, it is difficult to detect the malicious nodes since the malicious nodes may claim that those nodes, to which they forward the packets, behave dishonestly and cause problems in the data transmission. To defend against this attack without introducing high complexity, we develop a defense strategy using belief propagation. First, an initial route is found from the source to the destination. Each node keeps a table recording the feedbacks from the other nodes on the route, exchanges feedback information and computes beliefs. Finally, the source node can detect the malicious nodes based on the final belief values. Simulation results show that the proposed defense strategy against the RPU attack is effective and efficient in terms of significant reduction in the delay and interference caused by the RPU attack.

In Wireless Sensor Networks (WSNs), sensors collaborate to perform various tasks, such as routing. A trust-based routing scheme can be used to route around compromised nodes that attempt to upset this collaboration. However, because wireless connections can be unreliable, the routing protocols must

Security issues in complex systems such as power grid, communication network, Internet, among others have attracted wide attention from academic, government and industry. In this paper, we investigate the vulnerabilities of power grid under a topology-based network model in the context of cascading failures caused by physical attacks against substations and transmission lines. In particular, we develop attack strategies from the attackers' points of view, aiming to cause severe damage to the network efficiency, as a way to revealing the vulnerability of the system. We propose a new and useful metric, load distribution vector (LDV), to describe the properties of nodes and links. Based on the LDV, we develop a multi-node attack strategy and a multi-link attack strategy, which are proved to be stronger attacks than the traditional load-based attacks using the Western North American power grid data. For example, the removal of only three critical nodes in the grid can reduce more than 30% of the original network efficiency, and the removal of only three critical links can reduce the network efficiency by 23%. In the above cases, the traditional load-based schemes reduce the network efficiency by 23.57% and 18.35%, respectively.

The quality of life of leg amputees can be improved dramatically by using a cyber physical system (CPS) that controls artificial legs based on neural signals representing amputees' intended movements. The key to the CPS is the neural-machine interface (NMI) that senses electromyographic (EMG) signals to make control decisions. This paper presents a design and implementation of a novel NMI using an embedded computer system to collect neural signals from a physical system - a leg amputee, provide adequate computational capability to interpret such signals, and make decisions to identify user's intent for prostheses control in real time. A new deciphering algorithm, composed of an EMG pattern classifier and a post-processing scheme, was developed to identify the user's intended lower limb movements. To deal with environmental uncertainty, a trust management mechanism was designed to handle unexpected sensor failures and signal disturbances. Integrating the neural deciphering algorithm with the trust management mechanism resulted in a highly accurate and reliable software system for neural control of artificial legs. The software was then embedded in a newly designed hardware platform based on an embedded microcontroller and a graphic processing unit (GPU) to form a complete NMI for real time testing. Real time experiments on a leg amputee subject and an able-bodied subject have been carried out to test the control accuracy of the new NMI. Our extensive experiments have shown promising results on both subjects, paving the way for clinical feasibility of neural controlled artificial legs.

The power grid network is a complex network which is subjected to attacks and cascading failures. In this paper, we study the vulnerabilities of power grid in terms of cascading failures caused by node failures. Specifically, we define three metrics, the percentage-of-failure, Required Redundancy (RED), and Risk if Failure (RIF), to represent the critical level for each node. Based on these metrics, we can easily find the optimal victim nodes which attackers should choose to attack in order to cause cascading failure. From the defense point of view, these nodes are the weakest components of the power grid system and need more protection. With the results in this paper, we can be more aware of the risk level faced by the system if some nodes are taken down. Simulation results demonstrate the effectiveness of the proposed optimal victim nodes selection strategy.

This paper presents MAPS - a personalized Multi-Attribute Probabilistic Selection framework - to estimate the probability of an item being a user's best choice and rank the items accordingly. The MAPS framework makes three original contributions in this paper. First, we capture the inter-attribute t

The existing research on security issues in cognitive radio networks mainly focuses on attack and defense in individual network layers. However, the attackers do not necessarily restrict themselves within the boundaries of network layers. In this paper, we design cross-layer attack strategies that can largely increase the attackers' power or reducing their risk of being detected. As a case study, we investigate the coordinated report-false-sensing data attack (PHY layer) and small-back-off-window attack (MAC layer). Furthermore, we propose a trust-based cross-layer defense framework that relies on abnormal detection in individual layers and cross-layer trust fusion. Simulation results demonstrate that the proposed defense framework can significantly reduce the maximum damage caused by attackers.

With the popularity of voting systems in cyberspace, there is growing evidence that current voting systems can be manipulated by fake votes. This problem has attracted many researchers working on guarding voting systems in two areas: relieving the effect of dishonest votes by evaluating the trust of voters, and limiting the resources that can be used by attackers, such as the number of voters and the number of votes. In this paper, we argue that powering voting systems with trust and limiting attack resources are not enough. We present a novel attack named as Reputation Trap (RepTrap). Our case study and experiments show that this new attack needs much less resources to manipulate the voting systems and has a much higher success rate compared with existing attacks. We further identify the reasons behind this attack and propose two defense schemes accordingly. In the first scheme, we hide correlation knowledge from attackers to reduce their chance to affect the honest voters. In the second scheme, we introduce robustness-of-evidence, a new metric, in trust calculation to reduce their effect on honest voters. We conduct extensive experiments to validate our approach. The results show that our defense schemes not only can reduce the success rate of attacks but also significantly increase the amount of resources an adversary needs to launch a successful attack.

Collaborative spectrum sensing in cognitive radio networks has been proposed as an efficient way to improve the performance of primary users detection. In collaborative spectrum sensing schemes, secondary users are often assumed to be trustworthy. In practice, however, cognitive radio nodes can be compromised. Compromised secondary users can report false detection results and significantly degrade the performance of spectrum sensing. In this paper, we investigate the case that there are multiple malicious users in cognitive radio networks and the exact number of malicious users is unknown. An onion-peeling approach is proposed to defense against multiple untrustworthy secondary nodes. We calculate suspicious level of all nodes according to their reports. When the suspicious level of a node is beyond certain threshold, it will be considered as malicious and its report will be excluded in decision-making. We continue to calculate the suspicious level of remaining nodes until no malicious node can be found. Simulation results show that malicious nodes greatly degrade the performance of collaborative sensing, and the proposed scheme can efficiently detect malicious nodes. Compared with existing defense methods, the proposed scheme significantly improves the performance of primary user detection, measured by ROC curves, and captures the dynamic change in the behaviors of malicious users.

User behavior models are important for building realistic simulation environment for research on P2P multimedia file-sharing systems. In this paper, we build a user download behavior model and a user removal behavior model, which can describe important user characteristics that are not captured in the existing models. The proposed download behavior model incorporates retry behavior, and the removal behavior model integrates free-riding, file usage, and file removal. Based on two-year real user logs, we derive the range of all the model parameters and generate many interesting observations. To validate the proposed models, we compare several models in a case study on the number of living replicas in P2P file-sharing systems. The results demonstrate the accuracy, usability, and advantage of the proposed models.

Online feedback-based rating systems are gaining popularity. Dealing with unfair ratings in such systems has been recognized as an important but difficult problem. This problem is challenging especially when the number of regular ratings is relatively small and unfair ratings can contribute to a significant portion of the overall ratings. Furthermore, the lack of unfair rating data from real human users is another obstacle toward realistic evaluation of defense mechanisms. In this paper, we propose a set of statistical methods to jointly detect collaborative unfair ratings in product-rating type online rating systems. Based on detection, a framework of trust-assisted rating aggregation system is developed. Furthermore, we collect unfair rating data from real human users through a rating challenge. The proposed system is evaluated through simulations as well as experiments using real attack data. Compared with existing schemes, the proposed system can significantly reduce negative impact from unfair ratings.

There have been many time synchronization protocols proposed for sensor networks. However, the security problems related to time synchronization have not been fully solved yet. If malicious entities manipulate time synchronization, failures of many functionalities in the sensor networks would occur. In this paper, we identify various attacks against time synchronization and then develop a detection and self-healing scheme to defeat those attacks. The proposed scheme has three phases: (1) abnormality detection performed by individual sensors, (2) trust- based malicious node detection performed by the base station, and (3) self-healing through changing the topology of synchronization tree. Simulations are performed to demonstrate the effectiveness of the proposed scheme as well as the implementation overhead.

Underwater acoustic channels normally have low data rate, long propagation delay, severe multipath effect, and time varying fading. Cooperative transmission is a new wireless communication technique in which diversity gain is achieved by utilizing relay nodes as virtual antennae. In this paper, we investigate cooperative transmission techniques for underwater acoustic communications. First, we study the performance of several cooperative transmission schemes, originally designed for radio communications, in an underwater scenario. Second, by taking advantage of the low propagation speed of sound, we design a new wave cooperative transmission scheme. In this scheme, the relay nodes amplify the signal received from the source node, and then forward the signal immediately to the destination. The goal is to alter the multipath effect at the receiver. Third, we derive the performance upper bound for the proposed wave cooperative transmission scheme. The simulation results show that the proposed wave cooperative transmission has significant advantages over the traditional direct transmission and the existing cooperative transmission schemes originally designed for radio wireless networks.

Reputation systems are playing critical roles in securing today's distributed computing and communication systems. Similar to other security mechanisms, reputation systems can be under attack. In this paper, we report the discovery of a new attack, named RepTrap(Reputation Trap), against feedback-based reputation systems, such as those used in P2P file-sharing systems and E-commerce websites(e.g. Amazon.com). We conduct an in-depth investigation on this new attack, including analysis, case study, and performance evaluation based on real data and realistic user behavior models. We discover that the RepTrap is a strong and destructive attack that can manipulate the reputation scores of users, objects, and even undermine the entire reputation system. Compared with other known attacks that achieve the similar goals, the RepTrap requires less effort from the attackers and causes multi-dimensional damage to the reputation systems.

For cooperative transmission in wireless networks, a very challenging problem is to combine information from different paths that do not necessarily perfectly represent the source's information. The excessive channel information needs to be estimated at the destination, especially in the networks with mobile nodes. Channel estimation error is often inevitable. Moreover, there may be untrustworthy nodes that do not honestly forward messages. Additionally, good nodes may fail to forward messages because they move out of the transmission range. In all above scenarios, the message forwarded by a relay node may not be the same as the message broadcast by the source node. This could significantly degrade the performance of cooperative transmission. Another challenging problem is to study complicated cooperative relaying topologies with multi- hop, multiple relays. Currently, existing schemes only analyze the one-hop case possibly with multiple relays, or the multi-hop case in a single chain topology. In this paper, we propose a low-overhead self-learning cooperative transmission scheme that solves above problems. In particular, each node detects the events in which the received signal- to-noise-ratio (SNR) is greater than a threshold. The distribution of the events, which can describe reliability/trustworthiness of a relay node, is modeled as a Beta function. With a very low overhead, those Beta functions can propagate through the complicated cooperative relaying topology from the source to the destination. When the destination combines information from different paths, both mean and variance are taken into the consideration. The simulation results have shown that the proposed scheme can adapt to estimation errors, untrustworthy nodes, and node mobility. The BER performance of our proposed scheme is more than 4 times better than the traditional Maximal Ratio Combining (MRC) that does not consider unreliability in relays.

Recently, trust establishment is recognized as an important approach to defend distributed networks, such as mobile ad hoc networks and sensor networks, against malicious attacks. Trust establishment mechanisms can stimulate collaboration among distributed computing and communication entities, facilitate the detection of untrustworthy entities, and assist decision-making in various protocols. In the current literature, the methods proposed for trust establishment are always evaluated through simulation, but theoretical analysis is extremely rare. In this paper, we present a suite of approaches to analyze trust establishment process. These analysis approaches are used to provide in-depth understanding of trust establishment process and quantitative comparison among trust establishment methods. The proposed analysis methods are validated through simulations.

Cooperative transmission is an emerging communication technique that takes advantages of spatial diversity and broadcast natures of wireless channels to improve wireless channel capacity. However, cooperative transmission can be vulnerable to malicious attacks, especially in its current design. In this paper, we examine whether the cooperative transmission still have performance advantages when we consider the security issues. In particular, we identify various attacks against cooperative transmission, analyze the vulnerabilities of current schemes, design a trust-assisted cooperative transmission scheme, and evaluate the proposed scheme through analysis and simulations. The proposed scheme can fix the security problems and maintain the performance advantage. It performs much better than the traditional scheme when there are malicious/greedy relays or severe channel estimation errors. In addition, we investigate the advantage of cooperative transmission in terms of defending bad mouth attacks. A recovery of link bit error rate is observed.

Although a contributory group key agreement is a promising solution to achieve access control in collaborative and dynamic group applications, the existing schemes have not achieved the performance lower bound in terms of time, communication, and computation costs. In this paper, we propose a contributory group key agreement that achieves the performance lower bound by utilizing a novel logical key tree structure, called PFMH, and the concept of phantom user position. In particular, the proposed scheme only needsO(1) rounds of the two-party Diffie-Hellman (DH) upon any single-user join event andO(logn) rounds of the two-party DH upon any single-user leave event. Both the theoretical bound analysis and simulation show that the proposed scheme achieves a lower rekeying cost than the existing tree-based contributory group key agreement schemes.

In secure group-oriented applications, key management schemes are employed to distribute and update keys such that unauthorized parties cannot access group communications. Key management, however, can disclose information about the dynamics of group membership, such as the group size and the number of joining and departing users. This is a threat to applications with confidential group membership information. This paper investigates techniques that can stealthily acquire group dynamic information from key management. We show that insiders and outsiders can successfully obtain group membership information by exploiting key establishment and key updating procedures in many popular key management schemes. Particularly, we develop three attack methods targeting tree-based centralized key management schemes. Further, we propose a defense technique utilizing batch rekeying and phantom users, and derive performance criteria that describe security level of the proposed scheme using mutual information. The proposed defense scheme is evaluated based on the data from MBone multicast sessions. We also provide a brief analysis on the disclosure of group dynamic information in contributory key management schemes.

Energy has been identified as a crucial resource in wireless sensor networks (WSNs). Since it is usually difficult to recharge the sensors deployed in a remote or hostile environment, reducing the energy consumption is the key to prolong the lifetime of the network. In this paper, we investigate an energy-saving strategy, called outsourcing, which allows a sensor to outsource tasks to others in order to reduce the overall energy consumption as well as the energy consumption of the sensors with low remaining energy. Based on this strategy, we designed an energy-efficient reliable transport protocol that intelligently distributes the task of data recovery. Both analysis and simulations show the significant reduction in energy consumption. In addition, the implementation of the proposed scheme requires minor modification to the existing protocols.

The performance of distributed networks depends on collaboration among distributed entities. To enhance security in distributed networks, such as ad hoc networks, it is important to evaluate the trustworthiness of participating entities since trust is the major driving force for collaboration. In this paper, we present a framework to quantitatively measure trust, model trust propagation, and defend trust evaluation systems against malicious attacks. In particular, we address the fundamental understanding of trust, quantitative trust metrics, mathematical properties of trust, dynamic properties of trust, and trust models. The attacks against trust evaluation are identified and defense techniques are developed. The proposed trust evaluation system is employed in ad hoc networks for securing ad hoc routing and assisting malicious node detection. The implementation is fully distributed. Simulations show that the proposed system can significantly improve network throughput as well as effectively detect malicious behaviors in ad hoc networks. Further, extensive simulations are performed to illustrate various attacks and the effectiveness of the proposed defense techniques.

The performance of ad hoc networks depends on cooperation and trust among distributed nodes. To enhance security in ad hoc networks, it is important to evaluate trustworthiness of other nodes without centralized authorities. In this paper, we present an information theoretic framework to quantitatively measure trust and model trust propagation in ad hoc networks. In the proposed framework, trust is a measure of uncertainty with its value represented by entropy. We develop four Axioms that address the basic understanding of trust and the rules for trust propagation. Based on these axioms, we present two trust models: entropy-based model and probability-based model, which satisfy all the axioms. Techniques of trust establishment and trust update are presented to obtain trust values from observation. The proposed trust evaluation method and trust models are employed in ad hoc networks for secure ad hoc routing and malicious node detection. A distributed scheme is designed to acquire, maintain, and update trust records associated with the behaviors of nodes' forwarding packets and the behaviors of making recommendations about other nodes. Simulations show that the proposed trust evaluation system can significantly improve the network throughput as well as effectively detect malicious behaviors in ad hoc networks.

The performance of ad hoc networks depends on the cooperative and trust nature of the distributed nodes. To enhance security in ad hoc networks, it is important to evaluate the trustworthiness of other nodes without central authorities. In this paper, we present an information theoretic framework to quantitatively measure trust and model trust propagation in ad hoc networks. In the proposed information theoretic framework, trust is a measure of uncertainty with its value represented by entropy. We develop Axioms that address the basic rules for trust propagation. Based on these Axioms, we present two trust models: entropy-based model and probability-based model, which satisfy all the Axioms. The proposed trust evaluation method and trust models are employed in ad hoc networks for secure ad hoc routing and malicious node detection. Simulations show that the proposed framework can significantly improve network throughput as well as effectively detect malicious behaviors in ad hoc networks.

While contributory group key agreement is a promising solution to achieve access control in collaborative and dynamic group applications, the existing schemes have not achieved the performance lower bound in terms of rekeying overhead. In this paper we introduce a contributory group key agreement that achieves the performance lower bound by utilizing a novel logical key tree structure, called PFMH, and the concept of phantom user position. In particular, the proposed scheme only needs O(1) rounds of two-party Diffie-Hellman upon any single user join event and O(log n) rounds of two-party Diffie-Hellman upon any single user leave event. Both theoretical bound analysis and simulation studies show that the proposed scheme achieves much lower rekeying cost than the existing tree-based contributory group key agreement schemes.

HADOF is a set of mechanisms to protect mobile ad hoc networks against routing disruption attacks launched by inside attackers. First, each node launches a route traffic observer to monitor the behavior of each valid route in its route cache, and to collect the packet forwarding statistics submitted by the nodes on this route. Since malicious nodes may submit false reports, each node also keeps cheating records for other nodes. If a node is detected as dishonest, this node will be excluded from future routes, and the other nodes will stop forwarding packets for it. Third, each node will try to build friendship with other nodes to speed up malicious node detection. Route diversity will be explored by each to discover multiple routes to the destination, which can increase the chance of defeating malicious nodes who aim to prevent good routes from being discovered. In addition, adaptive route rediscovery will be applied to determine when new routes should be discovered. HADOF can handle various attacks and introduces little overhead to the existing protocols. Both analysis and simulation studies have confirmed the effectiveness of HADOF.

We propose a time-efficient contributory key agreement framework for secure communications in dynamic groups. The proposed scheme employs a special join-tree/exit-tree topology in the logical key tree and effectively exploits the efficiency of amortized operations. We derive the optimal parameters and design an activation algorithm for the join and exit trees. We also show that the asymptotic average time cost per user join and leave event is /spl theta/(log (log n)), where n is the group size. Our experiment results on both simulated user activities and the real MBone data have shown that the proposed scheme outperforms the existing tree-based schemes.

Many group communications require a security infrastructure that ensures multiple levels of access privilege for group members. Access control in hierarchy is prevalent in multimedia applications, which consist of users that subscribe to different quality levels or different sets of data streams. We present a multi-group key management scheme that achieves such a hierarchical access control by employing an integrated key graph and by managing group keys for all users with various access privileges. Compared with applying existing tree-based group key management schemes directly to the hierarchical access control problem, the proposed scheme significantly reduces the communication, computation and storage overhead associated with key management and achieves better scalability when the number of access levels increases. In addition, the proposed key graph is suitable for both centralized and contributory environments.

In secure multicast communications, key management schemes are employed to prevent unauthorized access to multicast content. Key management, however, can disclose the information about the dynamics of the group membership, such as the group size and the number of join and departure users, to both inside and outside attackers. This is a threat to applications with confidential group membership information. This paper investigates attack/anti-attack strategies for stealing/protecting group dynamic information in the existing key management schemes. We show that attackers can successfully acquire the membership information by exploiting the key updating procedure in popular centralized key management schemes. Particularly, we develop two attack strategies and demonstrate their effectiveness through simulations. Further, we propose an anti-attack technique utilizing hatch rekeying and phantom users, and derive performance criteria that describe the security level of the proposed scheme using mutual information. The proposed anti-attack scheme is evaluated based on the data obtained from real MBone sessions.

Transmit diversity (TD) is one of the key technologies to achieve high data rate communications in wireless fading environments. While transmit diversity techniques have been extensively studied for point-to-point communications, their applications in wireless multicast scenario have not been fully exploited. In this paper, we first design an adaptive transmit antenna array for multicasting, with the assumption of perfect channel information at the transmitter and the performance criteria as maximizing the worst signal-to-noise ratio (SNR) among all receivers. Compared with the existing beamforming scheme that aims to maximize the average SNR, a performance gain in decoding bit error rate is obtained. Then, the proposed close-loop scheme is compared with space-time block coding that does not require feedback of channel information. When the number of multicast receivers is below a certain threshold, the proposed close-loop scheme outperforms the space-time codes. When the multicast group size is large, the space-time codes have better performance. Finally, jointly considering close loop and open loop techniques based on group size and availability of channel information feedback is suggested.

Secure multicast applications require key management that provides access control. In wireless networks, where the error rate is high and the bandwidth is limited, the design of key management schemes should place emphasis on reducing the communication burden associated with key updating. A communication-efficient class of key management schemes is those that employ a tree hierarchy. However, these tree-based key management schemes do not exploit issues related to the delivery of keying information that provide opportunities to further reduce the communication burden of rekeying. In this paper, we propose a method for designing multicast key management trees that match the network topology. The proposed key management scheme localizes the transmission of keying information and significantly reduces the communication burden of rekeying. Further, in mobile wireless applications, the issue of user handoff between base stations may cause user relocation on the key management tree. We address the problem of user handoff by proposing an efficient handoff scheme for our topology-matching key management trees. The proposed scheme also addresses the heterogeneity of the network. For multicast applications containing several thousands of users, simulations indicate a 55%-80% reduction in the communication cost compared to key trees that are independent of the network topology. Analysis and simulations also show that the communication cost of the proposed topology-matching key management tree scales better than topology-independent trees as the size of multicast group grows.

In secure multicast communications, key management is employed to prevent unauthorized access to the multicast content. Key management, however, can disclose the information about the dynamics of the group membership to inside attackers, which is a potential threat to many multicast applications. In this paper, we investigated several attack strategies for stealing group dynamic information and demonstrated their effectiveness through simulations. Further, we proposed an anti-attack technique utilizing batch rekeying and phantom users, and derived performance criteria describing the security level of the proposed scheme. The proposed anti-attack scheme was evaluated based on the membership data obtained from real MBone sessions.

In secure multicast applications, key management must be employed to provide access control to the multicast content In wireless networks, where the error rate is high and the bandwidth is limited, the design of key management schemes should place emphasis on reducing the communication burden associated with key updating and improving the reliability of key distribution. The topology-matching key management (TMKM) scheme has been proposed to reduce the communication burden associated with rekeying by matching the key tree to the network topology and localizing the transmission of rekeying messages. This scheme, however, is only suitable for homogeneous networks where mobiles users are uniformly distributed in the service area and experience similar delay and transmission error rates. In this paper, we present an improved topology-aware key management scheme that is suitable for a large-scale cellular wireless network where the heterogeneity of the network is taken into consideration. The proposed scheme not only reduces the communication overhead, but also improves the reliability of the key distribution.

Orthogonal frequency division multiplexing (OFDM) has been adopted in many broadband wireless communication standards. In OFDM systems, channel estimation is a crucial module in coherent signal detection and receiver diversity combining. In this paper, we investigate the leakage effect of pilot-symbol-assisted channel estimation techniques and propose a channel-aware unequal error protection (UEP) scheme for multimedia communications in OFDM systems. The proposed method exploits the variations of (he bit error rate in different OFDM subchannels caused by the leakage effect, and provides UEP by transmitting the more important data through the subchannels experiencing lower channel estimation error. Compared with the systems that do not jointly consider the channel estimation in the transmission of multimedia data, the proposed scheme can improve the peak-signal-to-noise-ratio (PSNR) of reconstructed images by up to 3 dB.

An important application of multicasting is group-oriented multimedia communication, such as video-on-demand and video conferencing. Before these services can be successfully deployed, security infrastructures must be developed to ensure access control to multicast content. In this paper, we present a multi-group key management scheme tailored to multimedia multicast services that distribute data in multi-layer or multi-object format. Compared with the existing tree-based key management schemes that are used for generic data multicasting services, the proposed scheme can reduce the communication overhead by a factor of (M + 1)/2 and M is the number of layers. For example, when the multimedia data is encoded into three layers according to different quality requirements, the proposed scheme reduces the communication overhead associated with key updating by 50%.

In the future, many multicast services will take place in the wireless domain. However, before these services can successfully be deployed, security infrastructures must be developed that manage the keys needed to provide access control to content. We present a method for designing multicast key management trees that are suitable for mobile wireless environments. By matching the key management tree to the cellular network topology, the total communication burden is reduced by 33%-45% compared to using the traditional key management trees that are independent of the topology.

Group-oriented services are attractive to both customers and service providers since they allow for potential markets that exploit user collaboration and shared experiences. In order to protect the content of a group service, the content must be encrypted using a key shared by group members. With the advancement of network technologies, customers will access group services from different networks using different terminals. The computational capabilities of the group members will vary. Contributory key agreement schemes have been developed to support secure communication between many senders and many receivers, but do not consider that users might require different amounts of time to perform a round of key establishment. In this paper, we develop a contributory key agreement scheme to setup the customer group which achieves a smaller latency for group key generation than existing schemes. We then address the latency needed to rekey the group during member joins and departures. A protocol that determines the location to place a joining member is presented that considers the time needed for rekeying the joining member as well as the effect the joining member has upon the departure times of the other group members. Simulations are presented that compare our algorithms with existing schemes for different group sizes, as well as different user join rates.

OFDM modulation, which has been adopted for the digital video broadcasting (DVB-T) standard in Europe, has been recognized for its good performance for high data rate wireless communications. Therefore, the study of the robust transmission of multimedia data over OFDM systems has attracted extensive research interest. In the past, channel estimation, which is an important aspect in OFDM systems, has not been exploited for multimedia transmission. When using block-training based channel estimation schemes, there exists a periodic variation of the error rates due to channel estimation imprecision. We use this property to provide unequal error protection (UEP) for video transmission. Compared with systems using pilot-training based channel estimation schemes, which are recommended in the DVB-T standard, a performance gain of about 2dB in the PSNR of the reconstructed images is achieved.