Network traffic classification is crucial for cybersecurity, as it helps detect abnormal traffic and potential attack patterns, enabling timely protective measures. With the widespread adoption of remote work, video conferencing, and VPN services, the rapid growth of network traffic has increased the complexity of traffic classification. Traditional methods, such as port-based classification, Deep Packet Inspection, and Machine Learning (ML), face limitations in handling complex and encrypted traffic. In this study, we propose a novel network traffic classification system that combines 1D-CNN and Transformer, utilizing different activation functions to extract both local and long-range features. These complementary features are fused together to improve classification capability. The experimental results on the ISCX VPN-nonVPN dataset indicate that our proposed system outperforms baseline methods in most categories with respect to Precision, Recall, and F1-score.

Privacy-preserving k-nearest neighbor (PPkNN) classification for multiple clouds enables categorizing queried data into a class in keeping with data privacy, where the database and key servers jointly perform cryptographic operations. The existing solutions, unfortunately, take a long time and incur a large amount of traffic between the database and key servers. Therefore, in this paper, we propose a fast and secure kNN classification protocol, namely FSkNN, over distributed databases deployed in multiple clouds under the semi-honest model. Particularly, we focus on optimizing the network-related operations during kNN classification. That is, the proposed cryptographic protocol reduces the number of interactions between the servers by using a fully homomorphic encryption scheme and eliminates unnecessary traffic by applying mathematical techniques. In addition, the indistinguishability-based security of FSkNN is proven. We implemented FSkNN with C++ and the testbed experiments demonstrate that the proposed scheme significantly facilitates the query response time and reduces the communication cost.

Urbanization, characterized by the continuous growth of population and density in urban areas, has led to the expansion and increased complexity of transportation networks, exacerbating traffic congestion. Accurate traffic speed prediction is crucial for effective traffic network management and planning. As the complexity of real-world road networks increases, integrating spatial and temporal information for accurate traffic speed prediction has become a challenging research task. This paper proposes a novel approach by introducing a spatial-temporal graph neural network (STGNN)-based model to enhance the accuracy of traffic speed prediction. By employing an attention-based STGNN, we effectively capture the complex relationships among road segments in real-world scenarios. We utilize the Huber loss as the training objective to improve prediction accuracy. Furthermore, we present an architecture that incorporates Root Mean Square Layer Normalization into the Transformer and integrates the Spatial-Temporal Attention Wavenet (STAWnet) into the model backbone, enabling faster training while maintaining model stability. We evaluate the proposed model using five real-world traffic speed benchmark datasets. The experimental results demonstrate that our method achieves superior performance compared to state-of-the-art traffic speed prediction approaches.

Multi-armed bandits (MABs) are widely used for decision making processes, in which an agent tries to maximize a long-term reward by balancing exploiting and exploring actions. In this paper, we are particularly interested in federated MABs for mobile social networks (MSNs), where a collection of agents learn action values by locally observing rewards and exchanging estimated action values with other agents. Our research differs from the existing federated MABs in the underlying network setting, such that agents can communicate with each other only at opportunistic contact events. To this end, we first design the weighted-connectivity (WC) centrality to quantify the importance of agents in an MSN, and then, we propose the weighted-connectivity upper confidence bound (WC-UCB) algorithm for the MSN contexts. The key idea to reduce biases at each agent and to utilize limited opportunities of federated updates is to prioritize the estimates of action-value functions computed by the agents with a high WC centrality. In addition, the performance bound in terms of the cumulative regret is analyzed. The performance of the proposed algorithm is evaluated by simulations using real mobility traces and the results demonstrate that our WC-UCB outperforms the state-of-the-art algorithms in terms of the average reward and the cumulative regret.

Cyber Threat Intelligence (CTI) significantly enhances organizational cybersecurity defenses by providing actionable insights from diverse data sources. This research studies the correlation between CTI analysis and the MITRE ATT&CK framework, focusing on their alignment to strengthen threat detection and response capabilities. A pivotal aspect of this study involves developing a classifier using a fine-tuned BERT-based model to map CTI reports to specific ATT&CK techniques. Our model demonstrated substantial improvements over the baseline SecBERT, achieving a 2.6% higher F1-score and a 4.2% improvement in Top-3 Accuracy. By integrating CTI with the MITRE ATT&CK framework, researchers can shift from reactive to proactive cybersecurity strategies. This integration enables swift detection of emerging threats, enhances incident response effectiveness, and fortifies defensive measures against evolving cyber threats. Ultimately, the synergy between CTI and ATT&CK fosters a comprehensive approach to cybersecurity management in today’s dynamic threat landscape.

The topic of study, Intelligent Transportation System (ITS), focuses on using vehicle-to-environment communication to address severe traffic problems, such as safety and congestion difficulties. Pedestrians remain the most Vulnerable Road Users (VRUs). Moreover, pedestrian movements are more challenging to predict because humans can quickly change their direction and status (e.g., walking or stopping). In this research, we build a system called PedCross, which uses human image semantic information to predict the behavior of pedestrians (i.e., crossing or not crossing). In PedCross, images of pedestrians are first used to detect skeletons. The features in the detected skeletons are then extracted for model training. Two types of models, Random Forest and LSTM, are considered for pedestrian crossing prediction. To further improve the efficiency and accuracy of PedCross, Skip Frame, Head Orientation, and Warning/Dangerous Zones are integrated. PedCross is tested with the collected ITRI dataset and deployed on the auto-driving bus for a road test. The road test indicates that PedCross achieves all the requirements set forth by ITRI and outperforms Free Space, a baseline system developed by ITRI.

We consider a potential communication problem in national security, where wireless spy sensors with eavesdropping capability are strategically deployed around an area of interest. For counterintelligence, achieving secure communication by penetrating such a spy barrier is of great importance. In this paper, we first formulate the problem of barrier penetration routing against spy barriers consisting of strategically deployed wireless sensors. We point out that existing multi-path avoidance routing protocols cannot efficiently counteract collusion attacks, where connected adversaries collaborate with each other to compromise data packets. We propose a barrier penetration routing (BPR) protocol to securely penetrate the barrier of adversaries. In the protocol, a set of physically distanced paths are identified based on distance vectors as well as network-wide flooding. Then, each data packet encoded by XOR coding is routed via a different path. Unlike existing avoidance routing, the proposed scheme does not rely on the assumption that the adversary's locations are known. The simulation results demonstrate that the proposed BPR outperforms the baseline protocol as well as existing routing protocols in terms of secure delivery rate.

Opportunistic network-based mobile crowdsourcing (MCS) outsources location-based human tasks to a crowd of workers, where workers with mobile devices opportunistically have contact with the server. While a number of task assignment algorithms have been proposed for different objectives, real-timeness is not considered. In this article, we are interested in real-time MCS (RT-MCS), in which tasks can be generated at any time step, and task assignment is performed in real-time. We first model an abstract RT-MCS and then instantiate the real-time task assignment problem for opportunistic network-based RT-MCS. A generic real-time task assignment (RTA) algorithm is designed based on the principle of the greedy approach, where each task is assigned to the best worker with the highest expected completion probability. To understand the fundamental performance issues, we formulate closed-form solutions for task completion probability as well as delay. In addition, we identify the critical condition that illuminates the busy state and the not-busy state of an RT-MCS. Furthermore, the analytical and simulation results demonstrate that our analysis yields close approximation of simulation results.

With the growing consumption of online visual contents, there is an urgent need for video translation in order to reach a wider audience from around the world. However, the materials after direct translation and dubbing are unable to create a natural audio-visual experience since the translated speech and lip movement are often out of sync. To improve the viewing experience, an accurate automatic lip-movement synchronization generation system is necessary. To improve the accuracy and visual quality of speech to lip generation, this research proposes two techniques: Embedding Attention Mechanisms in Convolution Layers and Deploying SSIM as Loss Function in Visual Quality Discriminator. The proposed system as well as several other ones are tested on three audiovisual datasets. The results show that our proposed methods achieve superior performance over the state-of-the-art speech to lip synthesis on not only the accuracy but also the visual quality of audio-lip synchronization generation.

Protecting the privacy of users is one of the most important issues in recommender systems, where new items, e.g., books, movies, and friends in online social networking service/sites, are recommended to target users. To identify recommended items, encryption-based privacy-preserving collaborative filtering is widely used to generate recommendations. However, existing solutions are either slow or not scalable. To tackle this issue, in this paper, we first propose a privacy-preserving user-based CF protocol using the BGV fully homomorphic encryption scheme, which is named BGV-CF. By reducing interactions and the amount of communication traffic among users and recommendation servers, the proposed BGV-CF protocol significantly facilitates the recommendation process. Then, we propose an optimized BGV-CF (OBGV-CF) protocol where some computations are offloaded to users during the recommendation process. The security of the proposed schemes is qualitatively analyzed and quantitative analyses of the computation and communication costs are performed. In addition, provable security analysis using random oracles is provided. The BGV-CF and OBGV-CF protocols are implemented using C++, and testbeds using the MovieLens dataset are conducted. Experimental results demonstrate that the proposed BGV-CF and OBGV-CF successfully achieve their design goals.

In this paper, we propose privacy-preserving user-based CF protocols using the BGV fully homomorphic encryption scheme, named BGV-CF and optimized BGV-CF (OBGV-CF), in order to protect privacy of users in recommender systems. The proposed schemes are implemented by C++, and testbeds using the MovieLens dataset demonstrate that the proposed protocols successfully achieve their design goals.

By predicting the air pollutant concentration, people can take precautions to avoid overexposure to air pollutants. Consequently, accurate$\mathbf{PM}_{2.5}$prediction becomes more important. In this paper, we propose a$\mathbf{PM}_{2.5}$prediction system, which utilizes the dataset from EdiGreen Airbox and Taiwan EPA. Our$\mathbf{PM}_{2.5}$prediction system is composed of four parts: data collection, data preprocessing, prediction model construction, and Line platform. To assess the performance of the model prediction, the daily average error and the hourly average accuracy for the duration of a week are calculated. The experimental results show that LSTM based on K- means has the best performance among all methods. Therefore, LSTM based on K-means is chosen to provide real-time$\mathbf{PM}_{2.5}$prediction through the Linebot.

An RFID system is a contactless automatic identification technology and a solution to many business and personal applications. However, the privacy threats to RFID systems cause significant concerns in real RFID applications. Among the privacy issues in RFID systems, we are particularly interested in designing an RFID authentication protocol that achieves strong indistinguishability-based privacy (ind-privacy) and possesses the zero-knowledge property for active tags. In this article, a zero-knowledge authentication protocol (ZKAP) using quadratic residues is proposed. Then, an indistinguishability-based experiment using random oracles is modeled to prove that the proposed scheme achieves strong ind-privacy against active eavesdroppers. The zero-knowledge property is defined with respect to the tags’ private information against dishonest readers, and that property of our ZKAP is proven by constructing a polynomial time simulator that generates a transcript with the same distribution as that of ZKAP. Finally, the proposed protocol is implemented in a Raspberry Pi and a PC, and the performance is evaluated in terms of authentication time. The experimental results demonstrate that our ZKAP achieves its design goals.

The progress of industrial development naturally leads to the demand for more electrical power. Unfortunately, due to the fear of the safety of nuclear power plants, many countries have relied on thermal power plants, which will cause more air pollutants during the process of coal burning. This phenomenon as well as increased vehicle emissions around us, have constituted the primary factors of serious air pollution. Inhaling too much particulate air pollution may lead to respiratory diseases and even death, especially PM$_{2.5}$. By predicting the air pollutant concentration, people can take precautions to avoid overexposure to air pollutants. Consequently, accurate PM$_{2.5}$prediction becomes more important. In this study, we propose a PM$_{2.5}$prediction system, which utilizes the dataset from EdiGreen Airbox and Taiwan EPA. Autoencoder and Linear interpolation are adopted for solving the missing value problem. Spearman’s correlation coefficient is used to identify the most relevant features for PM$_{2.5}$. Two prediction models (i.e., LSTM and LSTM based on K-means) are implemented which predict PM$_{2.5}$value for each Airbox device. To assess the performance of the model prediction, the daily average error and the hourly average accuracy for the duration of a week are calculated. The experimental results show that LSTM based on K-means has the best performance among all methods. Therefore, LSTM based on K-means is chosen to provide real-time PM$_{2.5}$prediction through the Linebot.

Mobile users and wireless devices are now the sources of a large volume of data. In such data-intensive mobile and wireless computing systems, delay-tolerant network (DTN) routing plays a critical role in data routing, dissemination, and collection. In this paper, we first introduce a new routing problem in DTNs - data-intensive routing - where data transmitted from one node to another is very large with respect to the size of data which can be transmitted in a single contact and available buffer size at relay nodes. In the proposed opportunistic path model, the contact frequency, contact duration, and buffer constraint are all integrated into a single routing metric. Then, we design the data-intensive routing (DIR) protocol where the path with the highest bottleneck link capacity is defined as the path weight. In addition, we propose the advanced DIR (A-DIR) protocol which focuses on the probability that the last message block will be delivered to its destination within the time constraint. Both the DIR and A-DIR protocols forward messages to better relays or to their destinations based on a greedy strategy with the proposed path metric. Simulations using real mobility traces demonstrate that the proposed DIR and A-DIR protocols achieve their design goals.

Designing fast and secure RFID private authentication with structured key management is one of the most essential components for RFID-enabled large-scale object management. Since group keys are shared by some tags in structured key-based authentication, physical tampering of tags, so called the compromise attack, may enable the adversary to obtain group keys stored in the compromised tags, which in turn can be used to distinguish other tags. All existing structured key-based protocols try to reduce the common group key effect to preserve high privacy. However, the theoretical bound of weak privacy achievable by structured key-based authentication remains unknown. In this paper, we investigate weak privacy in RFID authentication. To this end, we first formulate a mathematical model which identifies the probability of two tags being linked with respect to the number of group keys. Our model shows that the existing solutions are far from the ultimate goal in weak privacy. Then, we propose a k-neighbor graph-based RFID authentication (KNGA) protocol, where random walk over a k-neighbor graph is performed. In addition, we show that KNGA achieves the performance bound, and we then quantify the degree of privacy by anonymity. Finally, the extensive simulations demonstrate that the proposed protocol successfully achieves its design goals.

Due to instability of links in the network, the model of delaytolerant networks (DTNs) is often adopted in many emerging mobile applications. To organizations and individuals using these mobile applications, security and privacy are considered to be two of the most significant concerns. This research seeks to address anonymous communications in DTNs. While many different anonymous routing protocols have been proposed for ad hoc networks, to the best of our knowledge, only variants of onion-based routing have been tailored for DTNs. Since each type of anonymous routing protocol has its pros and cons, there is no single anonymous routing protocol for DTNs that can adapt to the different levels of security requirements. In this paper, we first design a set of anonymous routing protocols for DTNs, called anonymous Epidemic and zone-based anonymous routing, based on the original anonymous routing protocols for ad hoc networks. Then, we propose a framework of anonymous routing (FAR) for DTNs, which subsumes all the aforementioned protocols. By tuning its parameters, the proposed FAR is able to outperform onion-based, anonymous Epidemic, and zone-based routing. In addition, numerical analyses for the traceable rate, node anonymity, and path anonymity models are built. Extensive simulations using randomly generated graphs as well as real traces demonstrate that the proposed framework for DTNs successfully achieves its design goals.

Due to software implementation failure and misuse of cryptography, data encryption can no longer be considered a safeguard from security attacks. As a result, adversaries with eavesdropping capability along a routing path can compromise data privacy. In addition, should an adversary be one of the intermediate relay nodes in a path, she can deny data forwarding to disconnect the end-to-end communications. One solution is to avoid message routing through certain insecure areas, such as malicious countries or likely-compromised nodes. To this end, an avoidance routing based on the single path has been proposed. However, this single-path-based protocol relies on the availability of a safe path, i.e., no adversary is in the proximity of the whole path, which is difficult to achieve and therefore limits the routing opportunity. To tackle this issue, we propose an avoidance routing framework, namely timer-based multi-path avoidance routing (TMPAR). In our approach, a source node first encodes a message into k different pieces, and each piece is sent via a different path. During its path discovery phase, a timer is used to efficiently discover a better set of paths. The destination can assemble the original message easily. Under the condition that no adversary obtains all the k pieces of the message, the proposed TMPAR can securely deliver a message to its destination in spite of eavesdropping. The extensive ns-2 simulation results demonstrate that our TMPAR achieves its design goals.

Mobile crowdsourcing outsources large-scale tasks to a crowd of mobile users. To efficiently perform such tasks, many studies on tasks assignment have been conducted. However, the existing task assignment algorithms assume that each task can be completed by single worker. In reality, there are many types of tasks, including disaster recovery, which must be processed by multiple workers. To tackle this issue, we first introduce the problem of collaborative-task assignment, where multiple workers must collaborate to perform each task. To the best of our knowledge, this paper is the first work that address such a problem. Then, we propose a collaborative-task assignment (CTA) algorithm to efficiently complete a set of collaborative tasks by minimizing the idle times of workers. The simulation results using the real mobility traces as well as random graphs demonstrate that the proposed CTA algorithm achieves its design goals.

Mobile crowdsourcing in opportunistic networks outsources location-based tasks, such as taking photos and surveying Wi-Fi signal characteristic at points of interests, to a crowd of workers. The performance of tasks assignment is generally evaluated by the makespan. However, not only the makespan, but also the quality of performed tasks is important. Therefore, in this paper, we propose two task assignment schemes. One is the quality-aware task assignment (QA-TA) that tries to maximize the quality of tasks for given a deadline; the other is the minimum quality threshold task assignment (MQT-TA) that tries to minimize the makespan for a given minimum quality requirement. To this end, we apply the optimal stopping, which is one of the widely used techniques in mathematics, to the algorithm designs. The simulations using real mobility traces demonstrates that the proposed schemes successfully achieve their design goals.

In this paper, we seek to address anonymous communications in delay tolerant networks (DTNs). While many different approaches for the internet and ad hoc networks, to the best of our knowledge, only variants of onion-based routing have been tailored for DTNs. Since each type of anonymous routing protocol has its advantages and drawbacks, there is no single anonymous routing protocol for DTNs that can adapt to the different levels of security requirements. In this paper, we first design a set of anonymous routing protocols for DTNs, called anonymous epidemic and zone-based anonymous routing, based on the original anonymous routing protocols for ad hoc networks. Then, we propose a framework of anonymous routing (FAR) for DTNs, which subsumes all the aforementioned protocols. By tuning its parameters, the proposed FAR is able to outperform onion-based, anonymous Epidemic, and zone-based routing.

Security and privacy issues are considered to be two of the most significant concerns to organizations and individuals using mobile applications. In this paper, we seek to address anonymous communications in delay tolerant networks (DTNs). While many different anonymous routing protocols have been proposed for ad hoc networks, to the best of our knowledge, only variants of onion-based routing have been tailored for DTNs. Since each type of anonymous routing protocol has its advantages and drawbacks, there is no single anonymous routing protocol for DTNs that can adapt to the different levels of security requirements. In this paper, we first design a set of anonymous routing protocols for DTNs, called anonymous Epidemic and zone-based anonymous routing, based on the original anonymous routing protocols for ad hoc networks. Then, we propose a framework of anonymous routing (FAR) for DTNs, which subsumes all the aforementioned protocols. By tuning its parameters, the proposed FAR is able to outperform onion-based, anonymous Epidemic, and zone-based routing. In addition, numerical analyses for the traceable rate and node anonymity models are built. Extensive simulations using randomly generated graphs as well as real traces are conducted to demonstrate that given appropriate parameter settings, our FAR outperforms all the existing anonymous routing protocols for DTNs.

Delay tolerant networks (DTNs) are widely adopted to many network applications, such as disaster recovery and battlefield communications. Such critical network scenarios call for an outright prevention mechanism against contact-based attacks, e.g., blackmailing a legitimate user to compromise sensitive information at a contact. To the best of our knowledge, there is no work on secure routing protocol against contact-based attacks in DTNs. Therefore, in this paper, we first formulate the problem of contact avoidance routing, in which the node holding a message tries to avoid having a contact with an adversary. By applying the phase-type distribution, we build the secure opportunistic path model, which integrates the delivery probability within the deadline and the safety of opportunistic paths. Then, we propose a contact avoidance routing (CAR) protocol to securely deliver a message to its destination against the contact-based compromise attack. In addition, we further propose an adaptive CAR (A-CAR) to accommodate complicated network scenarios, where the capabilities of adversaries are parameterized. The extensive simulations using real traces as well as random graphs demonstrate that the proposed CAR and A-CAR protocols achieve their design goals.

For electric vehicles and hybrid vehicles in recent years, there has been a significant increase in the costs of implementing EMC technology to protect Controller Area Networks (CANs) from the influence of switching noise from power converter circuits. In this paper, we have investigated a method that aims to work with EMC in the future, from the perspective of dependable computing technology, to reduce its costs. Here, we define a fault model based on an experiment in which noise emitted from the DC-DC converter was applied to a CAN bus, and propose an application layer-based hybrid communications protocol consisting of an Automatic Repeat reQuest (ARQ), Forward Error Correction (FEC), and HALT mode. In this protocol, there is a transition to a suitable mode by reference to send/receive error counters incorporated in the CAN node. In an experiment in which noise was applied to a CAN node using simulated noise application circuits, we measured the total number of overhead frames and non-utilization rate, and we demonstrate the benefit of the proposed protocol in terms of increased fault tolerance when sending data frames in a highly electromagnetic environment.

Delay tolerant network (DTN) routing provides a communication primitive in intermittently disconnected networks, such as battlefield communications and human-contact networks. In these applications, the anonymity preserving mechanism, which hides the identities of communicating parties, plays an important role as a defense against cyber and physical attacks. While anonymous routing protocols for DTNs have been proposed in the past, to the best of our knowledge, there is no work that emphasizes analysis of the performance of these protocols. In this paper, we first design an abstract of anonymous routing protocols for DTNs and augment the existing solution with multi-copy message forwarding. Then, we construct simplified mathematical models, which can be used to understand the fundamental performance and security guarantees of onion-based anonymous routing in DTNs. To be specific, the delivery rate, message forwarding cost, traceable rate, and path and node anonymity are defined and analyzed. The numerical and simulation results using randomly generated contact graphs and the real traces demonstrate that our models provide very close approximations to the performance of the anonymous DTN routing protocol.

Delay tolerant network (DTN) routing provides a communication primitive in intermittently disconnected networks, such as battlefield communications and human-contact networks. In these applications, the anonymity preserving mechanism, which hides the identities of communicating parties, plays an important role as a defense against cyber and physical attacks. While anonymous routing protocols for DTNs have been proposed in the past, to the best of our knowledge, there is no work that emphasizes the theoretical aspects. In this paper, we first design an abstract of anonymous routing protocols for DTNs and augment the existing solution with multi-copy message forwarding. Then, we construct simplified mathematical models, which can be used to understand the fundamental performance and security guarantees of onion-based anonymous routing in DTNs. The numerical and simulation results using randomly generated contact graphs and the real traces demonstrate that our models provide very close approximations to the performance of the anonymous DTN routing protocol.

Privacy protection is the primary concern when RFID applications are deployed in our daily lives. Due to the computational power constraints of passive tags, non-encryption-based singulation protocols have been recently developed, in which wireless jamming is used. However, the existing private tag access protocols without shared secrets rely on impractical physical layer assumptions, and thus they are difficult to deploy. To tackle this issue, we first redesign the architecture of RFID system by dividing an RF reader into two different devices, an RF activator and a trusted shield device (TSD). Then, we propose a novel coding scheme, namely Random Flipping Random Jamming (RFRJ), to protect tags' content. Unlike the past work, the proposed singulation protocol utilizes only the physical layer techniques that are already implemented. Analyses and simulation results validate our distributed architecture with the RFRJ coding scheme, which defends tags' privacy against various adversaries including the random guessing attack, correlation attack, ghost-and-leech attack, and eavesdropping.

The performance of key authentication and the degree of privacy in large-scale RFID systems are considered by many researchers as tradeoffs. Based on how keys are managed in the system, the privacy preserving tag authentications proposed in the past can be categorized into tree-based and group-based approaches. While a tree-based approach achieves high performance in key authentication, it suffers from the issue of low privacy should a fraction of tags be compromised. On the contrary, while group-based key authentication is relatively invulnerable to compromise attacks, it is not scalable to a large number of tags. In this paper, we propose a new private tag authentication protocol based on skip lists, named randomized skip lists-based authentication (RSLA). Without sacrificing the authentication performance, RSLA provides a high privacy preserving mechanism. While RSLA provides the same level of unpredictability-based-privacy and indistinguishability-based privacy compared with other structured key management approaches, our scheme achieves the highest system anonymity with good performance in key look up and update. In addition, the simulation results match our analyses closely.

The speedy advancement in computer hardware has caused data encryption to no longer be a 100% safe solution for secure communications. To battle with adversaries, a countermeasure is to avoid message routing through certain insecure areas, e.g., Malicious countries and nodes. To this end, avoidance routing has been proposed over the past few years. However, the existing avoidance protocols are single-path-based, which means that there must be a safe path such that no adversary is in the proximity of the whole path. This condition is difficult to satisfy. As a result, routing opportunities based on the existing avoidance schemes are limited. To tackle this issue, we propose an avoidance routing framework, namely Multi-Path Avoidance Routing (MPAR). In our approach, a source node first encodes a message into k different pieces, and each piece is sent via k different paths. The destination can assemble the original message easily, while an adversary cannot recover the original message unless she obtains all the pieces. We prove that the coding scheme achieves perfect secrecy against eavesdropping under the condition that an adversary has incomplete information regarding the message. The simulation results validate that the proposed MPAR protocol achieves its design goals.

The performance of key authentication and the degree of privacy in large-scale RFID systems are considered by many researchers as tradeoffs. Based on how keys are managed in the system, the privacy preserving tag authentications proposed in the past can be categorized into tree-based and group-based approaches. While a tree-based approach achieves high performance in key authentication, it suffers from the issue of low privacy should a fraction of tags be compromised. On the contrary, while group-based key authentication is relatively invulnerable to compromise attacks, it is not scalable to the large number of tags. In this paper, we propose a new private tag authentication protocol based on skip lists, named Randomized Skip Lists-based Authentication. Without sacrificing the authentication performance, our scheme provides a strong privacy preserving mechanism.

Today Radio Frequency Identification (RFID) technologies are applied in many fields for a variety of applications. Though bringing great productivity gains, RFID systems may cause new security and privacy threats to individuals or organizations. Therefore, it is important to protect the security of RFID systems and the privacy of RFID tag owners. Unfortunately, none of the existing solutions provide a complete defense against eavesdroppers who could monitor the communication between RFID readers and tags and recover the contents of tags. Based on our research, we propose two novel RFID backward channel protection protocols, namely dynamic bit encoding and optimized dynamic bit encoding. Our schemes are able to achieve high anonymity with limited communication overhead. Our extensive simulations show that both proposed schemes provide much stronger backward channel protection than existing techniques. In addition, analytical models were created and validated through comparisons with simulation results.

At present, mobile devices are prevalent with end users and continuous media streaming services in mobile ad-hoc networks (MANETs) support popular applications. It is required for applications that stream isochronous media that the network link be continuously available. In this study, we introduce two group-server scheduling schemes to improve link continuity: static group-server scheduling and dynamic group-server scheduling . With our solution, if one of the current links between a client and a server instance breaks, the client can still download the multimedia content from another scheduled server peer. In addition, we incorporate the data link layer constraints as well as privacy concerns into our protocol design. The simulation results show that the proposed schemes significantly improve the effective link duration, overall system performance, and degree of privacy in MANETs.

The connected dominating set (CDS) has been extensively used for routing and broadcast in wireless ad hoc networks. While existing CDS protocols are successful in constructing CDS of small size, they either require localized information beyond immediate neighbors, lack the mechanism to properly handle nodal mobility, or involve lengthy recovery procedure when CDS becomes corrupted. In this paper, we introduce the timer-based CDS protocols, which first elect a number of initiators distributively and then utilize timers to construct a CDS from initiators with the minimum localized information. We demonstrate that our CDS protocols are capable of maintaining CDS in the presence of changes of network topology. Depending on the number of initiators, there are two versions of our timer-based CDS protocols. The Single-Initiator (SI) generates the smallest CDS among protocols with mobility handling capability. Built on top of SI, the Multi-Initiator (MI) version removes the single point of failure at single-initiator and possesses most advantages of SI. We evaluate our protocols by both the ns-2 simulation and an analytical model. Compared with the other known CDS protocols, the simulation results demonstrate that both SI and MI produce and maintain CDS of very competitive size. The analytical model shows the expected convergence time and the number of messages required by SI and MI in the construction of CDS, which match closely to our simulation results. This helps to establish the validity of our simulation.

Distributed Denial of Service (DDoS) attacks are one of the most damaging threats against Internet based applications. Many of the DDoS defense mechanisms may unintentionally deny a certain portion of legitimate user accesses by mistaking them as attackers or may simply not block enough traffic to adequately protect the victim. Other better performing systems have not yet to reach adoption because of designs that require a substantial investment into the Internet infrastructure before offering much effectiveness. This paper proposes Heimdall, a novel traffic verification based framework to protect legitimate traffic from bilateral damages. Based on a proof-of-work technique and application of distributed hash ID, aside from protecting established connections, our system can validate new initial request for communication and open valid channels between users and the protected server. Through intensive simulation experiments on the ns-2 network simulator, we verified that Heimdall scheme can effectively protect legitimate communications and filter out malicious flows with very high accuracy.

The connected dominating set (CDS) has been commonly used for routing and broadcast in mobile ad hoc networks (MANETs). Considering the applications of MANETs, it is generally preferred that the CDS protocol not only creates CDS of small size, incurs less communication and computational overheads, adapts to the nodal mobility, but also generates CDS as quickly as possible. Unfortunately, none of the existing CDS protocols possesses all of these desirable properties. In this paper, we propose an algorithm to rapidly grow a CDS tree from an initiator. By incorporating this algorithm with the Multi-Initiator CDS protocol, we have created the CDS protocol for MANETs which enjoys all the aforementioned desirable properties. The simulation results validate that our proposed protocol achieves its design goals. In addition, an analytical model is provided that can accurately estimate the convergence time required by our proposed CDS protocol.

At present mobile devices are prevalent with end users and continuous media streaming services in mobile ad-hoc networks (MANETs) support popular applications. One of the requirements for applications that stream isochronous media is that the network link has to be continuously available. In this study, we introduce two group-server scheduling schemes to improve link continuity: static group-server scheduling and dynamic group-server scheduling. With our solution, if one of the current links between a client and a server instance breaks, the client can still download the multimedia content from another scheduled server peer. The simulation results show that the proposed schemes significantly improve the effective link duration in MANETs.

The connected dominating set (CDS) has been extensively used for routing and broadcast in mobile ad hoc networks. While existing CDS protocols are successful in constructing CDS of competitive size with localized information, they either lack the mechanism to properly handle nodal mobility or require lengthy period of time to recover when CDS becomes corrupted. In this paper, a novel protocol, namely Multi-Initiator Connected Dominating Set protocol (MI-CDS), is proposed that constructs and maintains CDS of competitive size efficiently without introducing much communication overhead. The simulation results demonstrate that MI-CDS permits CDS to be available for the highest percentage of time in the mobile network scenario compared with the other CDS protocols.