Red team testing, an effective proactive method for evaluating the security of multimodal large language models (MLLMs), requires an expanding toolkit alongside the development of MLLM safeguards.We propose the Reference Attack, a powerful tool for red team testing against MLLMs.The Reference Attack is a reference-guided cross-modal jailbreak method that enhances existing prompt-to-image injection attacks by exploiting MLLMs' semantic reconstruction capabilities.Our method embeds malicious prompts in non-text modalities (e.g., images, spreadsheets) and constructs recursive symbolic references in text, enabling MLLMs to gradually recover and generate harmful content through layered reference resolution.The attack introduces a new vector that circumvents conventional content moderation by exploiting MLLMs' lack of security checks during crossmodal reference resolution.We evaluate the Reference Attack on leading MLLMs, including ChatGPT, Gemini, Claude, and the widely used open-source LLaMA model, and achieved an attack success rate of over 93% across all tested models.Compared to state-of-the-art attacks, Reference Attack achieves higher success rates than all baselines under identical evaluation, with a maximum gain of 70.8%.Our study reveals a critical gap in MLLM security and highlights the need for strict security auditing of cross-modal interactions in future content moderation.

Adversarial patches pose a significant threat to deep neural networks (DNNs). Unlike conventional adversarial attacks that are digital and less effective in the real world, adversarial patches can disrupt DNNs in real-world scenarios with potentially catastrophic outcomes. Understanding the characteristics of these patches is crucial to comprehending this new form of adversarial attack. While prior research has primarily aimed at enhancing the success rate of adversarial patches on specific DNN models, the rise of federated learning (FL) introduces a novel attack vector. In this context, attackers could manipulate a DNN model’s learnable parameters by contributing models trained on poisoned data. To assess the feasibility and danger of adversarial patches in this context, we introduce a novel attack method named PoisonPatch. This method merges FL poisoning attacks with adversarial patch search, first poisoning a DNN-based image classifier through FL, and then employing an adversarial patch search algorithm to create patches that increase the success rate of the attacks. This dual approach, combining poisoning attacks with adversarial patches, results in patches that are more challenging for machines to detect than traditional poisoning attacks and less noticeable to the human eye than typical adversarial patches due to their natural appearance. Our extensive experimental results demonstrate that PoisonPatch surpasses current state-of-the-art methods, producing natural-looking patches while achieving a 100% attack success rate.

Deep neural networks (DNNs) for image classification remain vulnerable to adversarial perturbations–subtle input manipulations that induce catastrophic misclassifications. To address this issue, we propose the Adversarial Image Rectifier (AIR), a linguistically inspired detection and mitigation framework that enhances DNN robustness by intercepting and inverting adversarial perturbations at the feature level. Unlike existing defenses, AIR operates without prior knowledge of attack patterns: it first encodes hierarchical hidden-layer feature maps of a DNN into semantically structured sentence representations, then identifies adversarial inputs through “sentiment” anomalies in these sentences–a linguistic metaphor for subtle adversarial traces. Crucially, we pinpoint a pivotal intermediate layer where adversarial perturbations dominantly propagate and train a lightweight rectifier network to selectively nullify adversarial features at this layer while preserving benign semantics. Extensive experiments on Tiny-ImageNet, CIFAR-10, SVHN, and MS COCO demonstrate that AIR achieves a correction rate of up to 95.02% and 94.62% when defending against known attacks and unknown attacks, respectively, significantly surpassing existing defense techniques.

Deep neural networks (DNNs) are vulnerable to adversarial examples, while adversarial attack models, e.g., DeepFool, are on the rise and outrunning adversarial example detection techniques. This article presents a new adversarial example detector that outperforms state-of-the-art detectors in identifying the latest adversarial attacks on image datasets. Specifically, we propose to use sentiment analysis for adversarial example detection, qualified by the progressively manifesting impact of an adversarial perturbation on the hidden-layer feature maps of a DNN under attack. Accordingly, we design a modularized embedding layer with the minimum learnable parameters to embed the hidden-layer feature maps into word vectors and assemble sentences ready for sentiment analysis. Extensive experiments demonstrate that the new detector consistently surpasses the state-of-the-art detection algorithms in detecting the latest attacks launched against ResNet and Inception neutral networks on the CIFAR-10, CIFAR-100, and SVHN datasets. The detector only has about 2 million parameters and takes less than 4.6 ms to detect an adversarial example generated by the latest attack models using a Tesla K80 GPU card.

Sensory data is only meaningful if correctly associated with originating locations, e.g., in three-dimensional wireless sensor systems inside an aircraft cabin. Received signal strength is a cost-effective option to locate low-cost sensors due to its universal availability, but suffers from coarse ranging accuracy with multiplicative errors. This paper presents a new received signal strength-based approach to locating a large number of low-cost sensors given their possible three-dimensional installation points. Our approach achieves the accuracy which has not been achieved in the literature. The approach first cooperatively locates the sensors in the continuous three-dimensional spaces, then associates the continuous location estimates to the installation points, and finally refines the association with likelihood ascent search. A new convex relaxation-based optimization is designed for cooperative localization in continuous three-dimensional spaces. The Kuhn-Munkres algorithm is generalized for the association. Cramér-Rao Lower Bounds are derived to specify the local three-dimensional regions in which refinement is carried out to improve the final accuracy with little complexity overhead. The proposed approach is validated experimentally with signal strength measurements collected in a Fokker 100 passenger plane, achieves 100% accuracy in all experiments conducted, and outperforms state-of-the-art metaheuristics significantly with much shorter execution time. Note to Practitioners—This paper is motivated by a goal to realize various aircraft automation applications through three-dimensional localization and association of wireless sensors. For example, automatic identification of seat locations can be implemented efficiently with wireless sensors, which enables effortless re-association between control buttons in the seats and the corresponding functions after cabin refurbishment. Meanwhile, automatic detection of missing/misplaced safety equipment can be carried out to improve the efficiency of flight preparation. There currently lacks an received signal strength-based three-dimensional sensor association method in indoor environments. The theoretical framework proposed in this paper aims to utilize the readily available received signal strength measurements to associate Commercial-Off-The-Shelf sensors to their possible installation points. This approach avoids the requirement of labor-intensive fingerprinting effort and high-cost equipment. The proposed approach has been tested and evaluated using both synthetic data and real data collected in a passenger plane as a proof of concept.

Typical deep neural network (DNN) backdoor attacks are based on triggers embedded in inputs. Existing imperceptible triggers are computationally expensive or low in attack success. In this paper, we propose a new backdoor trigger, which is easy to generate, imperceptible, and highly effective. The new trigger is a uniformly randomly generated three-dimensional (3D) binary pattern that can be horizontally and/or vertically repeated and mirrored and superposed onto three-channel images for training a backdoored DNN model. Dispersed throughout an image, the new trigger produces weak perturbation to individual pixels, but collectively holds a strong recognizable pattern to train and activate the backdoor of the DNN. We also analytically reveal that the trigger is increasingly effective with the improving resolution of the images. Experiments are conducted using the ResNet-18 and MLP models on the MNIST, CIFAR-10, and BTSR datasets. In terms of imperceptibility, the new trigger outperforms existing triggers, such as BadNets, Trojaned NN, and Hidden Backdoor, by over an order of magnitude. The new trigger achieves an almost 100% attack success rate, only reduces the classification accuracy by less than 0.7%–2.4%, and invalidates the state-of-the-art defense techniques.

Aspect term extraction and opinion word extraction are two fundamental subtasks of aspect-based sentiment analysis. The internal relationship between aspect terms and opinion words is typically ignored, and information for the decision-making of buyers and sellers is insufficient. In this paper, we explore an aspect–opinion pair extraction (AOPE) task and propose a Question-Driven Span Labeling (QDSL) model to extract all the aspect–opinion pairs from user-generated reviews. Specifically, we divide the AOPE task into aspect term extraction (ATE) and aspect-specified opinion extraction (ASOE) subtasks; we first extract all the candidate aspect terms and then the corresponding opinion words given the aspect term. Unlike existing approaches that use the BIO-based tagging scheme for extraction, the QDSL model adopts a span-based tagging scheme and builds a question–answer-based machine-reading comprehension task for an effective aspect–opinion pair extraction. Extensive experiments conducted on three tasks (ATE, ASOE, and AOPE) on four benchmark datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches.

Typically, existing works utilize static methods to extract the latent feature representation of user and item reviews, neglecting the time signals and behavior patterns hidden in the user-item interaction history, which may fail to capture users' instant interests and items' temporal attributes. Moreover, there is no framework that unifies recent behavior sequences and reviews. Therefore, in this paper, we first define dynamic and static features to describe users' short- and long-term preferences and items' temporal and inherent attributes. We then design feature extractors to capture these latent factors simultaneously from recent behavior sequences and reviews. Then, we propose a novel unified framework to extract and fuse these fine-grained characteristics, named unified model with dynamic-static features (UMDSF). Specifically, the proposed model extracts both temporal sequence and review features by two parallel feature extractors based on self-attention and a multi-head attention mechanism. Subsequently, an adaptive fusion module is utilized to combine the fine-grained representations for the downstream recommendation tasks. Extensive experiments on four real-world datasets demonstrate the superiority of UMDSF and additional ablation studies verify the effectiveness of the components designed in the proposed model.

Sensory data is only meaningful if correctly associated with originating locations. Received signal strength (RSS) is a cost-effective option to locate low-cost sensors due to its universal availability, but yet to be practical because of its coarse ranging accuracy with multiplicative errors. This paper presents a new RSS-based approach to the localization and association of a large number of low-cost sensors given their possible 3D installation locations. The approach addresses the mathematically challenging combinatorial optimization of association by first co-operatively locating the sensors in the continuous 3D spaces, then associating the continuous location estimates to the installation points, and finally refining the association with likelihood ascent search. A new convex relaxation-based optimization is designed for cooperative localization in continuous 3D spaces. The Kuhn-Munkres algorithm is generalized for the association. Cramer-Rao Lower Bounds are derived to specify the local 3D regions in which refinement is carried out to improve the final accuracy with little complexity overhead. The proposed approach is validated through both computer simulation and lab test. It achieves close-to-100% accuracy in all experiments conducted, and outperforms state-of-the-art metaheuristics significantly.

Obtaining insight into user mobility for next point-of-interest (POI) recommendations is a vital yet challenging task in location-based social networking. Information is needed not only to estimate user preferences but to leverage sequence relationships from user check-ins. Existing approaches to understanding user mobility gloss over the check-in sequence, making it difficult to capture the subtle POI-POI connections and distinguish relevant check-ins from the irrelevant. We created a geographically-temporally awareness hierarchical attention network (GT-HAN) to resolve those issues. GT-HAN contains an extended attention network that uses a theory of geographical influence to simultaneously uncover the overall sequence dependence and the subtle POI-POI relationships. We show that the mining of subtle POI-POI relationships significantly improves the quality of next POI recommendations. A context-specific co-attention network was designed to learn changing user preferences by adaptively selecting relevant check-in activities from check-in histories, which enabled GT-HAN to distinguish degrees of user preference for different check-ins. Tests using two large-scale datasets (obtained from Foursquare and Gowalla) demonstrated the superiority of GT-HAN over existing approaches and achieved excellent results.

Traffic analysis is an effective mean for gathering intelligence from within a large enterprise’s local network. Adversaries are able to monitor all traffic traversing a switch by exploiting just one vulnerability in it and obtain valuable information (e.g., online hosts and ongoing sessions) for further attacking, while administrators have to patch all switches as soon as possible in hope of eliminating the vulnerability in time. Moving Target Defense (MTD) is a new paradigm for reobtaining the upper hand in network defense by dynamically changing attack surfaces of the network. In this paper, we propose U-TRI (unlinkability through random identifier) as a moving target technique for changing the information-leaking identifiers within PDUs for SDN network. U-TRI is based on VIRO protocol and implemented with the help of OpenFlow protocol. U-TRI employs an independent, binary tree-structured, periodically and randomly updating identifier to replace the first part of the static MAC address in PDU, and assigns unstructured random values to the remaining part of the MAC address. U-TRI also obfuscates identifiers in the network layer and transport layer in an unstructured manner. Such a semistructured random identifier enables U-TRI to significantly weaken the linkage between identifiers and end-hosts as well as communication sessions, thus providing anonymous communication in SDN network. The result of analysis and experiments indicates that U-TRI dramatically increases the difficulty of traffic analysis with acceptable burdens on network performance.

A matrix is a basic mathematical object that is widely used in various computations. When outsourcing expensive computations to untrusted parties, the involved matrix must be disguised before it's sent out in order to protect the privacy information in it. Some research works on secure computation had presented schemes for protecting the privacy in matrices. However, none of these schemes is defined deliberately for disguising a matrix and thus is neither highly efficient nor flexible. We propose a matrix-disguising method named FMD fast matrix disguising that has high time and space efficiency and can tune the trade-off between disguising speed and protecting strength with a parameter. FMD disguises a matrix by multiplying it with a semi-random non-singular matrix which is compose of many bar-shaped sub-matrices. Each of these sub-matrices contains a row/column of random elements with almost the same values. This special matrix structure allows FMD to disguise the original matrix with time complexity proportional to the size of the original matrix. While by adjusting the bar size of the sub-matrices, FMD can smoothly tune between high-disguising speed and high-privacy protection strength. The mathematical analysis and experimental results show that FMD is more efficient than the existing schemes and is especially suitable for resource-limited clients in privacy-preserving computation outsourcing scenarios. Copyright © 2015John Wiley & Sons, Ltd.

Abstract Denial‐of‐Service attacks continue to plague the Internet. Tracing an individual attack packet to its origin is an important step in defending against these attacks. For this reason, researchers have proposed several approaches for single‐packet IP traceback. Packet logging is a generic technique in these methods, which results in the high overhead at routers and low traceback accuracy. In this paper, we propose a novel path‐based approach for single‐packet IP traceback. Our approach makes use of the routing paths to set up traceback paths, instead of packet logging, so as to improve single‐packet IP traceback in several dimensions: (i) our storage overhead is only related to the number of routing paths, no matter how many packets traverse on them; (ii) the number of queried routers during the traceback process is only related to the number of hops in the attack path; (iii) the false positives in attack‐path construction can be negligible. We perform extensive mathematical analysis and simulations to evaluate our approach. The results show that our approach represents a step forward in preciseness and efficiency compared with the previous work. Copyright © 2013 John Wiley & Sons, Ltd.

Blocking attack flows to protect the threatened resources is a necessary step in defending against the Distributed Denial-of-Service DDoS attacks. Two kinds of reactive packet filtering technologies have been proposed as close to victim-end filtering and close to source-ends filtering. The first scheme only involves a single Active Filtering Routers AFRs but damages the whole network bandwidth resource; another extreme scheme requires millions of AFRs and thus degrades the network transmission performance, but it has the best defense effect. A feasible scheme should use a certain quantity of AFRs to filter attack flows between the victim end and the source ends. Going one step further, in this paper, we make the first effort on studying the filtering location to maximize the protected network bandwidth while not permitting any attack flow to reach the victim. We formulate this problem to an integer linear programming problem and design an efficient heuristic filtering location algorithm. We evaluate our algorithm through integrating it into the existing filtering architecture and implementing this integration scheme on the emulated DDoS scenarios based on real-world Internet topology. Our evaluation results show that compared to the state-of-the-art source-ends filtering scheme Active Internet Traffic Filtering, this integration scheme only uses 20% of its AFRs to achieve more than 70% of its protection effect. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT In recent years, large amounts of graph‐structured data have been outsourced to the commercial public cloud. It is a crucial requirement to enable substructure similarity query for effective data retrieval. However, for protecting data privacy, sensitive data have to be encrypted before outsourcing, which impedes the traditional similarity query schemes from being supported in cloud. Most existing works on encrypted cloud data retrieval pay little attention to this problem. Additionally, considering the huge amounts of encrypted data graphs, the complicated similarity computation and privacy requirements, it is particularly challenging to solve this problem effectively. In this paper, for the first time, we investigate the problem of privacy‐assured substructure similarity query over encrypted graph‐structured data in cloud computing. Our solution explores a secure framework and a series of secure algorithms to efficiently perform the substructure similarity query without privacy breaches. The proposed solution first builds a secure feature‐graph index to represent the feature‐related information about each encrypted data graph based on privacy homomorphism and obscuration methods and then calculates the similarity between the query graph and each data graph by the difference of feature frequency in a privacy‐preserving manner. Thorough analysis is given to investigate effectiveness and privacy guarantees, and the experiments with real dataset further demonstrate the validity and efficiency of the proposed solution. Copyright © 2013 John Wiley & Sons, Ltd.

Device-to-Device (D2D) communications make high-speed multicast services possible since the multicast receivers with poor downlink channel conditions can be retransmitted by devices nearby via D2D links. In this paper, we consider how to efficiently use D2D communications to help enhance the quality of wireless multicast services in cellular networks. To achieve this, a dynamic D2D retransmission scheme with maximized utility is proposed, which can adaptively select the retransmission algorithm according to the state of the network load. Through both analysis and simulations, we show that our algorithms achieve a significant gain in terms of utility, and reduce the burden of the base station (BS).

Multimedia Broadcast/Multicast Service (MBMS) uses point-to-multipoint transmission mode, which can effectively improve the frequency utilization efficiency of the mobile communication system and becomes a hot research field of mobile communication. However, as the frequency selectivity of the wireless channel, the throughput of the MBMS system is always limited by the user with the worst channel quality. In order to overcome the frequency selective fading, resource allocation methods is widely studied in the research. In this paper, the improved resource allocation algorithm based on user QoS is proposed. Firstly, the subcarriers are allocated according the users' urgency degree guaranteeing the QoS of all users, Secondly, the remaining subcarriers are allocated by the throughput maximization rule, Finally, the system throughput is further improved by the bit loading step with greedy algorithm. Simulation result shows that the proposed resource allocation scheme cannot effectively guarantee the QoS of all users in the group, but also can improve the system throughput as much as possible.

In this paper, the fairness problem of opportunistic multicast scheduling (OMS) is investigated and a parity-based OMS is proposed. In conventional multicast system, the system throughput is limited by the users with poor channel state information (CSI). In the proposed OMS, to improve the system throughput performance, the data reception of each user is taken into consideration by the base station (BS). Each user is assigned two aspects of priority, one of them is based on the system throughput, and the other is based on the data reception of users. For the users with poor channel condition, fewer packets would be received than the users with fine channel conditions. Thus, by raising the priority level of the user with worse channel condition, the performance of fairness among users can be improvement. Simulation results show that the proposed OMS can improve the system throughput performance while ensuring the fairness performance among users.

A wireless resources allocation method for Device-to-Multi-Device (D2MD) communication under the control of base station is proposed. In the premise of ensuring quality of services (QoS) of all D2MD groups, the method can allocate wireless resources for D2MD groups through three steps: mode selection, spectrum allocation and power control. Simulation results show that the method can select the high-spectrum-efficiency mode which reuses the spectrum of cellular users for most D2MD communications, and does not affect cellular communications, the interference to cellular uplink of proposed suboptimal LCH algorithm which allocates cellular spectrums for D2MD groups to reuse can achieve the same level as the optimal algorithm, in addition, the calculating complexity is lower therefore shortening the response time of the base station.

In this paper, new transmission schemes are proposed to deal with the CCI (co-channel interference) in wireless relay transmission. We first introduce an optimal decoding process to fully utilize the information forwarded by relay. Then based on conventional transmission schemes, two modified transmission schemes and a novel adaptive scheme are proposed. Furthermore, we derive the outage probabilities and throughput for all the proposed schemes in closed form. Numerical simulations are presented to validate the analytical results and it shows that the adaptive scheme improves the spectral efficiency significantly compared to others in most condition.

Two Opportunistic Multi-antenna Relay Selection (OMRS) strategies are proposed and analyzed in Decode-and-Forward (DF) relay networks for cooperative multicast using space time codes, assuming multiple antennas are available at the relay nodes. These two strategies select the best relay based on (i) maximizing the average SNR and (ii) maximizing the minimum SNR respectively. The closed-form expressions of multicast outage probability for the two strategies are studied over Rayleigh fading channel. Simulations are provided to verify the correctness of the theoretical analysis. Our multicast outage probability results reveal that OMRS based on maximizing the minimum SNR is outage-optimal among multi-antenna relay selection schemes and approaches the space time codes scheme. Moreover, it can be found that compared with previous single-antenna Opportunistic Relaying (OR) scheme, OMRS brings remarkable performance improvement obtained from maximum ratio combining (MRC) and space-time coding, which proves that multiple antennas at the relays could provide more array gain and diversity gain.

Most single-packet IP trace back approaches that have been proposed demand routers to log the packet digests to trace back, which lead to the linear growth of the storage overhead as the forwarded packets are increasing. This paper proposes a novel single-packet IP trace back approach based on the routing path to alleviate the burden of routers. Our approach introduces the relevant theories of label switching path in Multi-Protocol Label Switching (MPLS) and makes use of routing path to set up a Trace back Path (TP). During the trace back process, we can reconstruct the attack path on the basis of label switching mechanism. We use mathematical analysis and simulations to evaluate our approach. Our evaluation results show that compared to HIT and SPIE, two state-of-art single-packet trace back approaches, the storage overhead in our approach is only related to the number of routing paths, no matter how many packets traverse on the paths, and the number of queried routers during the trace back process is only related to the number of hops in an attack path.

Logging-based approaches are suitable for tracing single-packet attacks but incur heavy overhead for packet-digest storage as well as time overhead for both path recording and recovery. Marking-based approaches incur little trace back overhead but are unable to trace single-packet attacks. Recent researches suggest that hybrid approaches are more promising in efficiently tracing single-packet attacks. The major challenge lies in reducing storage and time overhead while maintaining single-packet trace back capability. We presented in this paper a more efficient hybrid approach by designing a novel path fragment encoding scheme using the orthogonality of Walsh matrix and the degree distribution characteristic of router-level topology. Compared to HIT, the most efficient hybrid approach for single-packet trace back to our best knowledge, our approach reduces 2/3 of the overhead in both storage and time for recording packet paths, and the time over-head for recovering packet paths is also reduced by a calculatable amount.

In this paper, we propose a location-aware relay selection (LARS) scheme based on geographical information in the distributed opportunistic relay (OR) communication system. The scheme aims to limit the contention among potential relays through dividing the whole relay selection region into some small nonoverlapping contending torus zones. If a relay lies in the specified contending zone and its local channels are better than the source-destination channel, it becomes a contending relay and will transmit a FLAG packet to signal the presence when its own timer expires. If no relay is selected in the previous contending zone, the current contending torus zone will be extended. The contention is an iterative course until the relay selection process completes. Performance analysis and simulation results show that the LARS scheme leads to a remarkable improvement in collision probability. Meanwhile the outage performance and consuming time of LARS scheme are closed to those of original OR scheme.

To avoid inter-cell interference effectively and to improve the performance of cell-edge users, a novel cellular architecture, termed Cellular Architecture Based on Sector Relay (CASR), is proposed in this paper, and two frequency planning schemes based on CASR are investigated. In CASR, fixed relays equipped with directional antennas are located at the vertex of the hexagonal cell for sectorization, thus each relay can be shared by three cells. The two frequency planning schemes are designed for different situations, one of which focuses more on the performance of cell one-hop users and the other one focuses on the performance balance between one-hop users and two-hop users. Interference analysis and performance evaluation are implemented for both one-hop users and two-hop users. System-level simulation in multi-cell and multi-user environment shows that compared with the existing cellular relay cell architecture, the proposed CASR combined with the two frequency planning schemes yield higher cell spectral efficiency. Moreover, the performance of cell-edge users is significantly improved.

In this paper, we investigate resource allocation issue in OFDMA-based decode-and-forward cooperative networks and propose joint subcarrier and power allocation schemes. The optimal solution of this combinable allocation has high computational complexity, so we divide our solution into two steps. The first step is to distribute subcarriers to relays and destination under the assumption of equal power distribution. Here, we propose Proportional Allocation (PA) strategy to achieve tradeoff between total throughput and fairness. To further improve the system performance, we introduce threshold into PA strategy, named Proportional Allocation with Threshold (PA-T), where subcarriers with bad performance are prevented from transmitting. Next, water-filling method is adopted to distribute the power to cooperative links in order to fully utilize the limited power. Simulation results show that system performance of the proposed schemes is significantly enhanced compared with an existing resource allocation scheme. Besides, the resource allocation schemes with water-filling method notably outperform schemes with equal power allocation.

According to the comprehensive analysis of the traditional DV-Hop, we propose an Iterative Cooperation DV-Hop localization (ICDV-Hop) algorithm, which improves the localization performance through adopting hop count threshold, collinearity test and new beacon nomination. The proposed algorithm selects the optimal beacon nodes for high localization accuracy by employing the hop count threshold to limit the distances between nodes and using collinearity degree to restrict topology relations between nodes. When the localization error of the located node is under the error threshold, it is nominated as new beacon to help the remaining unknown nodes to re-locate themselves, namely to expand localization coverage by iterative cooperation. Simulation results demonstrate that ICDV-Hop algorithm is more effective to improve the localization accuracy and coverage. Moreover, it is more reliable and robust compared to the traditional DV-Hop, especially when the ratio of beacon nodes is low and the network topology is sparse.

This paper compares disease with network vulnerability by their definitions and characteristics. A mapping between disease and vulnerability is built based on their similarities. We put forward a novel model of vulnerabilities in computer networks by simulating the reverse of cause-result of disease. Based on the model, a quantitative metric for vulnerabilities of computer networks is presented. The complexity of the algorithm for computing the metric is O(|V|2X|S|), where V and S stand for set of vulnerabilities and set of network states. By analyzing different structures of the vulnerability model, we found that the value reflecting vulnerability decreases when the model is more linear.