Mobile Crowd-Sensing (MCS) has emerged as a promising solution for large-scale data collection in Internet of Things (IoT) scenarios. However, the malicious behavior of smart terminals, such as providing corrupted and falsified data or deliberately spreading false data, poses a significant threat to the credibility of MCS services. At present the mainstream trust evaluation schemes evaluate its trust value through the accumulation of terminal interaction experience, but the inherent defects of these schemes will make MCS service suffer serious trust-decaying destruction. And the current trust computing methods do not yet take into account the balance between the quality of service completion and the crowd-sensing collaboration experience of the terminal. To solve these problems, this paper proposes a novel general grey buffer trust evaluation framework (TrustGrey), which is used to evaluate the trust relationship of smart terminals. Specially, we construct the trust value calculation model of grey trust state for smart terminal to make up for the inherent defects of normal trust value calculation model. The grey buffer of sudden drop is designed in the trust value calculation model to avoid trust-decaying destruction. And we design a quick recovery mechanism of grey buffer to avoid detecting false alarm caused by the sudden drop of trust, as well as avoiding the loss of multiple damage of intelligent malicious terminal by an irreversible black value reduction mechanism. Then, we design a supply-demand equilibrium based dynamic recruitment mechanism to dynamically coordinate the recruitment process of service requests by comprehensively considering the importance level of service requests and the credibility of terminals, so as to balance the experience of service originators and completion parties. Experiments on real word datasets highlight the advantages of our proposed framework TrustGrey. And, the experiments also show that TrustGrey has considerable versatility.

Multivariate time series data often demonstrate sparse and irregular characteristics in real-world signal processing applications, making anomaly detection challenging. This paper introduces STMG-AD, a spatio-temporal mixed graph neural controlled differential equation method with adaptive connection sampling, designed specifically for anomaly detection in irregular multivariate time series. By integrating causal graphs with graph attention networks and employing adaptive connection sampling coupled with Monte Carlo dropout, STMG-AD can enhance the robustness and accuracy of anomaly detection. Experiments on various real-world datasets demonstrate its superiority over existing methods in detecting anomalies in irregular multivariate time series.

With the increasing demand for access to and query of multimedia data, many applications rely on outsourcing data storage and queries to the cloud. Although efficient, this approach poses privacy risks to data owners. Researchers have sought secure methods for processing outsourced data, with the prerequisite of securely retrieving target data from datasets. In this paper, we address the performance limitations of existing three-party oblivious selection algorithms by proposing a novel online-efficient design. Furthermore, we optimize the proposed approach through algorithmic and system-level enhancements, achieving performance improvements across various settings.

To address the cybersecurity problems caused by encrypted traffic, current attack detection systems (ADSs) focus on non-decryption-based detection. However, non-decryption systems face problems including large training sample imbalances, which seriously affect ADS performance. To address these problems, we propose a structural causal model (SCM) for cyberattacks and define detection tasks such as eliminating noise features and providing interpretability. First, the influence of causal features on the results is enhanced by weighting the causal features, and low-weight noise features are removed to improve the causal interpretability of the detection results. Furthermore, samples are generated through a Wasserstein generative adversarial network (WGAN) that learns the causal feature distribution to balance the training samples. Finally, the detection performance is evaluated through the F1 score and interpretability indices. Our method achieves F1 score improvements on all datasets, and the causal relationships between cyberattacks and feature anomalies are explained through causal effects.

Multivariate time series data are produced in various domains, including AIOps, space crafts, and healthcare. Identifying anomalies within them is significant to ensure the stability of target systems. Current anomaly detectors mainly focus on devising intricate network structures based on recurrent neural networks, Transformers, or graph neural networks to model the temporal and inter-variate dependencies of the input time series. Nevertheless, complex models often lead to computational burden in training and inferencing stage. Also, system operators have to understand the technical details of these complex models to fine-tune them or add new modules for different needs. This motivates us to consider an intriguing question: can we construct an anomaly detection model merely based on simple networks? In this paper, we propose FlightAD, a light but effective anomaly detection model using only multi-layer perceptron (MLP) networks. FlightAD applies MLP networks on top of a multi-scale sampling strategy, followed by an informationfusing mechanism to fuse the learned features. More specific, the multi-scale sampling strategy is used to extract abundant temporal patterns of time series, and the MLP blocks applied to it can simultaneously model the dependencies along the time axis and dependencies between different sampled sub-sequences (i.e., different variables). Finally, we wield an informationfusing module to fully integrate the learned multi-scale features. Extensive experiments on six real-world datasets show that our model outperforms seven state-of-the-art competitors on average by 2.1%-18.2% in F1score and 3.8%-38.9% in AUC-PR.

Satellite Internet has broad emerging applications in aviation, marine, forest, disaster emergency and other fields. Since the openness of satellite Internet, it is extremely vulnerable to eavesdropping, replay, impersonation and other attacks, which may cause significant privacy and security concerns. To prevent malicious nodes from accessing and attacking satellite Internet, many authentication schemes have been proposed, but few of them consider the authentication and security of satellite nodes. In addition, most schemes assume that key management in network control center (NCC) is absolutely secure. But if it is maliciously attacked and the public-private key pairs are leaked, the entire system will crash immediately. In this paper, we propose CLASSI, a novel, secure and efficient certificateless authentication scheme for satellite Internet. CLASSI no longer assigns the complete public and private keys when users, satellites and gateway stations register, but uses their identity information to generate partial private keys. We make full use of the computing and storage capabilities of satellites to give satellites the ability to authenticate. We theoretically prove the security of our scheme through formal analysis and prove that our scheme can resist various of attacks. We also conducted extensive experiments and compared CLASSI with the existing schemes. The experimental results demonstrate that CLASSI is more suitable for user access in satellite Internet when meeting more security attributes.

Context-aware recommendation systems are of increasing popularity in the digital era to recommend personalized items to users. However, how to ensure user data privacy while remaining high recommendation accuracy is widely considered a challenge. In this work, we propose a privacy-preserving method for the context-aware recommendation system in the two-cloud model. In particular, we first adjust the standard additive secret sharing scheme to support secure negative integers computation, based on which we manage to design secure comparison protocol and division protocols that enjoy desirable security and efficiency. By using these new protocols, we propose a secure and efficient context-aware recommendation system that also supports offline users. Compared with the state-of-the-art, our scheme achieves stronger data privacy preservation by further protecting the intermediate data calculated during the system training. Experimental results on real-world datasets indicate that our scheme is efficient. Notable, our system could achieve more significant performance improvement by running the underlying schemes in parallel.

Due to factors such as differing distributions of training data and test data, false associations between features and weight associations lead to unstable detection performance and lack of generalization of network intrusion detection systems (NIDSs) based on machine learning (ML). To improve the stability and generalization of NIDSs, a detection system based on causal deep learning is proposed in this paper. First, causal weights were optimized by the propensity score through causal effects, the correlation between causal features and attack labels was increased, and the correlation between false correlation variables was weakened to improve the stability performance. Second, the approximate numerical optimization method of the Tammes problem was used to remove correlations between weights, maintain the independence of causal features, and improve the generalization of the detection system. Last, the feature distribution was disrupted by adding noise to four datasets to simulate different network environments. The results showed that our system can achieve good stability in various network environments where the training and testing datasets are not independently and identically distributed. In particular, after applying binary coding features and causal intervention (CIT) screening features, the average stability of the system improved by more than 10%.

In the communication environment of smart homes, personal data, control messages, and sensitive data are transmitted through wireless sensor networks (WSNs). Therefore, to prevent an invasion of privacy, communication has to be encrypted, and the data have to be stored securely. In this paper, we propose a new secure privacy-preserving authentication scheme for smart homes. We propose the concept of non-interactive chaotic zero-knowledge proof (NCZKP) and use it for our scheme to resist ephemeral secrets leakage (ESL) impersonation attack, which assures that the adversary can extract the sensitive information stored in gateway note, and use it to impersonate as a legal user. Also, the formal security analysis Random-or-real model is used to prove that our scheme is secure against different known attacks. In the end, according to the experiment, our scheme has low computation and communication costs compare with other related schemes.

Nowadays, most mobile terminals access the Internet via access points (APs) but AP is easy to become the performance bottleneck of the network. In-band full-duplex (IBFD) technique can theoretically double the network performance of an AP. However, how to solve the channel contention problem and fully utilize the channel resources under full-duplex mode at AP is a challenging problem. In this paper, we aim to exploit the full-duplex communication opportunities in AP- based wireless networks and present a novel medium access control (MAC) protocol named BiAP. To solve the channel contention problem, we first design a novel polling-based transmission mechanism and make comprehensive investigations on the effect of polling profile in full-duplex communication. Then, the channel contention problem is translated into finding a polling profile with the minimum transmission time and proved to be NP-Complete. Thus, we develop a stream-like heuristic algorithm to generate an efficient polling profile and it can work with the packet transmission procedure in parallel. By transmitting packets according to the generated polling profile, potential full-duplex opportunities are utilized. Simulation results show that our proposed MAC protocol can significantly improve the network throughput and reduce transmission delay, compared with state-of- the-art protocols.

The existing identity authentication of IoT devices mostly depends on an intermediary institution, i.e., a CA server, which suffers from the single-point-failure attack. Even worse, the critical data of authenticated devices can be tampered by inner attacks without being identified. To address these issues, we utilize blockchain technology, which serves as a secure tamper-proof distributed ledger to IoT devices. In the proposed method, we assign a unique ID for each individual device and record them into the blockchain, so that they can authenticate each other without a central authority. We also design a data protection mechanism by hashing significant data (i.e. firmware) into the blockchain where any state changes of the data can be detected immediately. Finally, we implement a prototype based on an open source blockchain platform Hyperledger Fabric to verify the proposed system.

Parameterized optimization problems (POPs) belong to a class of NP problems which are hard to be tackled by traditional methods. However, the relationship of the parameters (usually represented as k) makes a POP different from ordinary NP-complete problem in designing algorithms. In this paper, GEAS, an evolutionary computing algorithm (also can be seen as a framework) to solve POPs is proposed. This algorithm organically unifies genetic algorithm (GA) framework and the idea of evolutionary strategy (ES). It can maintain diversity while with a small population and has an intrinsic parallelism property:each individual in the population can solve a same problem that only has a different parameter. GEAS is delicately tested on an NP-complete problem, the Critical Link Set Problem. Experiment results show that GEAS can converge much faster and obtain more precise solution than GA which uses the same genetic operators.

ABSTRACT IP prefix hijacking remains a serious security threat to the traditional services in the Internet. It also harms the confidentiality and integrity of user data in Internet‐enabled cloud services because of its great dependence on Internet routing infrastructure. In addition, collaborations between networks in the cloud environment, especially in cross‐domain deployment, bring about new types of prefix hijacking attack, which may cause greater impact due to side‐effect of the cooperation of victim and infected autonomous systems. It is important to understand what impact a prefix hijacking attack can cause and how the number and locations of participants can affect the attacking results. In this paper, we model this problem as an attack planning task and solve it by applying a genetic algorithm. By analyzing the best solution to the problem, we find that the type of victims plays a more important role in IP prefix hijacking than that of attackers. Attackers can gain great impact even when the prefixes of a small number of victims are hijacked. For attack planning, the degree of an autonomous system is a major criterion to be considered. These findings are useful for securing cloud computing networks by preventing and eliminating IP prefix hijacking attacks. Copyright © 2013 John Wiley & Sons, Ltd.

Road networks have significant impact on mobility and network characteristics of wireless ad hoc networks. Discovering their characteristics and effects on mobility and network performance in urban environments is a fundamental research task. In this paper, we firstly study the graph attributes of road networks by sampling real road networks in main cities of Europe and USA. We propose a new graph metric, called characteristic central length, in order to estimate the average shortest-path length of a large-scale spatial network. We find that real road networks from Europe and USA have different patterns with regard to some graph attributes and a simple grid model is inadequate to describe them. Considering the diverse patterns of urban road networks caused by obstacles and shortcuts, we propose a random road network model, called the GRE model. The model is validated through fitting it to real road network samples using a genetic algorithm and simulation of delay-tolerant networks. The simulation results have shown that by extending the grid model with new probabilistic parameters, the GRE model has better capability on approximating real road networks. The simulation results have also shown that delay-tolerant networks operating on road networks may have better performance than scenarios without road networks.

Mobility is an important issue in the research of mobile delay-tolerant networks (DTNs). A simple grid model has been frequently used to simulate urban road networks in geographical restricted mobility models. However, by analyzing graph attributes of some urban road networks in main cities of Europe and USA, we discovered the discrepancy between real road network samples and the grid model. Based on the finding, we proposed a random graph-based road network model, called the Grid Model with Random Edges (GRE). The GRE model extends the basic grid model with new probabilistic parameters and thus has better capabilities to approximate real-world road networks. The model was validated through optimizing model parameter values using a genetic algorithm and comparing graph attributes of road networks generated by the model. It was demonstrated that the GRE model has better capability on approximating real road networks than the grid model, thus providing a better foundation for mobility modeling in mobile DTNs.

Due to the great dependence on Internet routing infrastructure, cloud services are vulnerable to IP prefix hijacking attacks which can destroy the confidentiality and integrity of user data. It is important to understand what impact a prefix hijacking attack can cause and how the number and locations of participants can affect the attacking results. In this paper, considering both attacking and detecting, we innovatively model this problem as an attack planning task, and solve it by applying a genetic algorithm. By analyzing the best solution to the problem, we find that the type of victims plays a more important role in IP prefix hijacking than that of attackers. We also find that attackers can gain great impact even when the prefixes of a small number of victims are hijacked. For attack planning, the degree of an AS is a major criterion to be considered. These findings are useful for securing cloud computing networks by preventing and eliminating IP prefix hijacking.

Mobility Model has drawn more and more attentions since its important role in Delay/Disruption Tolerant Networks (DTNs) routing protocol performance evaluation. In this paper, we first present a survey of various mobility models, and then analyze the movement characteristics in the disaster rescue scenario in detail. After that, we introduce a novel mobility model, the Random Way Point with Attraction Direction (RWPAD) Model to represent the rescue and transport groups in this special scenario. Then we investigate the impact of the mobility model on the performance evaluation of specific DTN protocols. To this end, we compare our models to the existing ones under five classic DTN routing protocols. The results indicate that different mobility patterns significantly affect the various protocols in different ways. So before a new routing protocol is deployed in a real situation, it should be tested under the scenario based mobility models. The model we proposed in this paper provides a relatively more realistic environment basis for DTN routing protocol research in the disaster rescue scenario than the existing ones.

Random walks are an excellent search mechanism in unstructured P2P network. However, it suffers long delay when searching files, especially for uncommon files. An efficient search mechanism - ARW is presented. ARW utilizes path information carrying and walker self-replication technologies to increase the number of different peers all walkers visit and reduces the delay which random walks spends on searching files especially for uncommon files greatly. Experimental results show that ARW reduces the delay of random walks on searching uncommon files by 63.7% with almost no extra overhead in a Gnutella-like overlay network and gets a good tradeoff between performance and overhead.

Numerous studies have shown the difficulty for a routing protocol to scale to large mobile ad hoc networks. This article proposes a cluster-based multipath dynamic source routing in MANET (CMDSR) that is designed to be adaptive according to network dynamics. It uses the hierarchy to perform route discovery and distributes traffic among diverse multiple paths. The CMDSR is based on a 2-level hierarchical scheme: the 1-cell cluster and 2-server cluster. The main idea of our proposition is to transfer the route discovery procedure to the 2-server level to prevent the network flooding due to the DSR route discovery. Thus, route discovery does not require flooding mechanism and overhead is minimized and improve the networks scalability.

Flooding in mobile ad hoc networks has poor scalability as it leads to serious redundancy, contention and collision. We propose an efficient approach to reduce the broadcast redundancy. In our approach, local topology information and the statistical information about the duplicate broadcasts are utilized to avoid unnecessary rebroadcasts. Simulation is conducted to compare the performance of our approach and flooding. The simulation results demonstrate the advantages of our approach. It can greatly reduce the redundant messages, thus saving much network bandwidth and energy. It can also enhance the reliability of broadcasting. It can be used in static or mobile wireless networks to implement scalable broadcast or multicast communications.