Biblio

Sybil attack is one of the most dangerous internal attacks in Vehicular Ad Hoc Network (VANET). It affects the function of the VANET network by maliciously claiming or stealing multiple identity propagation error messages. In order to prevent VANET from Sybil attacks, many solutions have been proposed. However, the existing solutions are specific to the physical or application layer's single-level data and lack research on cross-layer information fusion detection. Moreover, these schemes involve a large number of sensitive data access and transmission, do not consider users' privacy, and can also bring a severe communication burden, which will make these schemes unable to be actually implemented. In this context, this paper introduces FedMix, the first federated Sybil attack detection system that considers cross-layer information fusion and provides privacy protection. The system can integrate VANET physical layer data and application layer data for joint analyses simultaneously. The data resides locally in the vehicle for local training. Then, the central agency only aggregates the generated model and finally distributes it to the vehicles for attack detection. This process does not involve transmitting and accessing any vehicle's original data. Meanwhile, we also designed a new model aggregation algorithm called SFedAvg to solve the problems of unbalanced vehicle data quality and low aggregation efficiency. Experiments show that FedMix can provide an intelligent model with equivalent performance under the premise of privacy protection and significantly reduce communication overhead, compared with the traditional centralized training attack detection model. In addition, the SFedAvg algorithm and cross-layer information fusion bring better aggregation efficiency and detection performance, respectively.

While vehicle-to-everything communication technology enables information sharing and cooperative control for vehicles, it also poses a significant threat to the vehicles' driving security owing to cyber-attacks. In particular, Sybil malicious attacks hidden in the vehicle broadcast information flow are challenging to detect, thereby becoming an urgent issue requiring attention. Several researchers have considered this problem and proposed different detection schemes. However, the detection performance of existing schemes based on plausibility checks and neighboring observers is affected by the traffic and attacker densities. In this study, we propose a malicious attack detection scheme based on traffic-flow information fusion, which enables the detection of Sybil attacks without neighboring observer nodes. Our solution is based on the basic safety message, which is broadcast by vehicles periodically. It first constructs the basic features of traffic flow to reflect the traffic state, subsequently fuses it with the road detector information to add the road fusion features, and then classifies them using machine learning algorithms to identify malicious attacks. The experimental results demonstrate that our scheme achieves the detection of Sybil attacks with an accuracy greater than 90 % at different traffic and attacker densities. Our solutions provide security for achieving a usable vehicle communication network.

One of the main threats to the underwater communication cables identified in the recent years is possible tampering or damage by malicious actors. This paper proposes a solution with explanation abilities to detect and investigate this kind of threat within the evidence theory framework. The reasoning scheme implements the traditional “opportunity-capability-intent” threat model to assess a degree to which a given vessel may pose a threat. The scenario discussed considers a variety of possible pieces of information available from different sources. A source quality model is used to reason with the partially reliable sources and the impact of this meta-information on the overall assessment is illustrated. Examples of uncertain relationships between the relevant variables are modelled and the constructed model is used to investigate the probability of threat of four vessels of different types. One of these cases is discussed in more detail to demonstrate the explanation abilities. Explanations about inference are provided thanks to sensitivity spaces in which the impact of the different pieces of information on the reasoning are compared.

Firstly, this paper analyses the technical foundation of single sign-on solution of unified authentication platform, and analyses the advantages and disadvantages of each solution. Secondly, from the point of view of software engineering, such as function requirement, performance requirement, development mode, architecture scheme, technology development framework and system configuration environment of the unified authentication platform, the unified authentication platform is analyzed and designed, and the database design and system design framework of the system are put forward according to the system requirements. Thirdly, the idea and technology of unified authentication platform based on JA-SIG CAS are discussed, and the design and implementation of each module of unified authentication platform based on JA-SIG CAS are analyzed, which has been applied in ship cluster platform.

In order to avoid the situation that the diagnosis model based on single sensor data is easily disturbed by environmental noise and the diagnosis accuracy is low, an intelligent fault fusion diagnosis method for marine diesel engine is proposed. Firstly, the support vector machine which is optimized by genetic algorithm is used to learn the fault sample data from different sensors, then multiple fault diagnosis models and results can be got. After that, multiple groups of diagnosis results are taken as evidence bodies and fused by evidence theory to obtain more accurate diagnosis results. By analyzing the sample data obtained from the fault simulation experiment of marine diesel engine based on AVL BOOST software, the proposed method can improve the fault diagnosis accuracy of marine diesel engine and reduce the uncertainty value of diagnosis results.

To overcome the current cybersecurity challenges of protecting our cyberspace and applications, we present an innovative cloud-based architecture to offer resilient Dynamic Data Driven Application Systems (DDDAS) as a cloud service that we refer to as resilient DDDAS as a Service (rDaaS). This architecture integrates Service Oriented Architecture (SOA) and DDDAS paradigms to offer the next generation of resilient and agile DDDAS-based cyber applications, particularly convenient for critical applications such as Battle and Crisis Management applications. Using the cloud infrastructure to offer resilient DDDAS routines and applications, large scale DDDAS applications can be developed by users from anywhere and by using any device (mobile or stationary) with the Internet connectivity. The rDaaS provides transformative capabilities to achieve superior situation awareness (i.e., assessment, visualization, and understanding), mission planning and execution, and resilient operations.

A major challenge of the existing attack detection approaches is the identification of relevant information to a particular situation, and the use of such information to perform multi-evidence intrusion detection. Addressing such a limitation requires integrating several aspects of context to better predict, avoid and respond to impending attacks. The quality and adequacy of contextual information is important to decrease uncertainty and correctly identify potential cyber-attacks. In this paper, a systematic methodology has been used to identify contextual dimensions that improve the effectiveness of detecting cyber-attacks. This methodology combines graph, probability, and information theories to create several context-based attack prediction models that analyze data at a high- and low-level. An extensive validation of our approach has been performed using a prototype system and several benchmark intrusion detection datasets yielding very promising results.

Biometrics is attracting increasing attention in privacy and security concerned issues, such as access control and remote financial transaction. However, advanced forgery and spoofing techniques are threatening the reliability of conventional biometric modalities. This has been motivating our investigation of a novel yet promising modality transient evoked otoacoustic emission (TEOAE), which is an acoustic response generated from cochlea after a click stimulus. Unlike conventional modalities that are easily accessible or captured, TEOAE is naturally immune to replay and falsification attacks as a physiological outcome from human auditory system. In this paper, we resort to wavelet analysis to derive the time-frequency representation of such nonstationary signal, which reveals individual uniqueness and long-term reproducibility. A machine learning technique linear discriminant analysis is subsequently utilized to reduce intrasubject variability and further capture intersubject differentiation features. Considering practical application, we also introduce a complete framework of the biometric system in both verification and identification modes. Comparative experiments on a TEOAE data set of biometric setting show the merits of the proposed method. Performance is further improved with fusion of information from both ears.