An incentive mechanism for data sharing based on blockchain with smart contracts

© 2020 Data sharing techniques have progressively drawn increasing attention as a means of significantly reducing repetitive work. However, in the process of data sharing, the challenges regarding formation of mutual-trust relationships and increasing the level of user participation are yet to be solved. The existing solution is to use a third party as a trust organization for data sharing, but there is no dynamic incentive mechanism for data sharing with a large number of users. Blockchain 2.0 with smart contract has the natural advantage of being able to enable trust and automated transactions between a large number of users. This paper proposes a data sharing incentive model based on evolutionary game theory using blockchain with smart contract. The smart contract mechanism can dynamically control the excitation parameters and continuously encourages users to participate in data sharing

Hierarchically Authorized Transactions for Massive Internet-of-Things Data Sharing Based on Multilayer Blockchain

With the arrival of the Internet of Things (IoT) era and the rise of Big Data, cloud computing, and similar technologies, data resources are becoming increasingly valuable. Organizations and users can perform all kinds of processing and analysis on the basis of massive IoT data, thus adding to their value. However, this is based on data-sharing transactions, and most existing work focuses on one aspect of data transactions, such as convenience, privacy protection, and auditing. In this paper, a data-sharing-transaction application based on blockchain technology is proposed, which comprehensively considers various types of performance, provides an efficient consistency mechanism, improves transaction verification, realizes high-performance concurrency, and has tamperproof functions. Experiments were designed to analyze the functions and storage of the proposed system

Early Rumor Detection Based on Deep Recurrent Q-Learning

Online social networks provide convenient conditions for the spread of rumors, and false rumors bring great harm to social life. Rumor dissemination is a process, and effective identification of rumors in the early stage of their appearance will reduce the negative impact of false rumors. This paper proposes a novel early rumor detection (ERD) model based on reinforcement learning. In the rumor detection part, a dual-engine rumor detection model based on deep learning is proposed to realize the differential feature extraction of original tweets and their replies. A double self-attention (DSA) mechanism is proposed, which can eliminate data redundancy in sentences and words at the same time. In the reinforcement learning part, an ERD model based on Deep Recurrent Q-Learning Network (DRQN) is proposed, which uses LSTM to learn the state sequence features, and the optimization strategy of the reward function is to take into account the timeliness and accuracy of rumor detection. Experiments show that, compared with existing methods, the ERD model proposed in this paper has a greater improvement in the timeliness and detection rate of rumor detection

An Alert Aggregation Algorithm Based on Iterative Self-Organization

AbstractConsidering the problem that intrusion detection systems always produced duplicated alarm information, in this paper we propose an iterative self-organization clustering algorithm. It begins with calculating average value of classes as the new clustering center on the basis of random selection, merging and dividing dynamically, then finish the clustering procedure through the iteration finally. Experimental results with DARPA1999 testing data set show that the clustering method is more excellent than traditional clustering methods in both aggregation rate and error aggregation rate. Besides, it reduces duplicated alarm effectively and provides assistance to further related work

Performance Evaluation Model for Application Layer Firewalls.

Application layer firewalls protect the trusted area network against information security risks. However, firewall performance may affect user experience. Therefore, performance analysis plays a significant role in the evaluation of application layer firewalls. This paper presents an analytic model of the application layer firewall, based on a system analysis to evaluate the capability of the firewall. In order to enable users to improve the performance of the application layer firewall with limited resources, resource allocation was evaluated to obtain the optimal resource allocation scheme in terms of throughput, delay, and packet loss rate. The proposed model employs the Erlangian queuing model to analyze the performance parameters of the system with regard to the three layers (network, transport, and application layers). Then, the analysis results of all the layers are combined to obtain the overall system performance indicators. A discrete event simulation method was used to evaluate the proposed model. Finally, limited service desk resources were allocated to obtain the values of the performance indicators under different resource allocation scenarios in order to determine the optimal allocation scheme. Under limited resource allocation, this scheme enables users to maximize the performance of the application layer firewall

Thwarting Nonintrusive Occupancy Detection Attacks from Smart Meters

Occupancy information is one of the most important privacy issues of a home. Unfortunately, an attacker is able to detect occupancy from smart meter data. The current battery-based load hiding (BLH) methods cannot solve this problem. To thwart occupancy detection attacks, we propose a framework of battery-based schemes to prevent occupancy detection (BPOD). BPOD monitors the power consumption of a home and detects the occupancy in real time. According to the detection result, BPOD modifies those statistical metrics of power consumption, which highly correlate with the occupancy by charging or discharging a battery, creating a delusion that the home is always occupied. We evaluate BPOD in a simulation using several real-world smart meter datasets. Our experiment results show that BPOD effectively prevents the threshold-based and classifier-based occupancy detection attacks. Furthermore, BPOD is also able to prevent nonintrusive appliance load monitoring attacks (NILM) as a side-effect of thwarting detection attacks

Mathematical Performance Evaluation Model for Mobile Network Firewall Based on Queuing

While mobile networks provide many opportunities for people, they face security problems huge enough that a firewall is essential. The firewall in mobile networks offers a secure intranet through which all traffic is handled and processed. Furthermore, due to the limited resources in mobile networks, the firewall execution can impact the quality of communication between the intranet and the Internet. In this paper, a performance evaluation mathematical model for firewall system of mobile networks is developed using queuing theory for a multihierarchy firewall with multiple concurrent services. In addition, the throughput and the package loss rate are employed as performance evaluation indicators, and discrete-event simulated experiments are conducted for further verification. Lastly, experimental results are compared to theoretically obtained values to identify a resource allocation scheme that provides optimal firewall performance and can offer a better quality of service (QoS) in mobile networks

AI-based Intrusion Detection for Intelligence Internet of Vehicles

With the development of intelligent technologies, Internet of Things (IoT) opens up a new era in the field of automotive networks, namely Internet of Vehicles (IoV). The main goal of IoV is to provide a secure and reliable network to vehicles so that users can enjoy various services. However, vulnerabilities and incomplete protection mechanisms have led to a proliferation of security threats against IoV networks. Intrusion detection technology is an effective protection solution for IoV security, especially when Artificial Intelligence (AI) technology has been introduced into intrusion detection study. This paper first briefly introduces the concept and features of IoV, and then reviews the related research on AI-based IoV intrusion detection systems (IDSs). Finally, we discuss the open challenges and future research directions