A Fuzzy Trust Management Mechanism with Dynamic Behavior Monitoring for Wireless Sensor Networks

Traditional wireless sensor networks(WSNs)are typically deployed in remote and hostile environments for information collection.The wireless communication methods adopted by sensor nodes may make the network highly vulnerable to various attacks.Traditional encryption and authentication mechanisms cannot prevent attacks launched by internal malicious nodes.The trust-based security mechanism is usually adopted to solve this problem in WSNs.However,the behavioral evidence used for trust estimation presents some uncertainties due to the open wireless medium and the inexpensive sensor nodes.Moreover,how to efficiently collect behavioral evidences are rarely discussed.To address these issues,in this paper,we present a trust management mechanism based on fuzzy logic and a cloud model.First,a type-II fuzzy logic system is used to preprocess the behavioral evidences and alleviate uncertainty.Then,the cloud model is introduced to estimate the trust values for sensor nodes.Finally,a dynamic behavior monitoring protocol is proposed to provide a balance between energy conservation and safety assurance.Simulation results demonstrate that our trust management mechanism can effectively protect the network from internal malicious attacks while enhancing the energy efficiency of behavior monitoring.

A Behavior-Based Remote Trust Attestation Model

While remote trust attestation is a useful concept to detect unauthorized changes to software, the current mechanism only ensures authenticity at the start of the operating system and cannot ensure the action of running software. Our approach is to use a behavior-based monitoring agent to make remote attestation more flexible, dynamic, and trustworthy. This approach was mostly made possible by extensive use of process information which is readily available in Unix. We also made use of a behavior tree to effectively record predictable behaviors of each process. In this paper, we primarily focus on building a prototype implementation of such framework, presenting one example built on it, successfully find potential security risks in the run time of a ftp program and then evaluate the performance of this model.

A model of unsafe behavior in coal mines based on game theory

Behavior choice, coal mine monitoring, and control intensity are combined in a general mathematical model established from the perspective of a behavioral game. A case study is provided with effective conditions of monitoring provided. This paper defines the expected value difference of control return and behavior cost difference and discusses the measurement and optimization of variable indexes, including the monitoring intensity and costs of control. The results imply that the control of unsafe behavior can be more effective when monitoring and control of coal mines are both improved. Monitoring will be useful when the rewards for displaying safe behavior, and the monitoring of unsafe behavior, are improved to a high level.

AndroidCare: A Simple and Low Cost Assisted Living Solution

Population aging places a growing stress on society’s resources. There is a need for Assisted Living (AL) technologies that allow the elderly to live independently as long as possible. The AndroidCare open source project aims to explore what functionality can be provided in a low cost AL solution where no professional health organization is involved in the deployment or maintenance of the solution, nor in supervising the pa-tient;all these tasks are carried out by a relative of the elder. Therefore, in the system’s design simplicity of use has prevailed over having a lot of features. It is based on stan-dard off-the-shell commodity hardware (a smartphone) and it provides 1) assistance to the elder in complying with the treatment of chronic diseases;2) monitors and alerts of the occurrence of risk situations such as falls;and 3) simplifies the supervision of the elder’s therapy and behavior by the caregiver.

Research on Driver Monitoring Systems Based on Vital Signs and Behavior Detection

Aiming at drivers’dangerous driving behavior monitoring and health monitoring,this paper designs an intelligent steering wheel that can monitor dan-gerous driving behavior and a steering wheel sleeve that can monitor physical health.The MTCNN model is primarily used to obtain a driver’s face image in real time.The PFLD algorithm was used to obtain the facial model positioning feature points,and the degree of driver fatigue was determined by combining the relevant parameters.The fatigue algorithm proposed in this paper can improve the effectiveness and accuracy of monitoring.Then,according to the LSTM network model,11 groups of key point information of the human body are obtained,and the human motion track is identified and then combined with the facial information to complete the judgment of driving behavior such as drinking water,smoking and walking.Through the PPG and ECG fusion algorithm based on LSTM,the reliability of the system to collect vital signs such as body temperature,blood pressure,heart rate and blood oxygen of the driver is improved.It was determined that the system could monitor a driver’s driving behavior in real time and consider its health management.

Apparel Shines for the Holidays Monitoring America's Attitudes and Behavior

Mixed messages about the nation’s economic health and tightened house- hold budgets have kept consumers cautious for most of 2010. Consumers have pegged their conf idence in the economy to the labor market, which has shown few signs of improvement.

A Hierarchical P2P Model and a Data Fusion Method for Network Security Situation Awareness System

A hierarchical peer-to-peer（P2P）model and a data fusion method for network security situation awareness system are proposed to improve the efficiency of distributed security behavior monitoring network.The single point failure of data analysis nodes is avoided by this P2P model,in which a greedy data forwarding method based on node priority and link delay is devised to promote the efficiency of data analysis nodes.And the data fusion method based on repulsive theory-Dumpster/Shafer（PSORT-DS）is used to deal with the challenge of multi-source alarm information.This data fusion method debases the false alarm rate.Compared with improved Dumpster/Shafer（DS）theoretical method based on particle swarm optimization（PSO）and classical DS evidence theoretical method,the proposed model reduces false alarm rate by 3%and 7%,respectively,whereas their detection rate increases by 4%and 16%,respectively.

Malicious Code Detection Model Based on Behavior Association

Malicious applications can be introduced to attack users and services so as to gain financial rewards, individuals＇ sensitive information, company and government intellectual property, and to gain remote control of systems. However, traditional methods of malicious code detection, such as signature detection, behavior detection, virtual machine detection, and heuristic detection, have various weaknesses which make them unreliable. This paper presents the existing technologies of malicious code detection and a malicious code detection model is proposed based on behavior association. The behavior points of malicious code are first extracted through API monitoring technology and integrated into the behavior; then a relation between behaviors is established according to data dependence. Next, a behavior association model is built up and a discrimination method is put forth using pushdown automation. Finally, the exact malicious code is taken as a sample to carry out an experiment on the behavior＇s capture, association, and discrimination, thus proving that the theoretical model is viable.

Recognizing Malicious Behaviors of Chrome Extensions through Direct JavaScript Execution

There are a large number of extensions with many users in Google Chrome,which greatly enriches the functionalities of Chrome.However,due to inadequate security auditing,vulnerable updating mechanisms and time-delayed maintenance of Chrome Web Store,the store becomes a platform for attackers to distribute malicious extensions.Existing static analysis methods can hardly detect obfuscated codes and dynamic codes injected by extensions,while dynamic detection methods have low coverage due to the need to meet various constraints when extensions are being executed.We propose a method to analyze Chrome extension behaviors dynamically based on direct execution of Java Script(JS).The core idea of this method is to convert the analysis of the whole extension into the analysis of each JS in the extension,bypassing the constraints(e.g.language,region,URL)of the extension itself,and improving the coverage of detection.The analysis of more than 44000 extensions showed that the method can effectively identify predefined behaviors.Among them,20 extensions had access to malicious domains,1113 extensions injected advertisements and 381 extensions collected users’passwords or cookies.At the same time,the number of URL requests obtained from this method is 177893,which is 52.44%more than that from traditional dynamic analysis method.

Impact of different sampling rates on precise point positioning performance using online processing service

In this study,the effect of different sampling rates(i.e.observation recording interval)on the Precise Point Positioning(PPP)solutions in terms of accuracy was investigated.For this purpose,a field test was carried out inÇorum province,Turkey,on 11 September 2019.Within this context,a Geodetic Point(GP)was established and precisely coordinated.A static GNSS measurement was occupied on the GP for about 4-hour time at 0.10 second(s)/10 Hz measurement intervals with the Trimble R10 geodetic grade GNSS receiver.The original observation file was converted to RINEX format and then decimated into the different data sampling rates as 0.2 s,0.5 s,1 s,5 s,10 s,30 s,60 s,and 120 s.All these RINEX observation files were submitted to the Canadian Spatial Reference System-Precise Point Positioning(CSRS-PPP)online processing service the day after the data collection date by choosing both static and kinematic processing options.In this way,PPP-derived static coordinates,and the kinematic coordinates of each measurement epoch were calculated.The PPP-derived coordinates obtained from each decimated sampling intervals were compared to known coordinates of the GP for northing,easting,2D position,and height components.According to the static and kinematic processing results,high data sampling rates did not change the PPP solutions in terms of accuracy when compared to the results obtained using lower sampling rates.The results of this study imply that it was not necessary to collect GNSS data with high-rate intervals for many surveying projects requiring cm-level accuracy.