An optimized deep learning-based trust mechanism In VANET for selfish node detection

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jyothi N. ◽  
Rekha Patil
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
Computational Cost ◽  
Trust Model ◽  
Security And Privacy ◽  
Line Of Sight ◽  
Communication Overhead ◽  
Trust Mechanism ◽  
Content Type ◽  
Selfish Node

Purpose This study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection. Design/methodology/approach The authors built a deep learning-based optimized trust mechanism that removes malicious content generated by selfish VANET nodes. This deep learning-based optimized trust framework is the combination of the Deep Belief Network-based Red Fox Optimization algorithm. A novel deep learning-based optimized model is developed to identify the type of vehicle in the non-line of sight (nLoS) condition. This authentication scheme satisfies both the security and privacy goals of the VANET environment. The message authenticity and integrity are verified using the vehicle location to determine the trust level. The location is verified via distance and time. It identifies whether the sender is in its actual location based on the time and distance. Findings A deep learning-based optimized Trust model is used to detect the obstacles that are present in both the line of sight and nLoS conditions to reduce the accident rate. While compared to the previous methods, the experimental results outperform better prediction results in terms of accuracy, precision, recall, computational cost and communication overhead. Practical implications The experiments are conducted using the Network Simulator Version 2 simulator and evaluated using different performance metrics including computational cost, accuracy, precision, recall and communication overhead with simple attack and opinion tampering attack. However, the proposed method provided better prediction results in terms of computational cost, accuracy, precision, recall, and communication overhead than other existing methods, such as K-nearest neighbor and Artificial Neural Network. Hence, the proposed method highly against the simple attack and opinion tampering attacks. Originality/value This paper proposed a deep learning-based optimized Trust framework for trust prediction in VANET. A deep learning-based optimized Trust model is used to evaluate both event message senders and event message integrity and accuracy.

scholarly journals SenseCrypt: A Security Framework for Mobile Crowd Sensing Applications

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3280
Author(s):  
Nsikak Pius Owoh ◽  
Manmeet Mahinderjit Singh
Keyword(s):  
Computational Cost ◽  
Security And Privacy ◽  
Location Information ◽  
Communication Overhead ◽  
Spatial Coding ◽  
Mobile Crowd Sensing ◽  
Compression Technique ◽  
Sensing Applications ◽  
Crowd Sensing ◽  
Mobile Crowd

The proliferation of mobile devices such as smartphones and tablets with embedded sensors and communication features has led to the introduction of a novel sensing paradigm called mobile crowd sensing. Despite its opportunities and advantages over traditional wireless sensor networks, mobile crowd sensing still faces security and privacy issues, among other challenges. Specifically, the security and privacy of sensitive location information of users remain lingering issues, considering the “on” and “off” state of global positioning system sensor in smartphones. To address this problem, this paper proposes “SenseCrypt”, a framework that automatically annotates and signcrypts sensitive location information of mobile crowd sensing users. The framework relies on K-means algorithm and a certificateless aggregate signcryption scheme (CLASC). It incorporates spatial coding as the data compression technique and message query telemetry transport as the messaging protocol. Results presented in this paper show that the proposed framework incurs low computational cost and communication overhead. Also, the framework is robust against privileged insider attack, replay and forgery attacks. Confidentiality, integrity and non-repudiation are security services offered by the proposed framework.

scholarly journals A Trust Model Using Edge Nodes and a Cuckoo Filter for Securing VANET under the NLoS Condition

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 609
Author(s):  
Seyed Ahmad Soleymani ◽  
Shidrokh Goudarzi ◽  
Mohammad Hossein Anisi ◽  
Nazri Kama ◽  
Saiful Adli Ismail ◽  
...  
Keyword(s):  
Secure Communication ◽  
Trust Management ◽  
Vehicular Ad Hoc Networks ◽  
Nearest Neighbor ◽  
Negative Impact ◽  
Trust Model ◽  
Line Of Sight ◽  
Communication Overhead ◽  
K Nearest Neighbor ◽  
Cuckoo Filter

Trust, as a key element of security, has a vital role in securing vehicular ad-hoc networks (VANETs). Malicious and selfish nodes by generating inaccurate information, have undesirable impacts on the trustworthiness of the VANET environment. Obstacles also have a negative impact on data trustworthiness by restricting direct communication between nodes. In this study, a trust model based on plausibility, experience, and type of vehicle is presented to cope with inaccurate, incomplete and uncertainty data under both line of sight (LoS) and none-line of sight (NLoS) conditions. In addition, a model using the k-nearest neighbor (kNN) classification algorithm based on feature similarity and symmetry is developed to detect the NLoS condition. Radio signal strength indicator (RSSI), packet reception rate (PDR) and the distance between two vehicle nodes are the features used in the proposed kNN algorithm. Moreover, due to the big data generated in VANET, secure communication between vehicle and edge node is designed using the Cuckoo filter. All obtained results are validated through well-known evaluation measures such as precision, recall, overall accuracy, and communication overhead. The results indicate that the proposed trust model has a better performance as compared to the attack-resistant trust management (ART) scheme and weighted voting (WV) approach. Additionally, the proposed trust model outperforms both ART and WV approaches under different patterns of attack such as a simple attack, opinion tampering attack, and cunning attack. Monte-Carlo simulation results also prove validity of the proposed trust model.

scholarly journals Issues, Challenges, and Research Opportunities in Intelligent Transport System for Security and Privacy

2018 ◽  
Vol 8 (10) ◽  
pp. 1964 ◽  
Author(s):  
Qazi Ali ◽  
Naveed Ahmad ◽  
Abdul Malik ◽  
Gauhar Ali ◽  
Waheed Rehman
Keyword(s):  
Transport System ◽  
Traffic Management ◽  
Location Privacy ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Computational Cost ◽  
Security And Privacy ◽  
Communication Overhead ◽  
Intelligent Transport System ◽  
Intelligent Transport

Intelligent transport system (ITS), owing to their potential to enhance road safety and improve traffic management, have attracted attention from automotive industries and academia in recent years. The underlying technology—i.e., vehicular ad-hoc networks (VANETs)—provide a means for vehicles to intelligently exchange messages regarding road and traffic conditions to enhance safety. The open nature of ITS as wireless communication technology leads to many security and privacy challenges. These challenges pertain to confidentiality, authentication, integrity, non-repudiation, location privacy, identity privacy, anonymity, certificate revocation, and certificate resolution. This article aims to propose a novel taxonomy of security and privacy issues and solutions in ITS. Furthermore, categorization of security and privacy schemes in ITS and their limitations are discussed with various parameters—scalability, privacy, computational cost, communication overhead, latency—and various types of security attacks has been analyzed. This article leverages new researchers for challenges and opportunities related to security and privacy in ITS.

Hotel profitability: a multilayer neural network approach

2019 ◽  
Vol 11 (1) ◽  
pp. 35-48
Author(s):  
Rubén Lado-Sestayo ◽  
Milagros Vivel-Búa
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Performance Metrics ◽  
Public Access ◽  
Predictive Capacity ◽  
Neural Network Approach ◽  
Content Type ◽  
Tourist Destinations ◽  
Learning Techniques ◽  
Hotel Managers

Purpose The purpose of this paper is to design an algorithm to predict hotel profitability by means of deep learning techniques. Design/methodology/approach The methodology consists of a multi-layered neural network that includes a lag of profitability as the input. Furthermore, other input variables are related to hotel and tourist destinations; the raw data for hotel and tourist destinations were collected from multiple public access data sources. Findings The results show that the proposed model has a high predictive capacity of hotel profitability in all the years studied (2005-2011), according to the performance metrics evaluated within the sample. Thus, the authors can conclude that deep learning algorithms can be a useful tool to evaluate hotel performance. Practical implications The algorithm designed in this research could be of interest to improve decision-making processes related to profitability, for example, in evaluating the creation of new hotels. Moreover, the model provides a quick and efficient analyses that could be of interest to investors and lenders. In particular, they could compare investment alternatives in the hotel sector. Also, according to the results, the location variables are important determinants of hotel profitability, and consequently, hotel managers should collaborate with the tourist destination managers to improve profitability. From an internal perspective, hotel managers should focus on the management of human resources. Originality/value This paper is the first empirical study that predicts hotel profitability using deep learning techniques. In addition, this methodology is applied to analyse hotel profitability, for the first time, in the Spanish market. This market is an ideal analytical framework because of its heterogeneity with respect to hotel supply in terms of seasonality and coastal characteristics, among others.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

scholarly journals Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Gaoyang Li ◽  
Haoran Wang ◽  
Mingzi Zhang ◽  
Simon Tupin ◽  
Aike Qiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Artery Bypass ◽  
Computational Cost ◽  
Specific Model ◽  
Arterial System ◽  
Patient Specific ◽  
Cardiovascular Hemodynamics ◽  
Before And After ◽  
Deep Learning Network ◽  
Cfd Method

AbstractThe clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.

Human skill integrated motion planning of assembly manipulation for 6R industrial robot

2019 ◽  
Vol 46 (1) ◽  
pp. 171-180
Author(s):  
Yi Liu ◽  
Ming Cong ◽  
Hang Dong ◽  
Dong Liu
Keyword(s):  
Deep Learning ◽  
Motion Planning ◽  
Industrial Robot ◽  
Robotic Assembly ◽  
3D Vision ◽  
Content Type ◽  
Human Skill ◽  
Depth Images ◽  
Positioning Method ◽  
Assembly Technology

Purpose The purpose of this paper is to propose a new method based on three-dimensional (3D) vision technologies and human skill integrated deep learning to solve assembly positioning task such as peg-in-hole. Design/methodology/approach Hybrid camera configuration was used to provide the global and local views. Eye-in-hand mode guided the peg to be in contact with the hole plate using 3D vision in global view. When the peg was in contact with the workpiece surface, eye-to-hand mode provided the local view to accomplish peg-hole positioning based on trained CNN. Findings The results of assembly positioning experiments proved that the proposed method successfully distinguished the target hole from the other same size holes according to the CNN. The robot planned the motion according to the depth images and human skill guide line. The final positioning precision was good enough for the robot to carry out force controlled assembly. Practical implications The developed framework can have an important impact on robotic assembly positioning process, which combine with the existing force-guidance assembly technology as to build a whole set of autonomous assembly technology. Originality/value This paper proposed a new approach to the robotic assembly positioning based on 3D visual technologies and human skill integrated deep learning. Dual cameras swapping mode was used to provide visual feedback for the entire assembly motion planning process. The proposed workpiece positioning method provided an effective disturbance rejection, autonomous motion planning and increased overall performance with depth images feedback. The proposed peg-hole positioning method with human skill integrated provided the capability of target perceptual aliasing avoiding and successive motion decision for the robotic assembly manipulation.

An experimental study on feature-based SLAM for multi-legged robots with RGB-D sensors

2017 ◽  
Vol 44 (4) ◽  
pp. 428-441 ◽  
Author(s):  
Michał R. Nowicki ◽  
Dominik Belter ◽  
Aleksander Kostusiak ◽  
Petr Cížek ◽  
Jan Faigl ◽  
...  
Keyword(s):  
Performance Metrics ◽  
State Of The Art ◽  
Building Blocks ◽  
Bundle Adjustment ◽  
The State ◽  
Legged Robots ◽  
Evaluation Methodology ◽  
Walking Robots ◽  
Content Type ◽  
Feature Based

Purpose This paper aims to evaluate four different simultaneous localization and mapping (SLAM) systems in the context of localization of multi-legged walking robots equipped with compact RGB-D sensors. This paper identifies problems related to in-motion data acquisition in a legged robot and evaluates the particular building blocks and concepts applied in contemporary SLAM systems against these problems. The SLAM systems are evaluated on two independent experimental set-ups, applying a well-established methodology and performance metrics. Design/methodology/approach Four feature-based SLAM architectures are evaluated with respect to their suitability for localization of multi-legged walking robots. The evaluation methodology is based on the computation of the absolute trajectory error (ATE) and relative pose error (RPE), which are performance metrics well-established in the robotics community. Four sequences of RGB-D frames acquired in two independent experiments using two different six-legged walking robots are used in the evaluation process. Findings The experiments revealed that the predominant problem characteristics of the legged robots as platforms for SLAM are the abrupt and unpredictable sensor motions, as well as oscillations and vibrations, which corrupt the images captured in-motion. The tested adaptive gait allowed the evaluated SLAM systems to reconstruct proper trajectories. The bundle adjustment-based SLAM systems produced best results, thanks to the use of a map, which enables to establish a large number of constraints for the estimated trajectory. Research limitations/implications The evaluation was performed using indoor mockups of terrain. Experiments in more natural and challenging environments are envisioned as part of future research. Practical implications The lack of accurate self-localization methods is considered as one of the most important limitations of walking robots. Thus, the evaluation of the state-of-the-art SLAM methods on legged platforms may be useful for all researchers working on walking robots’ autonomy and their use in various applications, such as search, security, agriculture and mining. Originality/value The main contribution lies in the integration of the state-of-the-art SLAM methods on walking robots and their thorough experimental evaluation using a well-established methodology. Moreover, a SLAM system designed especially for RGB-D sensors and real-world applications is presented in details.

Trust Model Based on Feedback Evaluation in Cloud Manufacturing Environment

2011 ◽  
Vol 308-310 ◽  
pp. 1740-1745 ◽  
Author(s):  
Xiao Lan Xie ◽  
Liang Liu ◽  
Ying Zhong Cao
Keyword(s):  
Cloud Manufacturing ◽  
Trust Model ◽  
Human Society ◽  
Manufacturing Environment ◽  
Trust Mechanism ◽  
Model Based ◽  
Service Behavior ◽  
Relationship Of ◽  
Dynamic Adaptability ◽  
Feedback Evaluation

Aiming at the existing trust issues under manufacturing environment. This paper proposes a trust model based on feedback evaluation, TMBFCM, from the characteristics of human of trust relationship of human society. The model proposed a set of evaluation indicators of cloud manufacturing services properties, introduced the dynamic trust mechanism for attenuation by time,established the service which cloud manufacturing services providers provided and the feedback evaluation and incentive mechanism given by the user of cloud manufacturing service, improved the dynamic adaptability of the model. The results show that, compared with the existing trust model, the evaluation results are closer to the true service behavior of cloud manufacturing services provider, it can resist all kinds of malicious attacks acts effectively, demonstrated good robustness and recognition.

scholarly journals BeTrust: A Dynamic Trust Model Based on Bayesian Inference and Tsallis Entropy for Medical Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yan Gao ◽  
Wenfen Liu
Keyword(s):  
Bayesian Inference ◽  
Sensor Networks ◽  
Rapid Development ◽  
Trust Model ◽  
Tsallis Entropy ◽  
Single Node ◽  
Trust Mechanism ◽  
Trust Value ◽  
Medical Sensor Networks ◽  
Definition Of

With the rapid development and application of medical sensor networks, the security has become a big challenge to be resolved. Trust mechanism as a method of “soft security” has been proposed to guarantee the network security. Trust models to compute the trustworthiness of single node and each path are constructed, respectively, in this paper. For the trust relationship between nodes, trust value in every interval is quantified based on Bayesian inference. A node estimates the parameters of prior distribution by using the collected recommendation information and obtains the posterior distribution combined with direct interactions. Further, the weights of trust values are allocated through using the ordered weighted vector twice and overall trust degree is represented. With the associated properties of Tsallis entropy, the definition of path Tsallis entropy is put forward, which can comprehensively measure the uncertainty of each path. Then a method to calculate the credibility of each path is derived. The simulation results show that the proposed models can correctly reflect the dynamic of node behavior, quickly identify the malicious attacks, and effectively avoid such path containing low-trust nodes so as to enhance the robustness.

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