Pub Date : 2016-08-22DOI: 10.1109/ICCI-CC.2016.7862096
Rémi Canillas, R. Laurent, M. Faix, D. Vaufreydaz, E. Mazer
In the present paper we describe a bio-inspired non von Neumann controller for a simple sensorimotor robotic system. This controller uses a bitwise version of the Gibbs sampling algorithm to select commands so the robot can adapt its course of action and avoid perceived obstacles in the environment. The VHDL specification of the circuit implementation of this controller is based on stochastic computation to perform Bayesian inference at a low energy cost. We show that the proposed unconventional architecture allows to successfully carry out the obstacle avoidance task and to address scalability issues observed in previous works.
{"title":"Autonomous robot controller using bitwise gibbs sampling","authors":"Rémi Canillas, R. Laurent, M. Faix, D. Vaufreydaz, E. Mazer","doi":"10.1109/ICCI-CC.2016.7862096","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862096","url":null,"abstract":"In the present paper we describe a bio-inspired non von Neumann controller for a simple sensorimotor robotic system. This controller uses a bitwise version of the Gibbs sampling algorithm to select commands so the robot can adapt its course of action and avoid perceived obstacles in the environment. The VHDL specification of the circuit implementation of this controller is based on stochastic computation to perform Bayesian inference at a low energy cost. We show that the proposed unconventional architecture allows to successfully carry out the obstacle avoidance task and to address scalability issues observed in previous works.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113994375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/ICCI-CC.2016.7862029
Jean-Yves Bergier, Colette Faucher
Here is presented a model of message processing using one of the leading paradigms in social psychology of persuasion as main theoretical framework: the Elaboration Likelihood Model (ELM). Adapting this dual process theory to military context and actions and more specifically psychological operations allows developing a model taking into account many message characteristics as well as specific factors such as local culture of the audience. It focuses on measuring capacity and motivation of the agents to determine the effect of message sending on attitudes through a detailed cognitive treatment.
{"title":"Persuasive communication from a military force to local civilians: A PsyOps system based on the Elaboration Likelihood Model","authors":"Jean-Yves Bergier, Colette Faucher","doi":"10.1109/ICCI-CC.2016.7862029","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862029","url":null,"abstract":"Here is presented a model of message processing using one of the leading paradigms in social psychology of persuasion as main theoretical framework: the Elaboration Likelihood Model (ELM). Adapting this dual process theory to military context and actions and more specifically psychological operations allows developing a model taking into account many message characteristics as well as specific factors such as local culture of the audience. It focuses on measuring capacity and motivation of the agents to determine the effect of message sending on attitudes through a detailed cognitive treatment.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115157843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/ICCI-CC.2016.7862074
Muhammad Salman Khan, S. Siddiqui, R. McLeod, K. Ferens, W. Kinsner
Host Based Intrusion Detection Systems (HIDS) are gaining traction in discovering malicious software inside a host operating system. In this paper, the authors have developed a new cognitive host based anomaly detection system based on supervised AdaBoost machine learning algorithm. Particularly, information fractal dimension based approach is incorporated in the original AdaBoost machine learning algorithm to assign higher weight to the classifier that estimates wrong hypothesis. An agent based host sensor is developed that continuously gathers and extracts network profile of all the host processes and the modules spawned by each process of a Microsoft Windows 7 operating system. The main contributions of this paper are that a malware testing sandbox is developed using Microsoft native APIs and an information fractal (cognitive) based AdaBoost algorithm is designed and developed. Our results on empirical data set shows that the malware detection performance of the proposed algorithm outperforms original AdaBoost algorithm in detecting positives including the reduction of false negatives.
{"title":"Fractal based adaptive boosting algorithm for cognitive detection of computer malware","authors":"Muhammad Salman Khan, S. Siddiqui, R. McLeod, K. Ferens, W. Kinsner","doi":"10.1109/ICCI-CC.2016.7862074","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862074","url":null,"abstract":"Host Based Intrusion Detection Systems (HIDS) are gaining traction in discovering malicious software inside a host operating system. In this paper, the authors have developed a new cognitive host based anomaly detection system based on supervised AdaBoost machine learning algorithm. Particularly, information fractal dimension based approach is incorporated in the original AdaBoost machine learning algorithm to assign higher weight to the classifier that estimates wrong hypothesis. An agent based host sensor is developed that continuously gathers and extracts network profile of all the host processes and the modules spawned by each process of a Microsoft Windows 7 operating system. The main contributions of this paper are that a malware testing sandbox is developed using Microsoft native APIs and an information fractal (cognitive) based AdaBoost algorithm is designed and developed. Our results on empirical data set shows that the malware detection performance of the proposed algorithm outperforms original AdaBoost algorithm in detecting positives including the reduction of false negatives.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124818780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/ICCI-CC.2016.7862022
Y. Wang, Liang Chen
This paper proposes an image-to-image face recognition algorithm that uses Dual Linear Regression based Classification (DLRC) and an Electoral College voting approach. Each face image involved is first converted into a cluster of images; each image in the cluster is obtained by shifting the original image a few pixels. The similarity of a pair of face images can be measured by comparing the distance between the corresponding image clusters, which is calculated using DLRC approach. To further improve performance, each cluster of images, representing a single face image, is then partitioned into a union of clusters of sub images. DLRC is then used to measure similarities between corresponding sub-image clusters to provide temporary identity decisions; a voting approach is applied to make final conclusions. We have carried out experiments on a benchmark dataset for face recognition. The result demonstrates that the proposed approach works best in certain simple situations, while its performance is also comparable to known algorithms in complicated situations.
本文提出了一种基于双线性回归分类(Dual Linear Regression based Classification, DLRC)和选举人团投票方法的图像对图像人脸识别算法。所涉及的每张人脸图像首先被转换成一组图像;集群中的每个图像都是通过将原始图像移动几个像素来获得的。通过比较相应图像聚类之间的距离来衡量一对人脸图像的相似度，该方法采用DLRC方法计算。为了进一步提高性能，代表单个人脸图像的每个图像聚类随后被划分为子图像聚类的联合。然后使用DLRC测量相应子图像簇之间的相似性，以提供临时身份决策;采用投票的方法作出最后的结论。我们在人脸识别的基准数据集上进行了实验。结果表明，该方法在某些简单情况下效果最好，而在复杂情况下其性能也与已知算法相当。
{"title":"Image-to-image face recognition using Dual Linear Regression based Classification and Electoral College voting","authors":"Y. Wang, Liang Chen","doi":"10.1109/ICCI-CC.2016.7862022","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862022","url":null,"abstract":"This paper proposes an image-to-image face recognition algorithm that uses Dual Linear Regression based Classification (DLRC) and an Electoral College voting approach. Each face image involved is first converted into a cluster of images; each image in the cluster is obtained by shifting the original image a few pixels. The similarity of a pair of face images can be measured by comparing the distance between the corresponding image clusters, which is calculated using DLRC approach. To further improve performance, each cluster of images, representing a single face image, is then partitioned into a union of clusters of sub images. DLRC is then used to measure similarities between corresponding sub-image clusters to provide temporary identity decisions; a voting approach is applied to make final conclusions. We have carried out experiments on a benchmark dataset for face recognition. The result demonstrates that the proposed approach works best in certain simple situations, while its performance is also comparable to known algorithms in complicated situations.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122609872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/ICCI-CC.2016.7862047
H. Ching
Mankind has inherited the earth. But, up to now there is little hint on the meaning and the purpose of this inheritance. Science, thus far, has explained only a tiny fraction. The real issue is identified as the problem of surviving the permanently uncertain future. Technically, the problem can be called the Requirement of Permanence. All the knowledge discoveries of the past, the present, and the future and all the wisdom of the universe accumulated from the infinite past seem to be aimed at satisfying this Requirement of Permanence. And, by the best account, merely looking at the living system on earth, the problem seems to have been solved, as far as the universe in concerned. However, science, dealing with certainty, has missed the essence of the problem, which is solving the permanently uncertain future. The solution is expanded and further explained by Post-Science Social Science based on the solution of value, Post-Science Life or Computer Science based on the solution of complete automation, and Post-Creational Fuzzy Logic for describing the fuzzy reality resulting from the sacrifice of precision in the expansion of the range of tolerance of error of a creation for surviving all the possibilities of the uncertain future. The same solution for solving the problem of the uncertain future is used for solving the problems of financial crises and complexity crises, which will plague our society in the Age of Abundance. Post-science promotes the concept of a future cooperative society of abundance guided by knowledge. A knowledge-oriented, not power or money-oriented, society is needed to solve the problem of surviving the uncertain future permanently. Only knowledge will give mankind the right to inherit the earth and to connect to the wisdom of the universe.
{"title":"Knowledge-oriented society: The problem of surviving the permanently uncertain future and the Requirement of Permanence","authors":"H. Ching","doi":"10.1109/ICCI-CC.2016.7862047","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862047","url":null,"abstract":"Mankind has inherited the earth. But, up to now there is little hint on the meaning and the purpose of this inheritance. Science, thus far, has explained only a tiny fraction. The real issue is identified as the problem of surviving the permanently uncertain future. Technically, the problem can be called the Requirement of Permanence. All the knowledge discoveries of the past, the present, and the future and all the wisdom of the universe accumulated from the infinite past seem to be aimed at satisfying this Requirement of Permanence. And, by the best account, merely looking at the living system on earth, the problem seems to have been solved, as far as the universe in concerned. However, science, dealing with certainty, has missed the essence of the problem, which is solving the permanently uncertain future. The solution is expanded and further explained by Post-Science Social Science based on the solution of value, Post-Science Life or Computer Science based on the solution of complete automation, and Post-Creational Fuzzy Logic for describing the fuzzy reality resulting from the sacrifice of precision in the expansion of the range of tolerance of error of a creation for surviving all the possibilities of the uncertain future. The same solution for solving the problem of the uncertain future is used for solving the problems of financial crises and complexity crises, which will plague our society in the Age of Abundance. Post-science promotes the concept of a future cooperative society of abundance guided by knowledge. A knowledge-oriented, not power or money-oriented, society is needed to solve the problem of surviving the uncertain future permanently. Only knowledge will give mankind the right to inherit the earth and to connect to the wisdom of the universe.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122634320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/ICCI-CC.2016.7862062
Zuojin Li, Jun Peng, Liukui Chen, S. S. Tirumala
Under real driving conditions, the fatigue monitoring system based on drivers' video is highly affected by light environment, which deteriorates the registration of facial information and thus the accuracy of surveillance. This paper, on the basis of AAM (Active Appearance Model)-based face registration method, analyzes the reasons of its failure when lighting conditions change and proposes an improved AAM algorithm. The experiments show that the improved AAM method can well register driver's face under changing lighting environment, and thus improve the accuracy in fatigue state recognition based on drivers' videos.
{"title":"Light-adaptive face registration based on drivers' video","authors":"Zuojin Li, Jun Peng, Liukui Chen, S. S. Tirumala","doi":"10.1109/ICCI-CC.2016.7862062","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862062","url":null,"abstract":"Under real driving conditions, the fatigue monitoring system based on drivers' video is highly affected by light environment, which deteriorates the registration of facial information and thus the accuracy of surveillance. This paper, on the basis of AAM (Active Appearance Model)-based face registration method, analyzes the reasons of its failure when lighting conditions change and proposes an improved AAM algorithm. The experiments show that the improved AAM method can well register driver's face under changing lighting environment, and thus improve the accuracy in fatigue state recognition based on drivers' videos.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121992747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/ICCI-CC.2016.7862092
Luis Martin, Gustavo Torres, Gabriela E. López-Tolsa
Sustained attention is an important cognitive function for living organisms. This function allows them to stay focused on a task with a stable performance. This is the main reason that makes this function required by virtual creatures with realistic behaviours. This document presents a bio-inspired cognitive model for sustained attention which takes into account its impairment and performance enhancement factors. The impairment factors are given by the time on task, the circadian rhythm of sleep and by sleep deprivation. Furthermore, it makes use of the motivation as an enhancement factor to deal with the impairments. This model was designed using neuroscientific evidence about the brain structures related with sustained attention tasks and the processes around them. In order to validate the model, a simple psychological task was used to test the virtual agent behaviour. The presented model was designed to be a compatible sub-functionality of a more complex cognitive architecture.
{"title":"Bio-inspired cognitive model for sustained attention","authors":"Luis Martin, Gustavo Torres, Gabriela E. López-Tolsa","doi":"10.1109/ICCI-CC.2016.7862092","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862092","url":null,"abstract":"Sustained attention is an important cognitive function for living organisms. This function allows them to stay focused on a task with a stable performance. This is the main reason that makes this function required by virtual creatures with realistic behaviours. This document presents a bio-inspired cognitive model for sustained attention which takes into account its impairment and performance enhancement factors. The impairment factors are given by the time on task, the circadian rhythm of sleep and by sleep deprivation. Furthermore, it makes use of the motivation as an enhancement factor to deal with the impairments. This model was designed using neuroscientific evidence about the brain structures related with sustained attention tasks and the processes around them. In order to validate the model, a simple psychological task was used to test the virtual agent behaviour. The presented model was designed to be a compatible sub-functionality of a more complex cognitive architecture.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129518629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/ICCI-CC.2016.7862086
S. Tsumoto, S. Hirano
Application of attribute-oriented generalization to an informatation often lead to inconsistent results of rule induction, which can be viewed as generation of fuziness with partialization of attribute information. This paper focuses on fuzzy linguistic variables and proposes a solution for inconsistencies. The results show that domain ontology may play an important role in construction of linguistic variables.
{"title":"Construction of linguistic variables based on rule induction and concept hierarchy","authors":"S. Tsumoto, S. Hirano","doi":"10.1109/ICCI-CC.2016.7862086","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862086","url":null,"abstract":"Application of attribute-oriented generalization to an informatation often lead to inconsistent results of rule induction, which can be viewed as generation of fuziness with partialization of attribute information. This paper focuses on fuzzy linguistic variables and proposes a solution for inconsistencies. The results show that domain ontology may play an important role in construction of linguistic variables.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124033398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/ICCI-CC.2016.7862060
S. Wajid, A. Hussain, Kaizhu Huang, W. Boulila
The novel application of Local Energy-based Shape Histogram (LESH) feature extraction technique was recently proposed for breast cancer diagnosis using mammogram images [22]. This paper extends our original work to apply the LESH technique to detect lung cancer. The JSRT Digital Image Database of chest radiographs is selected for research experimentation. Prior to LESH feature extraction, we enhanced the radiograph images using a contrast limited adaptive histogram equalization (CLAHE) approach. Selected state-of-the-art cognitive machine learning classifiers, namely extreme learning machine (ELM), support vector machine (SVM) and echo state network (ESN) are then applied using the LESH extracted features for efficient diagnosis of correct medical state (existence of benign or malignant cancer) in the x-ray images. Comparative simulation results, evaluated using the classification accuracy performance measure, are further bench-marked against state-of-the-art wavelet based features, and authenticate the distinct capability of our proposed framework for enhancing the diagnosis outcome.
{"title":"Lung cancer detection using Local Energy-based Shape Histogram (LESH) feature extraction and cognitive machine learning techniques","authors":"S. Wajid, A. Hussain, Kaizhu Huang, W. Boulila","doi":"10.1109/ICCI-CC.2016.7862060","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862060","url":null,"abstract":"The novel application of Local Energy-based Shape Histogram (LESH) feature extraction technique was recently proposed for breast cancer diagnosis using mammogram images [22]. This paper extends our original work to apply the LESH technique to detect lung cancer. The JSRT Digital Image Database of chest radiographs is selected for research experimentation. Prior to LESH feature extraction, we enhanced the radiograph images using a contrast limited adaptive histogram equalization (CLAHE) approach. Selected state-of-the-art cognitive machine learning classifiers, namely extreme learning machine (ELM), support vector machine (SVM) and echo state network (ESN) are then applied using the LESH extracted features for efficient diagnosis of correct medical state (existence of benign or malignant cancer) in the x-ray images. Comparative simulation results, evaluated using the classification accuracy performance measure, are further bench-marked against state-of-the-art wavelet based features, and authenticate the distinct capability of our proposed framework for enhancing the diagnosis outcome.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131448460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/ICCI-CC.2016.7862076
Tao Liu, Gan Huang, Zheng Xu
As sensor nodes are usually installed in harsh environments and nobody attends them, the character means it's easy for the networks to be subjected to all kinds of attacks. Moreover, the networks have limited computer power, communication power and energies. Therefore, in the environment, a well-organized and lightweight user authentication is a vital issue when a user possesses right to use the data from sensor nodes. In this article, a new user authentication protocol using trust model and elliptic curve cryptography (ECC) for WSNs is submitted. The result of the trust model analysis indicates that the model can improve the model's ability of withstanding attacks from the malicious nodes. The results of safety analysis and performance analysis for our proposed user authentication protocol demonstrate that this protocol can be flexible to all sorts of common known attacks and performs similarly or better compared with some active user authentication protocols.
{"title":"A user authentication protocol combined with trust model and ECC for wireless sensor networks","authors":"Tao Liu, Gan Huang, Zheng Xu","doi":"10.1109/ICCI-CC.2016.7862076","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862076","url":null,"abstract":"As sensor nodes are usually installed in harsh environments and nobody attends them, the character means it's easy for the networks to be subjected to all kinds of attacks. Moreover, the networks have limited computer power, communication power and energies. Therefore, in the environment, a well-organized and lightweight user authentication is a vital issue when a user possesses right to use the data from sensor nodes. In this article, a new user authentication protocol using trust model and elliptic curve cryptography (ECC) for WSNs is submitted. The result of the trust model analysis indicates that the model can improve the model's ability of withstanding attacks from the malicious nodes. The results of safety analysis and performance analysis for our proposed user authentication protocol demonstrate that this protocol can be flexible to all sorts of common known attacks and performs similarly or better compared with some active user authentication protocols.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132736094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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