A Computational Model to Evaluate Honesty in Social Internet of Things

Trust in Social Internet of Things has allowed to open new horizons in collaborative networking, particularly by allowing objects to communicate with their service providers, based on their relationships analogy to human world. However, strengthening trust is a challenging task as it involves identifying several influential factors in each domain of social-cyber-physical systems in order to build a reliable system. In this paper, we address the issue of understanding and evaluating honesty that is an important trust metric in trustworthiness evaluation process in social networks. First, we identify and define several trust attributes, which affect directly to the honesty. Then, a subjective computational model is derived based on experiences of objects and opinions from friendly objects with respect to identified attributes. Based on the outputs of this model a final honest level is predicted using regression analysis. Finally, the effectiveness of our model is tested using simulations

Improving the Expected Performance of Self-Organization in a Collective Adaptive System of Drones using Stochastic Multiplayer Games

The Internet-of-Things (IoT) domain will be one of the most important domains of research in the coming decades. Paradigms continue to emerge that can employ self-organization to capitalize on the sheer number and variety of devices in the market. In this paper, we combine the use of stochastic multiplayer games (SMGs) and negotiation within two collective adaptive systems of drones tasked with locating and surveilling intelligence caches. We assess the use of an ordinary least squares (OLS) regression model that is trained on the SMG’s output. The SMG is augmented to incorporate the OLS model to evaluate integration configurations during negotiation. The augmented SMG is compared to the base SMG where drones always integrate. Our results show that the incorporation of the OLS model improves the expected performance of the drones while significantly reducing the number of failed surveillance tasks which result in the loss of drones

Filtering Dishonest Trust Recommendations in Trust Management Systems in Mobile Ad Hoc Networks

Trust recommendations, having a pivotal role in computation of trust and hence confidence in peer to peer (P2P) environment, if hampered, may entail in colossal attacks from dishonest recommenders such as bad mouthing, ballot stuffing, random opinion etc. Therefore, mitigation of dishonest trust recommendations is stipulated as a challenging research issue in P2P systems (esp in Mobile Ad Hoc Networks). In order to cater these challenges associated with dishonest trust recommendations, a technique named “intelligently Selection of Trust Recommendations based on Dissimilarity factor (iSTRD)” has been devised for Mobile Ad Hoc Networks.  iSTRD exploits  personal experience of an “evaluating node” in conjunction with majority vote of the recommenders. It successfully removes the recommendations of “low trustworthy recommenders” as well as dishonest recommendations of “highly trustworthy recommenders”. Efficacy of proposed approach is evident from enhanced accuracy of “recognition rate”, “false rejection” and “false acceptance”.  Moreover, experiential results depict that iSTRD has unprecedented performance compared to contemporary techniques in presence of attacks asserted

A Modified Approach by Using Prediction to Build a Best Threshold in ARX Model with Practical Application

تعتبر النماذج غير الخطية من الطرق المهمة في تحليل السلاسل الزمنية والتي تتميز بامكانيتها الواسعة في عملية التنبؤ لمختلف الظواهر منها الفيزيائية والهندسية والاقتصادية، من خلال دراسة خصائص الاضطرابات العشوائية فيها للتوصل الى التنبؤات بشكل دقيق. وفي هذا البحث تم بناء انموذج انحدار ذاتي مع متغيرا خارجي باستخدام العتبة Threshold كطريقة اولى، من خلال اسلوبين مقترحين تم توظيفهما لغرض تحديد افضل نقطة قطع (عتبة) هما ]التنبؤ الى الامام (Forecasting)  والتنبؤ من داخل السلسلة الزمنية (Prediction)  من خلال مؤشر نقطة العتبة[. بالاضافة الى استخدام نماذج B-J الموسمية الاعتيادية كطريقة ثانية اعتماداً على مبدأ الاسلوبين المقترحين في تحديد افضل انموذج موسمي. والمقارنة مع النماذج المستحصلة عليها من الاسلوبين المذكورة اعلاه للطريقتين، من خلال مجموعة من المعايير وهي AIC،MDL ،Loss Function، BIC،  FPE، MSE بالاضافة الى معايير المقارنة الموزون Weighted Comparison Criteria المقترحة، لتحديد افضل انموذج لتمثيل بيانات البحث والمتمثلة بسرعة الرياح كمتغير مدخل والاتربة والغبار كمتغير مخرج والخاصة بمحطة بغداد للفترة من شهر كانون الثاني 1956 ولغاية شهر كانون الاول 2012.The proposal of nonlinear models is one of the most important methods in time series analysis, which has a wide potential for predicting various phenomena, including physical, engineering and economic, by studying the characteristics of random disturbances in order to arrive at accurate predictions. In this, the autoregressive model with exogenous variable was built using a threshold as the first method, using two proposed approaches that were used to determine the best cutting point of [the predictability forward (forecasting) and the predictability in the time series (prediction), through the threshold point indicator]. B-J seasonal models are used as a second method based on the principle of the two proposed approaches in determining the best seasonal model. Then they are compared with the obtained models from two methods that mentioned above of the two approaches within a group of the criteria as AIC, MDL, Loss Function, BIC, FPE, MSE, in addition the proposed weighted comparison criteria to determine the best model for representing the wind speed data as input variable, soil and dust as an output variable, in Baghdad Station from January 1956 to December 2012

Machine Learning based Trust Computational Model for IoT Services

The Internet of Things has facilitated access to a large volume of sensitive information on each participating object in an ecosystem. This imposes many threats ranging from the risks of data management to the potential discrimination enabled by data analytics over delicate information such as locations, interests, and activities. To address these issues, the concept of trust is introduced as an important role in supporting both humans and services to overcome the perception of uncertainty and risks before making any decisions. However, establishing trust in a cyber world is a challenging task due to the volume of diversified influential factors from cyber-physical-systems. Hence, it is essential to have an intelligent trust computation model that is capable of generating accurate and intuitive trust values for prospective actors. Therefore, in this paper, a quantifiable trust assessment model is proposed. Built on this model, individual trust attributes are then calculated numerically. Moreover, a novel algorithm based on machine learning principles is devised to classify the extracted trust features and combine them to produce a final trust value to be used for decision making. Finally, our model’s effectiveness is verified through a simulation. The results show that our method has advantages over other aggregation methods

Autoregression models for trust management in wireless ad hoc networks

In this thesis, we propose a novel trust management scheme for improving routing reliability in wireless ad hoc networks. It is grounded on two classic autoregression models, namely Autoregressive (AR) model and Autoregressive with exogenous inputs (ARX) model. According to this scheme, a node periodically measures the packet forwarding ratio of its every neighbor as the trust observation about that neighbor. These measurements constitute a time series of data. The node has such a time series for each neighbor. By applying an autoregression model to these time series, it predicts the neighbors future packet forwarding ratios as their trust estimates, which in turn facilitate it to make intelligent routing decisions. With an AR model being applied, the node only uses its own observations for prediction; with an ARX model, it will also take into account recommendations from other neighbors. We evaluate the performance of the scheme when an AR, ARX or Bayesian model is used. Simulation results indicate that the ARX model is the best choice in terms of accuracy

Trust Evaluation in the IoT Environment

Along with the many benefits of IoT, its heterogeneity brings a new challenge to establish a trustworthy environment among the objects due to the absence of proper enforcement mechanisms. Further, it can be observed that often these encounters are addressed only concerning the security and privacy matters involved. However, such common network security measures are not adequate to preserve the integrity of information and services exchanged over the internet. Hence, they remain vulnerable to threats ranging from the risks of data management at the cyber-physical layers, to the potential discrimination at the social layer. Therefore, trust in IoT can be considered as a key property to enforce trust among objects to guarantee trustworthy services. Typically, trust revolves around assurance and confidence that people, data, entities, information, or processes will function or behave in expected ways. However, trust enforcement in an artificial society like IoT is far more difficult, as the things do not have an inherited judgmental ability to assess risks and other influencing factors to evaluate trust as humans do. Hence, it is important to quantify the perception of trust such that it can be understood by the artificial agents. In computer science, trust is considered as a computational value depicted by a relationship between trustor and trustee, described in a specific context, measured by trust metrics, and evaluated by a mechanism. Several mechanisms about trust evaluation can be found in the literature. Among them, most of the work has deviated towards security and privacy issues instead of considering the universal meaning of trust and its dynamic nature. Furthermore, they lack a proper trust evaluation model and management platform that addresses all aspects of trust establishment. Hence, it is almost impossible to bring all these solutions to one place and develop a common platform that resolves end-to-end trust issues in a digital environment. Therefore, this thesis takes an attempt to fill these spaces through the following research work. First, this work proposes concrete definitions to formally identify trust as a computational concept and its characteristics. Next, a well-defined trust evaluation model is proposed to identify, evaluate and create trust relationships among objects for calculating trust. Then a trust management platform is presented identifying the major tasks of trust enforcement process including trust data collection, trust data management, trust information analysis, dissemination of trust information and trust information lifecycle management. Next, the thesis proposes several approaches to assess trust attributes and thereby the trust metrics of the above model for trust evaluation. Further, to minimize dependencies with human interactions in evaluating trust, an adaptive trust evaluation model is presented based on the machine learning techniques. From a standardization point of view, the scope of the current standards on network security and cybersecurity needs to be expanded to take trust issues into consideration. Hence, this thesis has provided several inputs towards standardization on trust, including a computational definition of trust, a trust evaluation model targeting both object and data trust, and platform to manage the trust evaluation process