Reputation Scoring Research Articles

A Lightweight BT-Based Authentication Scheme for Illegal Signatures Identification in VANETs

Research related to vehicular ad hoc networks (VANETs) has received some significant attention in recent years. Despite all the advantages, the security and privacy in VANETs still become the main challenge that is widely open to discussion. The authentication scheme plays a substantial role to guarantee the security and privacy of information circulation and verification efficiency in VANETs. In this high-density environment, a scalability issue would emerge when the number of message-signature pairs received by a roadside unit (RSU) or vehicles becomes large. This issue happens because those entities cannot sequentially verify each received signature according to the required time limit. Some researchers believe that the symmetric cryptography-based authentication scheme provides a lightweight verification operation, which leads to low computation and communication costs. Combined with the batch verification process, this approach can be very beneficial. However, to the best of our knowledge, not many of those related schemes provide a realistic scenario regarding some illegal signatures’ appearance. Could the system identify the forged messages? Is it still efficient enough to do such an operation? In this paper, we propose a lightweight binary tree-based (BT-based) authentication scheme with a batch verification mechanism, that could efficiently identify a modest amount of illegal signatures in the sum of messages. To even improve the operation, we combine our BT-based batch verification scheme with our vehicle reputation scoring system. Given any <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> received messages with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</i> ≥ 1 illegal ones, the computation cost to verify all these messages only requires approximately (2[⌈lg <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> ⌉ - lg(⌈ b/2 ⌉2)] + 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⌈lg(⌈ b/2 ⌉2)⌉+1</sup> - 1) pairing operations, as the best-case scenario. By this approach, we can guarantee the best-case scenario (the most desirable condition) in our BT-based identification appear as much as possible. Hence, the computation cost can be kept low.

MOBILE CROWDSENSING FRAMEWORK FOR ROAD SURFACE QUALITY DETECTION.

Smartphones became ubiquitous and are used by so many people, at least to know the driving directions to their destination. Smartphones come with rich embedded sensors (e.g., GPS, accelerometer, and camera) as well as built-in radios (e.g., Bluetooth, Wi-Fi, and Cellular), which both enable users to gather data and distribute it among people at any time or location. These features have come up with the mobile crowdsensing (MCS) development which can be used in a wide range of applications. In this paper, we introduce a complete mobile crowdsensing framework for road surface condition detection. Various modules have been addressed such as task management, data fusion, reputation scoring, incentive awarding, security and privacy, as well as discussing popular techniques and algorithms utilized in the proposed MCS framework modules. A prototype of the crowd sensing application is designed which is related to our framework. The proposed framework considers the data quality and trustiness between the users and the server as well.

IP Reputation Scoring with Geo-Contextual Feature Augmentation

The focus of this article is to present an effective anomaly detection model for an encrypted network session by developing a novel IP reputation scoring model that labels the incoming session IP address based on the most similar IP addresses in terms of both network and geo-contextual knowledge. We provide empirical evidence that considering not only traditional network information but also geo-contextual information provides better threat assessment. The reputation scores provide a means to quantitatively capture good and bad IP behavior, making our model ideal for detecting malicious network behavior. With network encryption being the most practical solution to data security and privacy today, our approach expands the network administrator's ability to make decisions about IP addresses’ trustworthiness in an encrypted session with limited network information.

Promoting cooperation by reputation scoring mechanism based on historical donations in public goods game

In an acquaintance society, reputation plays an important role in individuals’ activities. Especially when we face with collective decision-making, such as crowd-funding. The existence of reputation could stimulate cooperation. In this work, individuals’ reputation has been divided into being good, bad and medium according to their neighbors’ judgement. Considering people with different requirements of reputation, we put forward a new reputation scoring mechanism with tolerance based on historical donations. First, we study the effect of different scoring rules on players’ strategies. Based on this, we discuss the effect of neighbors’ reputation on players’ simulation process. In addition, we explore the population structure’s influence on cooperation. Through numerical simulations, we are glad to find that the strict scoring rules could better stimulate cooperation in most circumstances. Moreover, imitation which depends on both reputation and payoff could promote cooperation. And individuals with good reputation have better effect on cooperation compared to those with bad reputation. This work provides a new reference for organizers to promote more people to participate and cooperate in Public Goods Game (PGG).

Multi-observable reputation scoring system for flagging suspicious user sessions

Conventionally, network and cloud infrastructure security is handled by firewalls which monitor traffic and block malicious access by matching certain observables, e.g., IP, and DNS, to blacklisted entries in intelligence databases. Therefore, such an approach fails to deal with emerging threats that utilize unclassified observables, and to report suspicious activities of individual users. In this paper we propose MuSeR, a novel approach to assign reputation scores for observables, even when no prior information is available, and flag suspicious sessions by conducting inter-observable analysis of user requests. In essence, MuSeR opts to assist network and cloud administrators mitigate attacks while avoiding unwarranted blocking of benign access. MuSeR achieves such an objective by associating session reputation scores based on the trustworthiness of the user navigation pattern, and conducting dynamic analysis of individual observables involved within requests. Specifically, MuSeR employs a new machine learning model for classifying observables using features specifically chosen to factor in evidence provided by blacklists, and access patterns of known attacks. To determine a request score, MuSeR maps the classifier probabilities to adaptive subjective logic and then uses multinomial fusion to leverage evidence from the different observables. Given the request scores, MuSeR further promotes a novel session reputation scoring model that uses three-valued subjective logic to handle trust propagation and aggregation over user requests. The effectiveness ofMuSeR is validated using a large dataset obtained from popular databases such as WHOIS, CYMUS, and passive DNS databases.

Qn: Quality and Quantity Based Unified Approach for Secure and Trustworthy Mobile Crowdsensing

A major challenge in mobile crowdsensing applications is the generation of false (or spam) contributions resulting from selfish and malicious behaviors of users, or wrong perception of an event. Such false contributions induce loss of revenue owing to undue incentivization, and also affect the operational reliability of the applications. To counter these problems, we propose an event-trust and user-reputation model, called QnQ, to segregate different user classes such as honest, selfish, or malicious. The resultant user reputation scores, are based on both `quality' (accuracy of contribution) and `quantity' (degree of participation) of their contributions. Specifically, QnQ exploits a rating feedback mechanism for evaluating an event-specific expected truthfulness, which is then transformed into a robust quality of information (QoI) metric to weaken various effects of selfish and malicious user behaviors. Eventually, the QoIs of various events in which a user has participated are aggregated to compute his reputation score, which in turn is used to judiciously disburse user incentives with a goal to reduce the incentive losses of the CS application provider. Subsequently, inspired by cumulative prospect theory (CPT), we propose a risk tolerance and reputation aware trustworthy decision making scheme to determine whether an event should be published or not, thus improving the operational reliability of the application. To evaluate QnQ experimentally, we consider a vehicular crowdsensing application as a proof-of-concept. We compare QoI performance achieved by our model with Jøsang's belief model, reputation scoring with Dempster-Shafer based reputation model, and operational (decision) accuracy with expected utility theory. Experimental results demonstrate that QnQ is able to better capture subtle differences in user behaviors based on both quality and quantity, reduces incentive losses, and significantly improves operational accuracy in presence of rogue contributions.

Publish or Drop Traffic Event Alerts? Quality-aware Decision Making in Participatory Sensing-based Vehicular CPS

Vehicular cyber-physical systems (VCPS), among several other applications, may help address an ever-increasing challenge of traffic congestion in large cities. Nevertheless, VCPS can be hindered by information falsification problem, resulting due to the wrong perception of a traffic event or deliberate faking by the participating vehicles. Such information fabrication causes the re-routing of vehicles and artificial congestion, leading to economic, safety, environmental, and health hazards. Thus, it is imperative to infer truthful traffic information in real-time to restore the operational reliability of the VCPS. In this work, we propose a novel reputation scoring and decision support framework, called Spoofed and False Report Eradicator (SAFE) , which offers a cost-effective and efficient solution to handle information falsification problem in the VCPS domain. The framework includes humans in the sensing loop by exploiting the paradigm of participatory sensing , a concept of a mobile security agent (MSA) to nullify the effects of deliberate false contribution, and a variant of the distance bounding mechanism to thwart location-spoofing attacks. A regression-based model integrates these effects to generate the expected truthfulness of a participant’s contribution. To determine if any contribution is true or false, a generalized linear model is used to transform the expected truthfulness into a Quality of Contribution (QoC) score. The QoC of different reports is aggregated to compute user reputation. Such reputation enables classification of different participation behaviors. Finally, an Expected Utility Theory (EUT) -based decision model is proposed that utilizes the reputation score to determine if event-specific information should be published or dropped. To evaluate the SAFE framework through experimental study, we used both simulated and real data to compare its reputation-based user segregation performance with state-of-the-art frameworks. Experimental results exhibit that SAFE captures the fine differences in participants’ behavior through the quality and quantity of participation, and the accuracy of their informed location. It also significantly improves operational reliability through publishing the information of only legitimate events.

A unified knowledge compiler to provide support the scientific community

The scientific community represents an insatiable network with important needs related to the searching of information. The ever-broadening amount of domain-scientific on-line information that can be found requires increasingly sophisticated frameworks to manage it. Nevertheless, these frameworks are usually focused on specific useful functionalities and work more like expert systems than general purpose approaches. In order to ease the research process to the scientific community, the Unified Knowledge Compiler (UNIKO) framework is presented. This framework includes, among other functionalities, the recommendation of articles and authors related to a specific field of application, the evaluation of reputation scores of articles and authors, and the sentiment analysis of the texts of the articles. UNIKO is built as a hybrid framework based on Knowledge-Based Systems and Content-Based Recommendation Systems. In order to evaluate the performance of the system, several experiments have been done. The first experiment is developed to illustrate the reputation scoring task. The second one addresses the sentiment scores calculation based on a lexicon which is supported by a Convolutional Neural Network. The last experiment shows the recommendation tasks based on specific similarity measures and unsupervised learning.

Reputation Scoring Fake News Using Text Mining

The classification of hoax news or news with incorrect information is one of the text categorization applications.Like text-based categorization of machine applications in general, this system consists of pre-processing andexecution of classification models. In this study, experiments were conducted to select the best technique in each sub-process by using 1200 articles hoax and 600 articles no hoax collected manually. This research Triedexperimenting to determine the best preprocessing stages between stop removals and stemming and showing the results of the deception Tree algorithm achieving an accuracy of 100% concluded above naive byes more stable level of accuracy in the number of datasets used in all candidates. Information gain, TFIDF and GGA based on using Naive Byes algorithm, supporting Vector Machine and Decision Tree no significant percentage change occurred on all candidates. But after using GGA (Optimize Generation) feature selection there is an increase of accuracy level The results of a comparison of classification algorithms between Naive Byes, decision trees and Support Vector machines combined with the GGA feature selection method for classifying the best result is generated by the selection of GGA + Decision Tree feature on candidate 2 (Paslon2) 100% and in the selection of the Information Gain + Decision Tree Feature selection with the lowest accuracy Candidate 3 at 36.67%, but overall improvement of accuracy Occurred on all algorithm after using feature selection and Naive byes more stable level of accuracy in the number of datasets used in all candidates.

Remote Medical Monitoring System in Wireless Sensor Networks

In order to solve the problem of monitoring remote medical care to identify good medical services, a medical service recommendation system based on privacy-preserved reputation scoring is designed. This system enables medical service users to get a reputation score of a Medical Service Provider before engaging in any services. The experiment results show that the proposed scheme is significantly more efficient in batch verification of digital signatures, making it a suitable practical solution to Mobile Health Care applications, while the third solution shows that the system is able to provide reputation scores as well as recommendation while preserving privacy as desired.

Online Reputation Scoring from Historical Feedbacks Based on Partially-Ordered Similarity

Online Reputation Scoring from Historical Feedbacks Based on Partially-Ordered Similarity

Sellers in Online Auction Markets: Introducing a Feedback-Based Classification

Increasing significance of the online consumer-to-consumer (C-2-C) auction market has amplified the need for buyers and sellers to engage in transactions with anonymous counterparts. The sequence of paying first and then taking delivery, introduces a great amount of risk for potential buyers. In order to mitigate this risk, online auction markets (OAMs) are employing an assortment of governance mechanisms, of which reputation scoring and reporting systems are the most popular. Researchers have found substantial evidence from theoretical models as well as empirical studies that higher the reputation rating of a seller, higher the bid prices he/she receives. However, a review of the current literature suggests a conspicuous absence of any standard classification of sellers in OAMs. Lack of such a classification hinders systematic research and theory development. Therefore, a comprehensive classification of sellers, based on feedbacks, is proposed to advance our understanding of online C-2-C auction market and to provide a basis for further research. In addition, the proposed classification is hierarchical rather than monothetic in nature and hence, gives greater systemic power to the classification. Toward demonstrating the classification’s systemic power, we present a propositional inventory developed from the classification. We also discuss how the proposed classification accommodates current research and furthers theory building in this research area.