User Misbehavior Research Articles

A research on the influence mechanism of humanization degree of service robots on user misbehavior

PurposeExplore how the degree of humanization affects user misconduct, and provide effective misconduct prevention measures for the wide application of artificial intelligence in the future.Design/methodology/approachBased on the “Uncanny Valley theory”, three experiments were conducted to explore the relationship between the degree of humanization of service machines and user misbehavior, and to analyze the mediating role of cognitive resistance and the moderating role of social class.FindingsThere is a U-shaped relationship between the degree of humanization of service machines and user misbehavior; Social class not only regulates the main effect of anthropomorphism on misbehavior, but also regulates the intermediary effect of anthropomorphism on cognitive resistance, thus affecting misbehavior.Research limitations/implicationsThe design of the service robot can be from the user’s point of view, combined with the user’s social class, match different user types, and provide the same preferences as the user’s humanoid service robot.Practical implicationsThis study is an important reference value for enterprises and governments to provide intelligent services in public places. It can prevent the robot from being vandalized and also provide users with a comfortable human-computer interaction experience, expanding the positive effects of providing smart services by government and enterprises.Social implicationsThis study avoids and reduces users' misbehavior towards intelligent service robots, improves users' satisfaction in using service robots, and avoids service robots being damaged, resulting in waste of government, enterprise and social resources.Originality/valueFrom the perspective of product factors to identify the inducing factors of improper behavior, from the perspective of social class of users to analyze the moderating effect of humanization degree and user improper behavior.

Measuring Cyber Security Awareness: A Comparison between Computer Science and Media Science Students

Internet users who behave carelessly or badly increase the possibility of a security attack on the information systems they use. Although the technical security of devices is constantly improving, the human factor still represents a significant vulnerability in the system. Awareness of user misbehavior in cybersecurity allows sufficient security countermeasures to be taken. This study examines level of attitudes and awareness of university students towards cyber security. Based on quantitative research, the survey contrasts the attitudes of students in two disciplines - Computer Science and Media Studies - on a sample of 570 students. The results collected from the statistical analysis show similarities and differences in the responses of students in the two disciplines.

Dynamic-IoTrust: A Dynamic Access Control for IoT Based on Smart Contracts

The Internet of things (IoT) is an active, real-world area in need of more investigation. One of the top weaknesses in security challenges that IoTs face, the centralized access control server, which can be a single point of failure. In this paper, Dynamic-IoTrust, a decentralized access control smart contract based aims to overcome distrusted, dynamic, trust and authentication issues for access control in IoT. It also integrates dynamic trust value to evaluate users based on behavior. In particular, the Dynamic-IoTrust contains multiple Main Smart Contract, one Register Contract, and one Judging Contract to achieve efficient distributed access control management. Dynamic-IoTrust provides both static access rights by allowing predefined access control policies and also provides dynamic access rights by checking the trust value and the behavior of the user. The system also provides to detected user misbehavior and make a decision for user trust value and penalty. There are several levels of trusted users to access the IoTs device. Finally, the case study demonstrates the feasibility of the Dynamic-IoTrust model to offer a dynamic decentralized access control system with trust value attribute to evaluate the internal user used IoTs devices.

A Signature Scheme with Unlinkable-yet-Accountable Pseudonymity for Privacy-Preserving Crowdsensing

Crowdsensing requires scalable privacy-preserving authentication that allows users to send anonymously sensing reports, while enabling eventual anonymity revocation in case of user misbehavior. Previous research efforts already provide efficient mechanisms that enable conditional privacy through pseudonym systems, either based on Public Key Infrastructure (PKI) or Group Signature (GS) schemes. However, previous schemes do not enable users to self-generate an unlimited number of pseudonyms per user to enable users to participate in diverse sensing tasks simultaneously, while preventing the users from participating in the same task under different pseudonyms, which is referred to as sybil attack. This paper addresses this issue by providing a scalable privacy-preserving authentication solution for crowdsensing, based on a novel pseudonym-based signature scheme that enables unlinkable-yet-accountable pseudonymity. The paper provides a detailed description of the proposed scheme, the security analysis, the performance evaluation, and details of how it is implemented and integrated into a real crowdsensing platform.

Secure Degrees of Freedom in Networks with User Misbehavior

We investigate the secure degrees of freedom (s.d.o.f.) of three new channel models: broadcast channel with combating helpers, interference channel with selfish users, and multiple access wiretap channel with deviating users. The goal of introducing these channel models is to investigate various malicious interactions that arise in networks, including active adversaries. That is in contrast with the common assumption in the literature that the users follow a certain protocol altruistically and transmit both message-carrying and cooperative jamming signals in an optimum manner. In the first model, over a classical broadcast channel with confidential messages (BCCM), there are two helpers, each associated with one of the receivers. In the second model, over a classical interference channel with confidential messages (ICCM), there is a helper and users are selfish. By casting each problem as an extensive-form game and applying recursive real interference alignment, we show that, for the first model, the combating intentions of the helpers are neutralized and the full s.d.o.f. is retained; for the second model, selfishness precludes secure communication and no s.d.o.f. is achieved. In the third model, we consider the multiple access wiretap channel (MAC-WTC), where multiple legitimate users wish to have secure communication with a legitimate receiver in the presence of an eavesdropper. We consider the case when a subset of users deviate from the optimum protocol that attains the exact s.d.o.f. of this channel. We consider two kinds of deviation: when some of the users stop transmitting cooperative jamming signals, and when a user starts sending intentional jamming signals. For the first scenario, we investigate possible responses of the remaining users to counteract such deviation. For the second scenario, we use an extensive-form game formulation for the interactions of the deviating and well-behaving users. We prove that a deviating user can drive the s.d.o.f. to zero; however, the remaining users can exploit its intentional jamming signals as cooperative jamming signals against the eavesdropper and achieve an optimum s.d.o.f.

COPS: Cooperative Statistical Misbehavior Mitigation in Network-Coding-Aided Wireless Networks

The altruistic user behavior and the isolation of malicious users are fundamental requirements for the proper operation of any cooperative network. However, the widespread use of new communication techniques that improve the cooperative performance (e.g., network coding) hinders the application of traditional schemes on malicious users detection, which are mainly based on packet overhearing. In this paper, we introduce a cooperative nonparametric statistical framework, namely COPS, for the mitigation of user misbehavior in network coding scenarios. Given that the behavior of adversaries cannot be characterized by certain probability distributions, the proposed scheme exploits two well-known nonparametric statistical methods, i.e., Kruskal–Wallis analysis and Conover–Iman multiple comparisons, for the detection and identification, respectively, of malicious users in the network. It is worth noting that the COPS framework does not require monitoring of the wireless channel and additional overhead, as its operation is based on the processing of the existing control packets. We assess the performance of the proposed scheme in various scenarios, showing that COPS is able to effectively handle attacks in the network, even when malicious users adopt a smart probabilistic misbehavior.

Prediction of road track paths by a fuzzy model

ABSTRACTThis research proposes a fuzzy model built to predict the track paths of a motor vehicle on a two-lane road and two-way traffic. The purpose of this study stems from the awareness that the position of the vehicle within the cross-section hardly coincides with the axis of the lane but deviates from this one because of some conditions generally not provided in the road standards. To solve this problem, the authors prepared an instrumented vehicle, measuring many variables during some trials in a rural road located in Sicily, Italy. The resulting database was treated at first with subtractive clustering techniques to correctly size the membership functions and rules without the subjective help of an analyst. Therefore, the fuzzy model was built only on the basis of the measured data, and the result was the position of the vehicle within the cross-section of the examined road. This research can help the analyst to look for the real cause of user's misbehavior even when the variables involved are fully respectful with the road standard to modify or eliminate disturbing elements in the understanding of the road by the users.

Malicious-Proof and Fair Credit-Based Resource Allocation Techniques for DSA Systems

We propose a credit-based resource allocation technique for dynamic spectrum access that is robust against malicious and selfish behaviors and ensures good overall system fairness performance while also allowing spectrum users to achieve high amounts of service. We also propose a new objective function that, when combined with the proposed credit-based technique, leads to further improvements of the system fairness performance. Our proposed techniques overcome user misbehavior by masking the impact of the users' pursued private objectives on the overall system performance. They also improve fairness among users by allocating service to users adaptively by accounting for how much service each user has received in the past. Our simulation results show that our proposed techniques maintain high system performance by allowing users to achieve high amounts of service and by ensuring fair allocation of spectrum resources among users even in the presence of misbehaved users. Using simulations, we also show that these high performances are also achievable under various different network scenarios.

Universal Non-Linear Cheat-Proof Pricing Framework for Wireless Multiple Access Channels

The success of future wireless networks depends on the correct and robust operation with selfish or even malicious nodes. Game theory provides methods to design such wireless systems. In this paper, we study a general multiple access system (with linear and nonlinear receiver) with three types of agents: the regulator, the system optimizer and the mobile users. The users formulate the signal to interference-plus-noise ratio (SINR) based quality-of-service (QoS) requirements and pay a corresponding virtual fee to the regulator depending on their transmit power. The regulator ensures the QoS requirements of all users by clever non-linear pricing and prevents cheating. The simple system optimizer solves the system utility maximization problem to allocate the power. The feasible utility region, power allocation, weights, the universal pricing, which is linear in the pricing parameters and logarithmic in power, and the resulting cost terms are derived in closed form. The user misbehavior is analyzed. Finally a repeated game is formulated with the worst case strategy for all the honest users and trigger strategy for the cheater. Analysis and simulation results show that the proposed framework is strategy-proof.

PEREA

Several anonymous authentication schemes allow servers to revoke a misbehaving user's ability to make future accesses. Traditionally, these schemes have relied on powerful Trusted Third Parties (TTPs) capable of deanonymizing (or linking) users' connections. Such TTPs are undesirable because users' anonymity is not guaranteed, and users must trust them to judge misbehaviors fairly. Recent schemes such as Blacklistable Anonymous Credentials (BLAC) and Enhanced Privacy ID (EPID) support “privacy-enhanced revocation”— servers can revoke misbehaving users without a TTP's involvement, and without learning the revoked users' identities. In BLAC and EPID, however, the computation required for authentication at the server is linear in the size (L) of the revocation list , which is impractical as the size approaches thousands of entries. We propose PEREA, a new anonymous authentication scheme for which this bottleneck computation is independent of the size of the revocation list . Instead, the time complexity of authentication is linear in the size of a revocation window K ≪ L , the number of subsequent authentications before which a user's misbehavior must be recognized if the user is to be revoked. We extend PEREA to support more complex revocation policies that take the severity of misbehaviors into account. Users can authenticate anonymously if their naughtiness , i.e., the sum of the severities of their blacklisted misbehaviors, is below a certain naughtiness threshold. We call our extension PEREA-Naughtiness. We prove the security of our constructions, and validate their efficiency as compared to BLAC analytically and quantitatively.

Improved Double Auction Protocol based on a Hybrid Trust Model

Recently, Wang et al. proposed a set of double auction protocols with full privacy protection based on distributed ElGamal encryption. Unfortunately, their protocols are expensive in computation, and are not robust in dealing with system malfunction or user misbehavior. In this paper, we propose a secure and practical double auction protocols based on a hybrid trust model, where computation load is distributed to buyers and sellers while a semi-trusted manager handles the registration phase. A prominent feature of the proposed protocol is its high robustness, achieved by using a publicly verifiable secret sharing scheme with threshold access structure.