Trusted Computing Research Articles

Detection of Malicious Social Bots Using Learning Automata With URL Features in Twitter Network

Malicious social bots generate fake tweets and automate their social relationships either by pretending like a follower or by creating multiple fake accounts with malicious activities. Moreover, malicious social bots post shortened malicious URLs in the tweet in order to redirect the requests of online social networking participants to some malicious servers. Hence, distinguishing malicious social bots from legitimate users is one of the most important tasks in the Twitter network. To detect malicious social bots, extracting URL-based features (such as URL redirection, frequency of shared URLs, and spam content in URL) consumes less amount of time in comparison with social graph-based features (which rely on the social interactions of users). Furthermore, malicious social bots cannot easily manipulate URL redirection chains. In this article, a learning automata-based malicious social bot detection (LA-MSBD) algorithm is proposed by integrating a trust computation model with URL-based features for identifying trustworthy participants (users) in the Twitter network. The proposed trust computation model contains two parameters, namely, direct trust and indirect trust. Moreover, the direct trust is derived from Bayes' theorem, and the indirect trust is derived from the Dempster-Shafer theory (DST) to determine the trustworthiness of each participant accurately. Experimentation has been performed on two Twitter data sets, and the results illustrate that the proposed algorithm achieves improvement in precision, recall, F-measure, and accuracy compared with existing approaches for MSBD.

The Sound of Trust: Towards Modelling Computational Trust using Voice-only Cues at Zero-Acquaintance

The Sound of Trust: Towards Modelling Computational Trust using Voice-only Cues at Zero-Acquaintance

Social recommendation algorithms with user feedback information

SummarySocial media information can effectively improve the performance of personalized recommendation model. However, the feedback information in the social media which can accurately reflect users' implicit preferences is often ignored by most existing methods. To improve the users' experience and reduce the push of unwelcome information, in this article, we propose a new social recommendation algorithm with user feedback information. Different from the existing recommendation methods based on probability matrix decomposition, we incorporate the user implicit feedback information into the user rating prediction function. To reduce the data sparsity of implicit feedback information, we also adopt social network trust calculation in our algorithm. As a result, we can not only optimize the recommendation list but also filter out most of disgusting content. Compared with PMF, UserCF, CUNE, and TrustSVD, but slightly lower than RSTE, the experimental results of our model on real‐world datasets demonstrate the effectiveness of our proposed method, and further verify that the user experience is significantly improved without obviously reducing the accuracy of the recommendation.

Trusted Location Selection in Vehicular ad-hoc Network

VANET gives the advantage to enhance road safety and assure less or no traffic accidents. However, false messages can result in serious conditions like collision. This paper proposed a new, trust based technique for location selection in VANET. In the paper presents a trust based location selection scheme has two phases computation of direct and indirect trust. However, evaluation of direct trust system using infrastructure (RSU) and trusted authority (TA) and evaluation of indirect trust uses watchdog approach. Results show that the proposed scheme suitable for the actual situation of VANET.

Cloud Security Data Critical Review

Cloud computing is a model for providing convenient network access on demand to some common fund of configurable computing resources. These calculations are flexible, scalable, and inexpensive, but have large-scale sharing of services among multiple users. The broad coverage of the idea of cloud computing has led to significant changes, both in public access systems and in mobile communications, which prompted advanced researchers to provide suitable system protocols and network architecture. In cloud computing, there are two main problems: access control and security. Therefore, the security of both services and users is a significant problem that stands in the way of using and trust in cloud computing.

Reliable Trust Computation model in Vehicular ad-hoc Network

This paper concerns about the various implemented work has been studied and analyzed to form a new survey on trust model to VANET. In this paper it is observed that there are lot of new techniques are possible to form a new trust model in VANET to provide better security with trust concern over the entire environment of trust management in VANET. This work concerns of entire trust calculation work which has been done yet over it. Here summarizing the various trust models, various security requirements, issues over it.

Industrial Internet of Things for smart manufacturing applications using hierarchical trustful resource assignment

The manufacturing industry undergoes a new age with major changes taking place on several fronts. Companies devoted to digital transformation take their future plants inspired by the Internet of Things (IoT). The IoT is a worldwide network of interrelated physical devices,which is an essential component of the internet, including sensors, actuators, smart apps, computers, mechanical machines, and people. The effective allocation of the computing resources and the carrier is critical in the industrial internet of Things (IIoT) for smart production systems. Indeed, the existing assignment method in the smart production system cannot guarantee that resources meet the inherently complex and volatile requirements of the user are timely. Many research results on resource allocations in auction formats which have been implemented to consider the demand and real-time supply for smart development resources, but safety privacy and trust estimation issues related to these outcomes are not actively discussed. The paper proposes a Hierarchical Trustful Resource Assignment (HTRA) and Trust Computing algorithm (TCA) based on Vickrey–Clarke–Groves (VGCs) in the computer carriers necessary resources to communicate wirelessly among IIoT devices and gateways, and the allocation of CPU resources for processing information at the CPC. Finally, experimental findings demonstrate that when the IIoT equipment and gateways are effective, the utilities of each participant are improved.

RETRACTED ARTICLE: Secure routing scheme with multi-dimensional trust evaluation for wireless sensor network

Security has been always a major concern in wireless network irrespective of evolving safety protocols, which motivates various researchers to explore an effective security solution. Existing encryption-based solution offers security but at the cost of resources which are sometime highly limited in communication devices. Therefore, this paper discusses about trust evaluation that is one of the critical operations for ascertaining the degree of security strength offered by the sensor nodes in Wireless Sensor Network (WSN). Existing research work has been carried out to find a strong connection between the resource efficiency and the critical security approaches; however, these approaches are not proven for lightweight energy efficient security scheme. Therefore, the proposed system developed using analytical approach offers a secured communication scheme that evaluates the comprehensive trust value for the target relay node followed by reputation value from the neighboring node in order to perform an effective selection of legitimate relay node. The authentication process has been carried out using progressive key-generation process while the resource efficiency is maintained by developing multi-dimensional trust computation. The essential contribution of the proposed model is to offer an effective balance between resource efficiency and trust-reputation based security strengthening features. The outcome shows that proposed model offers 75% of retention of energy-efficient nodes with more predictable energy dissipation trend. It also offers 82% of reduction of energy fluctuation during the entire operation which is again found to be 35% faster than existing approaches. The simulated outcome of study exhibits that proposed system offers better data transmission performance with significant energy efficiency in comparison to existing scheme.

Discrimination-aware trust management for social internet of things

Internet of Things combined with social networks, has emerged as an interesting research area which is referred to as Social Internet of Things (SIoT). Objects in SIoT interact with each other based on their social behavior. In this network, any object can be a service provider or a service consumer. Social objects are expected to be able to easily discover their desired services in a trusted way. Those that belong to humans are usually selfish. Discriminative objects are the ones that do not contribute to providing satisfactory services in some circumstances. By considering discriminative behavior as rational behavior, we introduce a discriminative-aware trust management (DATM) system for service provisioning in SIoT. DATM employs the ratings of objects and is based on a data mining model that compares the context of service query with the contexts of other raters’ previous queries. It takes metrics such as social similarity, importance of the service, and provider's remaining energy into account and takes the problem to a three-dimensional space where weighted-kNN is used to weigh the contribution of each of the k previous experiences in the estimation of trust value. Simulations, in the presence of discriminative and malicious objects, demonstrate that DATM can well detect objects’ selfish behaviors compared to other approaches which ignore social relationships or discrimination in the calculation of trust. Moreover, our scheme resists trust related attacks and does not allow malicious objects to damage the system.

Fog Level Trust for Internet of Things Devices Using Node Feedback Aggregation

Internet of things is a current exploding area of research now in which the number of devices is expected to reach 20 billion in 2050. Devices are becoming intelligent and are being used for many applications which support day to day living of humans. Since the information gathered are being used by human users in most of the cases, trust of the information provider is to be questioned. Lack of trust obstructs the acceptance of these devices and technologies to be implemented. As the technology advances, the IoT network architecture must establish trust between the devices to increase its adaptability among the potential users. In this paper, we propose a Fog level trust establishment technique has been proposed for IoT devices using Node feedback mechanism. Since the trust factors are decided in the fog level, the nodes will know the trust of the other node which they need to communicate only and thereby avoiding bad-mouthing attacks. Considering the node capability in IoT environment, we adopt a mechanism for the calculating the trust of the node which keeps the overhead of the trust computing device to be low. This makes our implementation capable of expanding to handle large scale IoT networks. Information Entropy theory has been adopted to gather the feedback about a node in the fog level which breaks the barriers of subjective trust calculation in the traditional trust management schemes. Results shows an improvement in the efficiency compared to the existing methods.

Public Increasing Trust of Biometric Technology

Publically agree to technology of biometric is compound based at extreme believe. The bounded function at believe to biometrics usually record in various types of biometrics with correlative samples associated with familiarity and confidential. To develop a complete understand trust of people recognition about biometrics, we employed current theory to develop a regular calculation of trust in biometrics from user’s theory. We increased confidence of first concern in terms of mutual interaction, technology, information systems and adoption of automated technology. Identified common belief components in these circumstances. Modified objects with reference to biometric technology. Managed an Examine study to determine the reliability of this new scale for sub-factors and trust. Our statistics give rise to seventh recently elements companion with user believe to technology of biometric. We will know work process with recommend conclusion also.

Health Information Systems in the Digital Health Ecosystem-Problems and Solutions for Ethics, Trust and Privacy.

Digital health information systems (DHIS) are increasingly members of ecosystems, collecting, using and sharing a huge amount of personal health information (PHI), frequently without control and authorization through the data subject. From the data subject’s perspective, there is frequently no guarantee and therefore no trust that PHI is processed ethically in Digital Health Ecosystems. This results in new ethical, privacy and trust challenges to be solved. The authors’ objective is to find a combination of ethical principles, privacy and trust models, together enabling design, implementation of DHIS acting ethically, being trustworthy, and supporting the user’s privacy needs. Research published in journals, conference proceedings, and standards documents is analyzed from the viewpoint of ethics, privacy and trust. In that context, systems theory and systems engineering approaches together with heuristic analysis are deployed. The ethical model proposed is a combination of consequentialism, professional medical ethics and utilitarianism. Privacy enforcement can be facilitated by defining it as health information specific contextual intellectual property right, where a service user can express their own privacy needs using computer-understandable policies. Thereby, privacy as a dynamic, indeterminate concept, and computational trust, deploys linguistic values and fuzzy mathematics. The proposed solution, combining ethical principles, privacy as intellectual property and computational trust models, shows a new way to achieve ethically acceptable, trustworthy and privacy-enabling DHIS and Digital Health Ecosystems.

Machine learning based trust management framework for vehicular networks

Establishing security metrics in vehicular networking is still being debated. The dynamic characteristics of vehicular networks, imposes challenges to realize an appropriate solution to organize and ensure reliable data transfer between the vehicular nodes. In order to ensure road safety, avoid/reduce traffic congestion, and to identify malicious vehicles, an efficient Trust Management System has to be implemented in real time scenarios. All existing applications in this area have focused on reliable data exchange and authentication process of vehicular nodes to forward messages. This study proposes a new entity centric trust framework using decision tree classification and artificial neural networks. Decision tree classification model is used to derive rules for trust calculation and artificial neural networks are used to self-train the vehicular nodes, when expected trust value is not met. This model uses multifaceted role and distance based metrics like Euclidean distance to estimate the trust. The proposed entity centric trust model, uses a versatile new direct and recommended trust evaluation strategy to compute trust values. The suggested model is simple, reliable and efficient in comparison to the other popular entity centric trust models. Results and comparative analyses are carried out to prove the better performance of the proposed model over other related approaches.

ELPC-Trust Framework for Wireless Sensor Networks

Wireless Sensor Network (WSN) and its security concern play a vital part in its effecting functioning. WSN routing layer attacks pose a great threat to its functionality, whereas if a situation arises in which multiple attacks co-exist, then the scenario will become worse. In such cases the reliability of the network deployed is greatly questionable. Thus an Intrusion detection cum Trust based framework is mandatory to detect such co existing malicious activities in order to maintain the network reliability. Energy packed Cluster heads and sink nodes are utilized for this kind of trust computation process. To achieve this, an energy based abnormality detection of malicious nodes is proposed, upon which based on the packet rate and the lifetime of sensor nodes a trust value is computed. The trust framework involves two phases namely Threat Detection phase which performs attack categorization based on Energy, Packet Count and Z scores followed by the second phase which computes the trust values for each node. Trust scores are computed based on the intensity of the attack in terms of their network performance degradation. Attacks such as Gray hole, Black hole, Wormhole, Flooding and Data Modification attacks are addressed. Data Modification attacks are detected uniquely based on the Z scores values of the sensed data set and upon the location of the nodes. If any node is found to consume abnormal energy either below or above certain threshold, identified nodes are categorized into two groups based on their threshold. Hierarchical decision making is introduced, which implies only the abnormal energy consuming nodes (among the total sensor nodes deployed in the network) are considered for further packet count based attack categorization. This way we can reduce the computational overhead of traditional trust models in which each node is checked for its reliability against multiple trust factors. Upon detecting the attacks and computing the trust values of nodes, only the nodes which have a nominal trust value are included in the routing process. Simulations were carried out in NS2 Mannasim Framework with static and mobile sensor nodes. Proposed Energy-Lifetime-Packet Count (ELPC)-Trust framework was tested under five different types of attack scenarios in a co-existing manner. Performance of the network by including the proposed ELPC trust and by ignoring the detected malicious nodes increases the throughput by 500 and PDR by 40% when compared with the attack scenario. But on the other hand introduces a delay of 0.5 seconds. Network Performance is improved in case of static networks. Under mobile scenarios proposed ELPC trust incurs high amount of delay.

IC-MADS: IoT Enabled Cross Layer Man-in-Middle Attack Detection System for Smart Healthcare Application

Smart healthcare is one of the core applications of the Internet of Things (IoT) technology in which the body sensors capture the patient's periodic data and transmit seamlessly over the wireless communications to remote monitoring stations. Due to the open nature of wireless communications, privacy-preserving and security are the biggest concern for IoT enabled healthcare systems. Several trust-based and cryptography-based solutions proposed to achieve the privacy-preserving and data security, however, such layered solutions failed to address the cross-layer attacks such as Man-in-Middle Attack (MIMA). Thus we believe that cross-layer attack detection methods achieve effective protection against such attacks. This paper proposed the novel lightweight cross-layer trust computation algorithm for MIMA attacker detection called IC-MADS. The IC-MADS consists of two main contributions such as energy-efficient clustering and cross-layer attack detection. The clustering algorithm proposed for optimal Cluster Head (CH) selection using the probability computation and evaluation approach. Each sensor node probability is computed using the parameters like residual energy, distance to Base Station, and node degree parameters. The node with a higher probability value becomes the CH. This solves the energy imbalance and load balancing problems. The cross-layer trust evaluation approach introduced node evaluation to detect the MIMA attackers in the network. The trust computation of each node is performed by using the sensor nodes parameters across the different layers such as network, physical, and MAC layers. The aggregate trust value of each sensor node is then compared with the threshold value to check whether the node is an attacker. The simulation results prove that IC-MADS achieves better protection against MIMA attacks with minimum overhead and energy consumption.

Abstract B92: Landscape of B-cell immunity and related immune evasion in human cancers

Abstract Tumor-infiltrating B cell is an important component in the microenvironment with unclear antitumor impacts. We enhanced our previous computational algorithm TRUST to extract the B cell immunoglobulin (Ig) hypervariable regions from bulk tumor RNA-seq data. TRUST assembled over 30 million complementarity-determining region 3 (CDR3s) of the B-cell heavy chain (IgH) from The Cancer Genome Atlas (TCGA). Widespread B-cell clonal expansions and Ig subclass switch events were observed in diverse human cancers. Prevalent somatic copy number alterations (SCNA) in MICA/B genes related to antibody-dependent cell mediated cytotoxicity (ADCC) were identified in tumors with elevated B-cell activity. IgG3-1 subclass switch interacts with the B cell receptor affinity maturation and defects in the ADCC pathway. The comprehensive pan-cancer analyses of tumor-infiltrating B-cell receptor repertoires revealed novel tumor immune evasion mechanism through genetic alterations. The IgH sequences identified in this work are potentially useful resources for future development of immunotherapies. Citation Format: Xihao Sherlock Hu, Jian Zhang, Jun Liu, Bo Li, Xiaole Shirley Liu. Landscape of B-cell immunity and related immune evasion in human cancers [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2018 Nov 27-30; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(4 Suppl):Abstract nr B92.

A Multi-Attribute Trust-based Authentication Model for Internet of Things based Military Environment

The Internet of Things (IoT) is an emerging field where physical objects are connected over the network, by the way, to make human life easy and more comfortable. The IoT environment is involved with various devices and those are working together to attain a common goal. The enhanced technology of IoT enables the military environment to work on it. Typically, most of the IoT devices are restricted in terms of their storage, process, compute and network capability. Hence, those devices are easy to attack and compromise. Compromised devices become behave as blackhole attacks. To assure the proper network function, we are in the situation to overcome those types of attacks. As trust plays a vital role in decision making, in this paper we proposed a Multi-attribute Trust-based Authentication mechanism (MTA). The ultimate aim of this model is to ensure authentication among the participating devices by identifying black hole nodes in the network. This multi-attribute trust calculation approach provides a maximum effort to evaluating the trustworthiness of devices. The simulation results show the applicability of the proposed model in terms of various performance metrics.

A Reliable Trust Computing Mechanism Based on Multisource Feedback and Fog Computing in Social Sensor Cloud

Social sensor cloud (SSC) is combined with social network, wireless sensor network, cloud computing, and fog computing, which is currently a new type of Internet of Things (IoT). In order to provide a convenient, open, and highly reliable SSC services, the devices of fog computing are distributed at the edge of cloud computing. The devices of fog computing can independently process and store data, and feedback more quickly in SSC. The sensing layer of SSC faces different types of physical attacks and communication attacks, such as message forgery, message tampering, reply attacks, hidden data attacks, etc., lead to the lack of trust between social sensors and cloud data centers in SSC. Therefore, the trust evaluation between the sensing layer and the network layer is necessary. However, computing the reliability of the social sensor data in cloud data centers will generate a large amount of trust computing overhead, communication overhead, and communication delay, which hinder the widespread application of SSC services. To combat this issue, a reliable trust computing mechanism (RTCM) based on multisource feedback and fog computing fusion is proposed. First, a new metric is designed for the trust of social sensor nodes, and multisource feedback trust value collection is performed at the sensing layer to improve the detection of malicious feedback nodes. Second, the trust feedback information of the sensing layer is collected by the devices of fog computing, and the recommendation trust calculation is performed, which reduces the communication delay and computing overhead. Third, a fusion algorithm is designed to aggregate different types of feedback trust values, which overcomes the limitation of trust weights in artificial weighting and subjective weighting in traditional trust mechanisms. Theoretical analyses and simulation results show that the proposed trust computing mechanism has better computational efficiency and higher reliability compared with existing methods.

IMPlementation of An online Relatives\u2019 Toolkit for psychosis or bipolar (IMPART study): iterative multiple case study to identify key factors impacting on staff uptake and use

BackgroundDespite the potential of digital health interventions to improve the delivery of psychoeducation to people with mental health problems and their relatives, and substantial investment in their development, there is little evidence of successful implementation into clinical practice. We report the first implementation study of a digital health intervention: Relatives Education And Coping Toolkit (REACT), into routine mental healthcare. Our main aim was to identify critical factors affecting staff uptake and use of this online self-management tool for relatives of people with psychosis or bipolar.MethodsA mixed-methods, theory-driven (Normalisation Process Theory), iterative multiple case study approach using qualitative analysis of interviews with staff and quantitative reporting of uptake. Carer researchers were part of the research team.ResultsIn all, 281 staff and 159 relatives from Early Intervention teams across six catchment areas (cases) in England registered on REACT; 129 staff took part in qualitative interviews. Staff were positive about REACT helping services improve support and meet clinical targets. Implementation was hindered by: high staff caseloads and difficulties prioritising carers; perception of REACT implementation as research; technical difficulties using REACT; poor interoperability with trust computer systems and care pathways; lack of access to mobile technology and training; restricted forum populations; staff fears of risk, online trolling, and replacement by technology; and uncertainty around REACT’s long-term availability.ConclusionsDigital health interventions, such as REACT, should be iteratively developed, evaluated, adapted and implemented, in partnership with the services they aim to support, and as part of a long term national strategy to co-develop integrated technology-enabled mental healthcare. Implementation strategies must instil a sense of ownership for staff and ensure they have adequate IT training, appropriate governance protocols for online working, and adequate mobile technologies. Wider contextual factors including adequate funding for mental health services and prioritisation of carer support, also need to be addressed for successful implementation of carer focussed digital interventions.Trial registrationStudy registration: ISCTRN 16267685.

Fast, Resource-Saving, and Anti-Collaborative Attack Trust Computing Scheme Based on Cross-Validation for Clustered Wireless Sensor Networks.

The trust computing mechanism has an increasing role in the cooperative work of wireless sensor networks. However, the computing speed, resource overhead, and anti-collaborative attack ability of a trust mechanism itself are three key challenging issues for any open and resource-constrained wireless sensor networks. In this study, we propose a fast, resource-saving, and anti-collaborative attack trust computing scheme (FRAT) based on across-validation mechanism for clustered wireless sensor networks. First, according to the inherent relationship among three network entities (which are made up of three types of network nodes, namely base stations, cluster heads, and cluster members), we propose the cross-validation mechanism, which is effective and reliable against collaborative attacks caused by malicious nodes. Then, we adopt a fast and resource-saving trust computing scheme for cooperation between between cluster heads or cluster members. This scheme is suitable for wireless sensor networks because it facilitates resource-saving. Through theoretical analysis and experiments, the feasibility and effectiveness of the trust computing scheme proposed in this study are verified.