Dishonest Nodes Research Articles

TbDd: A new trust-based, DRL-driven framework for blockchain sharding in IoT

Integrating sharded blockchain with IoT presents a solution for trust issues and optimized data flow. Sharding boosts blockchain scalability by dividing its nodes into parallel shards, yet it is vulnerable to the 1% attacks where dishonest nodes target a shard to corrupt the entire blockchain. Balancing security with scalability is pivotal for such systems. Deep Reinforcement Learning (DRL) adeptly handles dynamic, complex systems and multi-dimensional optimization. This paper introduces a Trust-based and DRL-driven (TbDd) framework, crafted to counter collusion attack risks and dynamically adjust node allocation, enhancing throughput while maintaining network security. With a comprehensive trust evaluation mechanism, TbDd discerns node types and performs targeted resharding against potential threats. The TbDd framework maximizes the tolerance for dishonest nodes, optimizes node movement frequency, ensures even node distribution in shards, and balances sharding risks. Extensive evaluations validate TbDd’s superiority over conventional random-, community-, and trust-based sharding methods in shard risk equilibrium and reducing cross-shard transactions.

A Two-Stage PBFT Architecture With Trust and Reward Incentive Mechanism

Consensus algorithm is an essential ingredient of any blockchain system. Many different consensus mechanisms such as Practical Byzantine Fault Tolerance (PBFT), Proof-of-Work (PoW), Proof-of-Stake (PoS), and their many derivatives have been proposed over the years, but the complementary problems of performance and resilience to malicious behavior of the nodes have yet to be resolved in a satisfactory manner. In this work, we propose a consensus mechanism that integrates PoS with PBFT, which can effectively deal with dishonest nodes, both individual validators and leaders, whilst maintaining high performance. Our model incentivized truthful behavior by using trust score and reward mechanisms as crucial components of the block validation and ordering processes. The performance of the proposed scheme is evaluated using an analytical model that employs a semi-Markov process, defined by an ergodic multidimensional Markov chain with a finite number of states. The results show the efficiency of the proposed model in consensus-based decision making, even under a high likelihood of dishonest node behavior.

A recommendation management defense mechanism based on trust model in underwater acoustic sensor networks

Underwater acoustic sensor networks (UASNs) have been applied in many civilian and military scenarios, but it is vulnerable to various security threats due to the broadcast transmission characteristics and the environment in which they are located. Trust management mechanism has been proven to be an effect way to improve network security. However, in the trust assessment process, some nodes may provide false suggestions, which will lead to trust conflicts and affect the normal operation of the trust mechanism. To screen out unreliable recommendations and dishonest nodes in the network and avoid potential dangers, a recommendation management trust mechanism based on collaborative filtering and variable weight fuzzy algorithm (CFFTM) is proposed in this paper. First, three kinds of evidence of trust: communication-based evidence, data-based evidence, and energy-based evidence are select as indicators. Then the variable weight fuzzy comprehensive evaluation algorithm is applied to calculate the direct trust value of the node. Secondly, in order to quantify the honesty ability of nodes, honesty degree is defined. Then the overall recommendation trust value of the node is obtained using the proposed collaborative filtering algorithm. The simulation results show that the mechanism can well filter out unreliable recommendations and improve the recognition rate and stability of the trust model under typical attack scenarios.

TB-ICT: A Trustworthy Blockchain-Enabled System for Indoor Contact Tracing in Epidemic Control

Recently, as a consequence of the COVID-19 pandemic, dependence on Contact Tracing (CT) models has significantly increased to prevent spread of this highly contagious virus and be prepared for the potential future ones. Since the spreading probability of the novel coronavirus in indoor environments is much higher than that of the outdoors, there is an urgent and unmet quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions. Despite such an urgency, this field is still in its infancy. The paper addresses this gap and proposes the Trustworthy Blockchain-enabled system for Indoor Contact Tracing (TB-ICT) framework. The TB-ICT framework is proposed to protect privacy and integrity of the underlying CT data from unauthorized access. More specifically, it is a fully distributed and innovative blockchain platform exploiting the proposed dynamic Proof of Work (dPoW) credit-based consensus algorithm coupled with Randomized Hash Window (W-Hash) and dynamic Proof of Credit (dPoC) mechanisms to differentiate between honest and dishonest nodes. The TB-ICT not only provides a decentralization in data replication but also quantifies the node’s behavior based on its underlying credit-based mechanism. For achieving high localization performance, we capitalize on availability of Internet of Things (IoT) indoor localization infrastructures, and develop a data driven localization model based on Bluetooth Low Energy (BLE) sensor measurements. The simulation results show that the proposed TB-ICT prevents the COVID-19 from spreading by implementation of a highly accurate contact tracing model while improving the users’ privacy and security.

Trust-Aware Fog-Based IoT Environments: Artificial Reasoning Approach

Establishing service-driven IoT systems that are reliable, efficient, and stable requires building trusted IoT environments to reduce catastrophic and unforeseen damages. Hence, building trusted IoT environments is of great importance. However, we cannot assume that every node in wide-area network is aware of every other node, nor can we assume that all nodes are trustworthy and honest. As a result, prior to any collaboration, we need to develop a trust model that can evolve and establish trust relationships between nodes. Our proposed trust model uses subjective logic as a default artificial reasoning over uncertain propositions to collect recommendations from other nodes in the IoT environment. It also manages and maintains existing trust relationships established during direct communications. Furthermore, it resists dishonest nodes that provide inaccurate ratings for malicious reasons. Unlike existing trust models, our trust model is scalable as it leverages a Fog-based hierarchy architecture which allows IoT nodes to report/request the trust values of other nodes. We conducted extensive performance studies, and confirm the efficiency of our proposed trust model. The results show that at an early stage of the simulation time (i.e., within the first 2% of the number of transactions), our trust model accurately captures and anticipates the behavior of nodes. Results further demonstrate that our proposed trust model isolates untrustworthy behavior within the same FCD and prevents untrustworthy nodes from degrading trustworthy nodes’ reputations.

Honesty-Based Social Technique to Enhance Cooperation in Social Internet of Things

The Social Internet of Things (SIoT) can be seen as integrating the social networking concept into the Internet of Things (IoT). Such networks enable different devices to form social relationships among themselves depending on pre-programmed rules and the preferences of their owners. When SIoT devices encounter one another on the spur of the moment, they seek out each other’s assistance. The connectivity of such smart objects reveals new horizons for innovative applications empowering objects with cognizance. This enables smart objects to socialize with each other based on mutual interests and social aspects. Trust building in social networks has provided a new perspective for providing services to providers based on relationships like human ones. However, the connected IoT nodes in the community may show a lack of interest in forwarding packets in the network communication to save their resources, such as battery, energy, bandwidth, and memory. This act of selfishness can highly degrade the performance of the network. To enhance the cooperation among nodes in the network a novel technique is needed to improve the performance of the network. In this article, we address the issue of the selfishness of the nodes through the formation of a credible community based on honesty. A social process is used to form communities and select heads in these communities. The selected community heads having social attributes prove effective in determining the social behavior of the nodes as honest or selfish. Unlike other schemes, the dishonest nodes are isolated in a separate domain, and they are given several chances to rejoin the community after increasing their honesty levels. The proposed social technique was simulated using MATLAB and compared with existing schemes to show its effectiveness. Our proposed technique outperforms the existing techniques in terms of throughput, overhead, packet delivery ratio (PDR), and packet-delivery latency.

Enforcing Fairness in Blockchain Transaction Ordering.

In Blockchain networks involving multiple applications, the quality of service of an application is affected by the transaction ordering. For instance, upon issuing payment transactions, users of an application would like to be notified quickly on the transactions approval. The application can be a financial institution (such as a bank), sharing the blockchain with other such applications and is represented by a node. A node might attempt to prioritize its own transactions by including them early in blocks added to the blockchain. A fair block proposal of a node follows a random selection of the transactions among the set of pending transactions the node is aware of. On the contrary, a dishonest node includes more of its transactions at the expense of transactions of other applications. In this work, we propose a toolbox of techniques to enforce such a fair block selection. First, we design an accurate statistical test for the honesty of a proposal and explain it. We conduct experiments demonstrating the accuracy of the new validation scheme. We also describe a technique that enforces fair block selection through concise commitments on the set of pending transactions known to a node. We clarify the advantages of the new mechanisms over state-of-the-art methods.

RETRACTED: An Adaptive Fuzzy System based Insider Attacks detection with Group Key management for Wireless Ad-Hoc Network

Ad-hoc networking of wireless devices permits the establishment of contact links on the taking part hubs without pre-arranged infrastructure. In wireless ad hoc networks, participatorynodestypicallyengageingroupcommunicationssuchasbroadcast, multi-accessor relay functions. Protecting these communications is a challenging task, as internal attackers may secretly cause damage to the network, which is difficult to predict. Internal attackers can affect the sensors negatively and may disrupt the network by dropping sensitive packets or modifying packet information maliciously. Inside attackers are capable of causing many types of activities as well as passive attacks. Attackers located at a critical point in the network can significantly affect network performance. Insider attackers conceal themselves by marking themselves as legitimate nodes. Therefore, it becomes hard for the system to differentiate dishonest nodes from honest nodes. So as to make the communication among the network more secure, an adaptive fuzzy system that uses CSO algorithm for selecting optimal membership function has been proposed. In this approach, the group key is distributed by the cluster head to the cluster members. This makes sure that only the nodes with the same group key can speak with one another. Implementing appropriate group-key management in the system that focuses on internal attacks identifies the dishonest nodes which can be then eliminated through some other security measures. This ensures the absence of the dishonest nodes inside the network. The results have been simulated and the execution of the proposed approach has been contrasted with the tradition FS approach. The results have shown the presented approach has performed better than the traditional approach.

A Malicious Node Detection Strategy Based on Fuzzy Trust Model and the ABC Algorithm in Wireless Sensor Network

Wireless sensor network (WSN) nodes owing to their openness, are susceptible to several threats, one of which is dishonest recommendation attacks providing false trust values that favor the attacker. In this letter, a malicious node detection strategy is proposed based on a fuzzy trust model and artificial bee colony algorithm (ABC) (FTM-ABC). The fuzzy trust model (FTM) is introduced to calculate the indirect trust, and the ABC algorithm is applied to optimize the trust model for detecting dishonest recommendation attacks. Besides, fitness function includes recommended deviation and interaction index deviation to enhance the effectiveness. Simulation results reveal the improved FTM-ABC maintains a high recognition rate and a low false-positive rate, even if the number of dishonest nodes reaches 50%.

LOPE: A Low‐Overhead Payment Verification Method for Blockchains

With its characteristics of decentralization, security, data traceability, and tamper-resistance, the blockchain has been widely used in various domains. Considering the difference in the performance of the devices, the light client is proposed so that devices without the ability to store a full blockchain copy can also participate in the blockchain transactions. However, the light client has to communicate with full nodes and verify the authenticity of a transaction which brings in some extent of communication, computation, and storage overheads to the light client. These overheads cannot be ignored for some low-performance devices, such as embedded devices or IoT chips, and therefore the current light client scheme does not work in this situation. We propose LOPE (a Low-overhead payment vErification method) for poor-capacity nodes in the blockchain system. In LOPE, a grouping protocol is designed to partition full nodes into groups to serve the verification requests of the light client. In addition, Practical byzantine fault tolerance (PBFT) is used to ensure the light client to get a credible result in spite of a few dishonest nodes existing in the group. We conduct LOPE and evaluate it in a testbed. The experiment results show that LOPE reduces more than half of the communication overhead, degrades the computation overhead of the light client to a large extent, and avoids the storage overhead of the hash roots of block headers in the light client. We also conduct theoretical analysis to show the performance improvement and security issues of LOPE.

CONSENSUS ALGORITHMS BASED BLOCKCHAIN OF THINGS FOR DISTRIBUTED HEALTHCARE

Internet of Things (IoT) consists of smart Things with evolution of ubiquitous computing. Fog Computing (FC) processes and analyzes data of these sensors near to users. However, the ever-increasing in number of Things and the consequent explosion in data traffic have led to fail traditional solutions of centralized storage. Blockchain is a new technology developed as a shared ledger build around peer-to-peer network to produce unchangeable blocks that contain multiple data. These blocks are linked to previous ones in sequence called chain through hash functions. Participants in blockchain selects a leader through one of consensus algorithms who adds new blocks in the chain to prevent dishonest nodes from creating invalid blocks. In this paper, we propose IoT based blockchain architecture named blockchain of Things to store medical records in a distributed manner. The architecture is emulated on Fog server Linux-based using Node.js and Postman. Three consensus protocols, namely: Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerance (PBFT) are emulated and communicated using WebSocket. The consensus algorithms are evaluated in terms of CPU utilization and memory usage. The results show that PoS compared to PoW and PBFT is more lightweight and beneficial to IoT application.

A Privacy-Preserving Attack-Resistant Trust Model for Internet of Vehicles Ad Hoc Networks

The Internet of things (IoT) and advancements of wireless technology have evolved intelligent transport systems to integrate billion of smart objects ready to connect to the Internet. The modern era of the Internet of things (IoT) has brought significant development in vehicular ad hoc networks (VANETs) which transformed the conventional VANET into the Internet of Vehicle (IoV) to improve road safety and diminished road congestion. However, security threats are increasing due to dependency on infrastructure, computing, dynamic nature, and control technologies of VANET. The security threats of VANETs could be addressed comprehensively by increasing trustworthiness on the message received and transmitting node. Conversely, the presence of dishonest vehicles, for instance, Man in the Middle (MiTM) attackers, in the network sharing malicious content could be posed as a severe threat to VANET. Thus, increasing trustworthiness among nodes can lead to increased authenticity, privacy, accuracy, security, and trusted information sharing in the VANET. In this paper, a lightweight trust model is proposed, presented model identifying dishonest nodes and revoking its credential in the MiTM attack scenario. Furthermore, addressing the privacy and security requirement, the pseudonym scheme is used. All nodes in the VANET established trust provided by initially RSU, which is a trusted source in the network. Extensive experiments are conducted based on a variety of network scenarios to evaluate the accuracy and performance of the presented lightweight trust model. In terms of recall, precision, and F-score, our presented model significantly outperformed compared to MARINE. The simulation results have validated that the proposed lightweight model realized a high trust level with 40% of MiTM attackers and in terms of F-score 95%, whereas the MARINE model has 90%, which leads to the model to attain high detection accuracy.

Resilient Privacy-Preserving Distributed Localization Against Dishonest Nodes in Internet of Things

Existing distributed localization methods rarely consider the location privacy preservation problem, which however is nonnegligible. Regarding location privacy, typical solutions rely on a curious-but-honest model, requesting that all participants follow the rule. Different from the existing studies, both honest and dishonest models are considered in this article. We first propose a privacy-preserving distributed localization algorithm (PP-DILOC) by adopting a noise-adding mechanism under the curious-but-honest model. The performance of localization and privacy preservation of PP-DILOC are both theoretically analyzed. Then, in the presence of dishonest nodes, we propose a resilient PP-DILOC (RPP-DILOC), where a time-varying relax factor and an adversary detection procedure are added into PP-DILOC. Theoretical results provide sufficient conditions for the convergence of RPP-DILOC. The privacy levels and the localization performance in the absence/presence of dishonest nodes are evaluated through numerical and experimental results.

MARINE: Man-in-the-Middle Attack Resistant Trust Model in Connected Vehicles

Vehicular ad hoc network (VANET), a novel technology, holds a paramount importance within the transportation domain due to its abilities to increase traffic efficiency and safety. Connected vehicles propagate sensitive information which must be shared with the neighbors in a secure environment. However, VANET may also include dishonest nodes such as man-in-the-middle (MiTM) attackers aiming to distribute and share malicious content with the vehicles, thus polluting the network with compromised information. In this regard, establishing trust among connected vehicles can increase security as every participating vehicle will generate and propagate authentic, accurate, and trusted content within the network. In this article, we propose a novel trust model, namely, MiTM attack resistance trust model in connected vehicles (MARINE), which identifies dishonest nodes performing MiTM attacks in an efficient way as well as revokes their credentials. Every node running MARINE system first establishes trust for the sender by performing multidimensional plausibility checks. Once the receiver verifies the trustworthiness of the sender, the received data are then evaluated both directly and indirectly. Extensive simulations are carried out to evaluate the performance and accuracy of MARINE rigorously across three MiTM attacker models and the benchmarked trust model. The simulation results show that for a network containing 35% of MiTM attackers, MARINE outperforms the state-of-the-art trust model by 15%, 18%, and 17% improvements in precision, recall, and F-score, respectively.

RETRACTED ARTICLE: Optimal Hop Selection Based Novel Trust Based DDoS Attack Removal Framework for Reliable and Secured Transmission of Data in VANETs

This paper proposes a new Trust based DDoS Attack Removal Framework (T-DARF) for effective trust management in VANETs. Based on a newly developed data centric validation unit and intrusion detection unit, all the misbehaving nodes are removed and allows the framework to prevent DDoS attacks in a distributed and collaborative manner. In other words, our proposed framework ensures a trusted, reliable communication between vehicles and delivering reduced network overhead by handling the DDoS attacks. T-DARF is built of three elements: (1) for the detection of dishonest nodes it includes a collaborative and distributed elements, (2) to filter the malicious data it includes a data-centric validation element, and (3) a delay checking element for the detection and prevention against DoS and DDoS attacks. Also, a new trust-based routing protocol is developed that uses the idea of companions (i.e. integrating the notions of link quality and trust value of neighbors) to select the trusted vehicles and to select the optimal path we imposed a hybrid Dragonfly based particle swarm optimization (DPSO) algorithm. The simulation results proved the efficiency of our proposed trust based framework on detecting dishonest nodes, the malicious data transmitted by the honest/dishonest nodes and also detecting the colluding attacks that destroys the network resources in a short period. Also, T-DARF in a worst case scenario can sustain its performance degrees and outperforms the effectiveness of existing schemes such as TRIP, AECFV and T-CLAIDS.

Distributed Privacy-Preserving Data Aggregation Against Dishonest Nodes in Network Systems

Privacy-preserving data aggregation (DA) in network systems, e.g., Internet of Things (IoT), is a challenging problem, considering the dynamic network topology, limited computing capacity, energy supply of IoT devices, etc. The difficulty is exaggerated when there exist dishonest nodes, and how to ensure privacy, accuracy, and robustness of the DA process against dishonest nodes remains an open issue. Different from the widely investigated cryptographic approaches, in this paper, we address this challenging problem by exploiting the distributed consensus technique. To mitigate the pollution from dishonest nodes, we propose an enhanced secure consensus-based DA (E-SCDA) algorithm that allows neighbors to detect dishonest nodes, and derive the error bound when there are undetectable dishonest nodes. We prove the convergence of the E-SCDA and show that the algorithm can preserve the privacy associated to nodes’ initial states. Extensive simulations have shown that the proposed algorithm has a high convergence accuracy and low complexity, even when there exist dishonest nodes in the network.

TFDD: A trust-based framework for reliable data delivery and DoS defense in VANETs

A trust establishment scheme for enhancing inter-vehicular communication and preventing DoS attacks ‘TFDD’ is proposed in this paper. Based on a developed intrusion detection module (IDM) and data centric verification, our framework allows preventing DDoS attacks and eliminating misbehaving nodes in a distributed, collaborative and instantaneous manner. In addition, a trusted routing protocol is proposed that, using context-based information such as link stability and trust information, delivers data through the most reliable way. In this study, the simulation results obtained demonstrate the effectiveness of our trust framework at detecting dishonest nodes, as well as malicious messages that are sent by honest or dishonest nodes, after a very low number of message exchanges. Furthermore, colluding attacks are detected in a small period of time, which results in network resources being released immediately after an overload period. We also show that, in a worst-case scenario, our trust-based framework is able to sustain performance levels, and outperforming existing solutions such as T-CLAIDS and AECFV.

T-VNets: A novel trust architecture for vehicular networks using the standardized messaging services of ETSI ITS

In this paper we propose a novel trust establishment architecture fully compliant with the ETSI ITS standard which takes advantage of the periodically exchanged beacons (i.e. CAM) and event triggered messages (i.e. DENM). Our solution, called T-VNets, allows estimating the traffic density, the trust among entities, as well as the dishonest nodes distribution within the network. In addition, by combining different trust metrics such as direct, indirect, event-based and RSU-based trust, T-VNets is able to eliminate dishonest nodes from all network operations while selecting the best paths to deliver legal data messages by taking advantage of the link duration concept. Since our solution is able to adapt to environments with or without roadside units (RSUs), it can perform adequately both in urban and highway scenarios. Simulation results evidence that our proposal is more efficient than other existing solutions, being able to sustain performance levels even in worst-case scenarios.

Mitigation of peer‐to‐peer overlay attacks in the automatic metering infrastructure of smart grids

AbstractMeasurements gathered by smart metres and collected through the automatic metering infrastructure of smart grids can be accessed by numerous external subjects for different purposes, ranging from billing to grid monitoring. Therefore, to prevent the disclosure of personal information through the analysis of energy consumption patterns, the metering data must be securely handled. Peer‐to‐peer networking is a promising approach for interconnecting communication nodes among the automatic metering infrastructure to efficiently perform data collection while ensuring privacy and confidentiality, but it is also prone to various security attacks. This paper discusses the impact of the most relevant peer‐to‐peer attack scenarios on the performance of a protocol for privacy preserving aggregation of metering data. The protocol relies on communication gateways located in the customers’ households and interconnected by means of a variant of the Chord overlay. We also propose some countermeasures to mitigate the effects of such attacks: we integrate a verifiable secret sharing scheme based on Pedersen commitments in the aggregation protocol, which ensures data integrity, with compliance checks aimed at identifying the injection of altered measurements. Moreover, we introduce Chord auxiliary routing tables to counteract the routing pollution performed by dishonest nodes. The paper evaluates the computational complexity and effectiveness of the proposed solutions through analytical and numerical results. Copyright © 2014 John Wiley & Sons, Ltd.

An Optimized and Improved Network Trust Model Based on P2P

Because of the spring up of the P2P network application, its open, anonymous and self-organized characteristics has offered a path for the spreading of virus and junk data, and the security has aroused people’s common concern. The traditional network trust model dealt with dishonest node has certain limitation, for this, improved P2P network trust model and applied D-S evidence theory to the trusted computing of trust model. Through simulation experiment, the result showed: the improved P2P network trust model efficiently increased the successful trading rate of P2P network and improved the network environment