Malicious Node Attacks Research Articles

Understanding the security failures of four user authentication schemes for wireless sensor networks in IoT environment

User authentication protocols are applied to provide a secure conversation between participating entities in wireless sensor networks (WSNs). To identify how to ensure security in these schemes, it is imperative to look into the security vulnerabilities of these authentication mechanisms. As a part of this study, we examine the security of four 2-factor authentication schemes for WSNs (namely, Zhang and Wen, Jangirala et al., Maurya and Sastry and Singh et al.). Security verification result shows that they are all confronted by serious security weaknesses (like sensor node capture attack, exposer of session key by gateway node attack, smart card lost attack, malicious gateway node attack, and session-specific ephemeral information loss attack) and lack of important security functionalities (like multi-factor security, perfect forward secrecy, user anonymity and untraceability, and session key security). This research concentrates on the ‘root cause of the problem. This will help in the research and development of reliable and efficient user authentication mechanisms for WSNs. Our research results point out some new challenges in building reliable multi-factor mechanisms of user authentication for WSNs. We additionally identify some significant insights from the cryptanalysis results of these mechanisms.

A Hybrid Intrusion Detection System to Mitigate Biomedical Malicious Nodes

This paper proposes an intrusion detection system to prevent malicious node attacks that may result in failure links in wireless body area networks. The system utilizes a combination of Optimized Convolutional Neural Networks and Support Vector Machine techniques to classify nodes as malicious or not, and links as failure or not. In case of detection, the system employs a trust-based routing strategy to isolate malicious nodes or failure links and ensure a secure path. Furthermore, sensitive data is encrypted using a modified RSA encryption algorithm. The experimental results demonstrate the improved network performance in terms of data rate, delay, packet delivery ratio, energy consumption, and network security, by providing effective protection against malicious node attacks and failure links. The proposed system achieves the highest classification rate and sensitivity, surpassing similar methods in all evaluation metrics.

A Blockchain-Based Trustworthy Access Control Scheme for Medical Data Sharing

Blockchain is commonly employed in access control to provide safe medical data exchange because of the characteristics of decentralization, nontamperability, and traceability. Patients share personal health data by granting access rights to users or medical institutions. The major purpose of the existing access control techniques is to identify users who are permitted to access medical data. They hardly ever recognize internal assailants from legitimate entities. Medical data will involve multilayer access within the authorized organizations. Considering the cost of permissions management and the problem of insider malicious node attacks, users hope to implement authorization constraints within the authorized institutions. It can prevent their data from being maliciously disclosed by end-users from different authorized healthcare domains. For the purpose to achieve the fine-grained permissions propagation control of medical data in sharing institutions, a trust-based authorization access control mechanism is suggested in this study. Trust thresholds are assigned to different privileges based on their sensitivity and used to generate zero-knowledge proof to be broadcasted among blockchain nodes. This method evaluates the trust of each user through the dynamic trust calculation model. And meanwhile, smart contract is employed to verify whether the user’s trust can activate some permissions and ensure the privacy of the user’s trust in the process of authorization verification. In addition, the authorization transaction between users and institutions is recorded on the blockchain for patient traceability and accountability. The feasibility and effectiveness of the scheme are demonstrated through comprehensive comparisons and extensive experiments.

Cycle‐Consistent Generative Adversarial Network and Crypto Hash Signature Token‐based Block chain Technology for Data Aggregation with Secured Routing in Wireless Sensor Networks

SummaryA fundamental component of Wireless Sensor Networks (WSN) is routing, because it is responsible for data transmission to the base stations (BS). Routing attacks are attacks that have the ability to interfere with the operation of WSN. A reliable routing system is needed for guaranteeing routing security including WSN effectiveness. Many studies have been conducted to improve trust among routing nodes, cryptographic algorithms and centralized routing decisions. Nevertheless, most of the routing techniques are not practical because it is challenging to detect suspicious actions of routing nodes. Generally, there is lack of reliable technique for preventing malicious node attacks. Therefore in this manuscript, Cycle‐Consistent Generative Adversarial Network (CCGAN) optimized with Ebola Optimization Search Algorithm (EOSA) and Crypto Hash Signature (CHS) Token‐based Block chain (BC) Technology for Data Aggregation with Secured Routing in Wireless Sensor Networks (SR‐CCGAN‐EOSA‐BDA‐WSN) is proposed for data aggregation with secured optimum routing in WSN. The proposed methodology uses a Proof of Authority (PoA) method in the block chain network to authenticate the process of node transmission. A Cycle‐Consistent Generative Adversarial Network optimized with the Ebola optimization algorithm methodology (CCGAN‐EOSA) is used to select the validation group required for proofing and select the proper next hop as a forwarding node proficient of securely and easily transmitting messages. The performance metrics, like delay, average latency with energy consumption, block chain token transactions throughput is analyzed. The performance of SR‐CCGAN‐EOSA‐BDAWSN proposed method provides 76.26%, 65.57%, and 42.9% lesser delay under 30% spiteful routing environment; 73.06% are compared with existing methods.

Grouped Multilayer Practical Byzantine Fault Tolerance Algorithm: A Practical Byzantine Fault Tolerance Consensus Algorithm Optimized for Digital Asset Trading Scenarios.

Based on the practical Byzantine fault tolerance algorithm (PBFT), a grouped multilayer PBFT consensus algorithm (GM-PBFT) is proposed to be applied to digital asset transactions in view of the problems with excessive communication complexity and low consensus efficiency found in the current consensus mechanism for digital asset transactions. Firstly, the transaction nodes are grouped by type, and each group can handle different types of consensus requests at the same time, which improves the consensus efficiency as well as the accuracy of digital asset transactions. Second, the group develops techniques like validation, auditing, and re-election to enhance Byzantine fault tolerance by thwarting malicious node attacks. This supervisory mechanism is implemented through the Raft consensus algorithm. Finally, the consensus is stratified for the nodes in the group, and the consensus nodes in the upper layer recursively send consensus requests to the lower layer until the consensus request reaches the end layer to ensure the consistency of the block ledger in the group. Based on the results of the experiment, the approach may significantly outperform the PBFT consensus algorithm when it comes to accuracy, efficiency, and preserving the security and reliability of transactions in large-scale network node digital transaction situations.

Regional autonomous security cooperative spectrum sensing method based on trust value

AbstractIn order to solve the problems of spectrum resource shortage, low spectrum utilization and shadow effect, a regional autonomous security cooperative spectrum sensing (CSS) method based on trust value is proposed to address the vulnerability of CSS technology in cognitive radio to malicious node attacks. Considering the difference in node position, the vulnerability of CSS to malicious node attacks, the large amount of centralized sensing data processing, and the large energy consumption of distributed sensing, based on the trust value update mechanism of ‘slow growth and fast recovery’, the perception area is divided and managed, and the final perception result is determined by majority voting based on the centralized sensing. The convergence value of the time difference of the perceptual process trust value is used to detect the anomaly of the node, and the cumulative difference in trust values between the nodes can detect and locate the malicious node. Experimental simulation results show that the proposed model is superior to traditional algorithms in spectral perception accuracy and malicious node detection.

SDAA: Secure Data Aggregation and Authentication Using Multiple Sinks in Cluster-Based Underwater Vehicular Wireless Sensor Network.

Security is one of the major concerns while designing robust protocols for underwater sensor networks (UWSNs). The underwater sensor node (USN) is an example of medium access control (MAC) that should control underwater UWSN, and underwater vehicles (UV) combined. Therefore, our proposed method, in this research, investigates UWSN combined with UV optimized as an underwater vehicular wireless network (UVWSN) that can completely detect malicious node attacks (MNA) from the network. Thus, MNA that engages the USN channel and launches MNA is resolved by our proposed protocol through SDAA (secure data aggregation and authentication) protocol deployed in UVWSN. SDAA protocol plays a significant role in secure data communication, as the cluster-based network design (CBND) network organization creates a concise, stable, and energy-efficient network. This paper introduces SDAA optimized network known as UVWSN. In this proposed SDAA protocol, the cluster head (CH) is authenticated through the gateway (GW) and the base station (BS) to guarantee that a legitimate USN oversees all clusters deployed in the UVWSN are securely established for providing trustworthiness/privacy. Furthermore, the communicated data in the UVWSN network guarantee that data transmission is secure due to the optimized SDAA models in the network. Thus, the USNs deployed in the UVWSN are securely confirmed to maintain secure data communication in CBND for energy efficiency. The proposed method is implemented and validated on the UVWSN for measuring reliability, delay, and energy efficiency in the network. The proposed method is utilized for monitoring scenarios for inspecting vehicles or ship structures in the ocean. Based on the testing results, the proposed SDAA protocol methods improve energy efficiency and reduce network delay compared to other standard secure MAC methods.

WSN Network Node Malicious Intrusion Detection Method Based on Reputation Score

Wireless sensor network (WSN) is the Internet of Things by a large number of sensors in the external physical environment to obtain data information, and use wireless communication technology to provide users with information transmission services. At this stage, communication and security mechanisms are the main problems faced by WSN. This is because most of the existing sensors are powered by batteries with very limited energy, and most of them are deployed in an outdoor open environment, which is easy to be captured as a malicious node. Network attacks. However, the existing malicious node detection methods have shortcomings such as low efficiency, high energy consumption, and insufficient performance. Therefore, this paper proposes a WSN malicious node intrusion detection method based on genetic algorithm optimization of LEACH hierarchical routing protocol. Based on the optimization of the LEACH protocol by genetic algorithm, the method integrates the reputation evaluation mechanism, and screens and eliminates malicious nodes by calculating direct reputation, indirect reputation and comprehensive reputation, thereby ensuring the safe operation of WSN. The simulation results show that this method can effectively resist the attack of malicious nodes on WSN, and has obvious advantages over other methods.

A Federated Learning Multi-Task Scheduling Mechanism Based on Trusted Computing Sandbox.

At present, some studies have combined federated learning with blockchain, so that participants can conduct federated learning tasks under decentralized conditions, sharing and aggregating model parameters. However, these schemes do not take into account the trusted supervision of federated learning and the case of malicious node attacks. This paper introduces the concept of a trusted computing sandbox to solve this problem. A federated learning multi-task scheduling mechanism based on a trusted computing sandbox is designed and a decentralized trusted computing sandbox composed of computing resources provided by each participant is constructed as a state channel. The training process of the model is carried out in the channel and the malicious behavior is supervised by the smart contract, ensuring the data privacy of the participant node and the reliability of the calculation during the training process. In addition, considering the resource heterogeneity of participant nodes, the deep reinforcement learning method was used in this paper to solve the resource scheduling optimization problem in the process of constructing the state channel. The proposed algorithm aims to minimize the completion time of the system and improve the efficiency of the system while meeting the requirements of tasks on service quality as much as possible. Experimental results show that the proposed algorithm has better performance than the traditional heuristic algorithm and meta-heuristic algorithm.

FRCR: Raft Consensus Scheme Based on Semi Asynchronous Federal Reconstruction

Today, Raft’s distributed cluster scale is growing rapidly and cluster throughput is declining. The Raft consensus algorithm needs to be continuously optimized to adapt to a complex and changeable application environment. To solve the above problems, we propose a federal reconstruction Committee Raft consensus algorithm FRCR. Based on the Federation reconstruction technology, the algorithm trains, updates and evaluates the model of the characteristic data set of the Raft node, runs the model to get the nodes with better performance, constructs the committee mechanism, and improves the quality and speed of the election. We also design a semi asynchronous buffer mechanism and a strategy to resist malicious node attacks to solve the inconsistency and security problems of federation aggregation. Finally, seven aspects of FRCR are tested and analyzed to verify the effectiveness of FRCR in the consensus cluster.

SV-PBFT: An Efficient and Stable Blockchain PBFT Improved Consensus Algorithm for Vehicle-to-Vehicle Energy Transactions

<p>Aiming at the problems of difficult charging, long waiting time and energy loss of IoEV (Internet of electric vehicles) during peak charging period, we design a V2V (vehicle to vehicle) energy trading model to simulate the energy trading process. Considering the problems of malicious node attacks and privacy protection, we combine the V2V energy transaction model with a federated chain and explore a distributed ledger to record the V2V energy transaction process. In addition, we design a credibility mechanism to initialize nodes according to their comprehensive strength and select master nodes based on their behavioral performance of participating in consensus, which ensures the reliability of the consensus. Based on this, we propose a more efficient and promising consensus algorithm SV-PBFT (shapley value-PBFT), which simplifies the consensus process, reduces the communication overhead, and improves the consensus efficiency. The SV-PBFT consensus algorithm is used to replace the traditional consensus algorithm in V2V energy trading. The proposed SV-PBFT algorithm is validated by extensive simulation experiments, and numerical results are provided to confirm the good performance of SV-PBFT in V2V energy trading models.</p> <p> </p>

Zone based Hierarchical Link State for Detecting Malicious Node and Black Hole Attack Detection for Mobile Ad hoc Networks

Zone based Hierarchical Link State for Detecting Malicious Node and Black Hole Attack Detection for Mobile Ad hoc Networks

A New Double-Layer Decentralized Consistency Algorithm for the Multi-Satellite Autonomous Mission Allocation Based on a Block-Chain.

The traditional on-board centralized-distributed mission negotiation architecture has poor security and reliability. It can easily give rise to the collapse of the whole system when the master node is attacked by malicious nodes. To address this issue, the decentralized consistency algorithms commonly used in the internet world are referred to in this paper. Firstly, four typical consistency algorithms suitable for the Internet and which are named RAFT, PBFT, RIPPLE and DPOS are selected and modified for a multi-satellite autonomous mission negotiation. Additionally, based on the above modified consistency algorithms, a new double-layer decentralized consistency algorithm named DDPOS is proposed. It is well known that the above four common consistency algorithms cannot have both a low resource occupation and high security. The DDPOS algorithm can integrate the advantages of four common consistency algorithms due to its freedom of choice attribute, which can enable satellite clusters to flexibly adopt different appropriate consistency algorithms and the number of decentralized network layers. The DDPOS algorithm not only greatly improves the security and reliability of the whole satellite cluster, but also effectively reduces the computing and communication resources occupation of the satellite cluster. Without the presence of a malicious node attack, the resource occupation of the DDPOS algorithm is almost the same as that of the RAFT algorithm. However, in the case of a malicious node attack, compared with the RAFT algorithm, the total computation and total bandwidth occupation of the DDPOS algorithm have decreased by 67% and 75%, respectively. Moreover, it is surprising that although the DDPOS algorithm is more complex, its code size is only about 8% more than the RAFT algorithm. Finally, the effectiveness and feasibility of the DDPOS algorithm in the on-board practical application are analyzed and verified via simulation experiments.

The Detection Scheme Against Selective Forwarding of Smart Malicious Nodes With Reinforcement Learning in Wireless Sensor Networks

Wireless sensor networks (WSNs) are extremely vulnerable to different attacks because of open communication, and distribution in unattended areas. The selective forwarding attack is one of the most difficult inside attacks to be detected for two reasons. The node in a harsh environment has to drop some data packets, and the smart malicious node frequently eludes detection. In this paper, we model a selective forwarding attack of smart malicious nodes with a reinforcement learning (RL) algorithm. To effectively detect the selective forwarding attack under a harsh environment, we design the double-threshold density peaks clustering (DT-DPC) algorithm. Abnormal nodes are identified as malicious and isolated owing to continuous abnormalities. Suspicious nodes are determined by the neighbor voting method because malicious behaviors show up separately and a harsh environment universally disturbs agglomerate nodes. Even if smart malicious nodes elude the detection by an RL algorithm, DT-DPC improves the network throughput. The simulation results show that DT-DPC has a low false detection rate (FDR) of around 1% and a missed detection rate (MDR) of around 10%. The network throughput increases by about 4% under a harsh environment.

Cooperative energy dispatch of smart building cluster based on smart contracts

The problem of efficient energy consumption in buildings is becoming increasingly prominent with the rapid development of the global economy. The application of distributed energy introduces new solutions for energy conservation in buildings. Hence, residents can use and store clean energy to reduce the amount of electricity they buy from the grid, thereby reducing energy consumption. However, the traditional centralised aggregator approach has many disadvantages, including malicious node attacks, difficult dispatching, high transmission consumption and high operation cost. The day-ahead smart contract has been innovatively designed in this paper to enable the collaborative decision of participants on the day-ahead dispatch plan and subsequently ensure the forecast monitoring during the next day. In addition, the intraday smart contract can autonomously realise multilateral transactions based on reputation value and billing autonomously. The simulation result shows that the proposed approach can reduce the cost of distributed energy dispatching.

Zone-based hierarchical link state for detecting malicious node and black hole attack detection for mobile ad-hoc networks

One of the most common types of network attacks involves non-forwardable bandwidth nodes that pretend to be intermediate nodes on the route to certain destinations and drop every packet that subsequently passes through it. This paper proposes attacks through black holes as an analysis method for detecting malicious nodes in Mobile Ad-hoc Networks (MANET). Because the mechanism and protocol are collaborative, it can confidently analyse and identify malicious hosts. To test the effectiveness of this approach, it uses a hybrid Hierarchical Link State routing protocol based on Modified-ZoneBased Hierarchical Link State Routing Protocol (M-ZHLS) in Network Simulations 2 (NS2) with mobile nodes. As the mobility rate changes and the topology size changes, it can be seen that the detection rate of black holes and malicious nodes is extremely high, the average delay is reduced, and the packet delivery rate is increased compared to the Zone-Based Hierarchical Link State Routing Protocol (ZHLS).

A Dynamic Reputation–based Consensus Mechanism for Blockchain

In recent years, Blockchain is gaining prominence as a hot topic in academic research. However, the consensus mechanism of blockchain has been criticized in terms of energy consumption and performance. Although Proof-of-Authority (PoA) consensus mechanism, as a lightweight consensus mechanism, is more efficient than traditional Proof-of-Work (PoW) and Proof-of-Stake (PoS), it suffers from the problem of centralization. To this end, on account of analyzing the shortcomings of existing consensus mechanisms, this paper proposes a dynamic reputation-based consensus mechanism for blockchain. This scheme allows nodes with reputation value higher than a threshold apply to become a monitoring node, which can monitor the behavior of validators in case that validators with excessive power cause harm to the blockchain network. At the same time, the reputation evaluation algorithm is also introduced to select nodes with high reputation to become validators in the network, thus increasing the cost of malicious behavior. In each consensus cycle, validators and monitoring nodes are dynamically updated according to the reputation value. Through security analysis, it is demonstrated that the scheme can resist the attacks of malicious nodes in the blockchain network. By simulation experiments and analysis of the scheme, the result verifies that the mechanism can effectively improve the fault tolerance of the consensus mechanism, reduce the time of consensus to guarantee the security of the system.

Wireless Sensor Networks Routing Attacks Prevention with Blockchain and Deep Neural Network

Routing is a key function in Wireless Sensor Networks (WSNs) since it facilitates data transfer to base stations. Routing attacks have the potential to destroy and degrade the functionality of WSNs. A trustworthy routing system is essential for routing security and WSN efficiency. Numerous methods have been implemented to build trust between routing nodes, including the use of cryptographic methods and centralized routing. Nonetheless, the majority of routing techniques are unworkable in reality due to the difficulty of properly identifying untrusted routing node activities. At the moment, there is no effective way to avoid malicious node attacks. As a consequence of these concerns, this paper proposes a trusted routing technique that combines blockchain infrastructure, deep neural networks, and Markov Decision Processes (MDPs) to improve the security and efficiency of WSN routing. To authenticate the transmission process, the suggested methodology makes use of a Proof of Authority (PoA) mechanism inside the blockchain network. The validation group required for proofing is chosen using a deep learning approach that prioritizes each node's characteristics. MDPs are then utilized to determine the suitable next-hop as a forwarding node capable of securely transmitting messages. According to testing data, our routing system outperforms current routing algorithms in a 50% malicious node routing scenario.

Extreme vulnerability of high-order organization in complex networks

The robustness and vulnerability of complex networks have always been an important research field, and there are many corresponding phenomena in the real world. For example, natural disasters may destroy power facilities and lead to the fragmentation of the electricity network, resulting in paralysis of network functions. The traditional research on the robustness of networks is to study the connected sub-clusters of low-order (original) networks. Recently, the conception of high-order organization was introduced to complex networks and it was pointed out that the functionality of networks is implemented by their high-order organization. Therefore, when the networks are attacked, one should observe the fragmentation of both low-order networks and high-order networks. In other words, when attacked, even if the low-order network is robust, but the network structure of its corresponding high-order network may have been severely damaged. In this case, it will still be difficult for the networks to maintain its functions. In this paper, based on percolation theory, we conducted random and malicious node and edge attacks on low-order networks. We gradually remove nodes or links from the low-order networks and we then analyze how the network fragmentation of the low-order network and the high-order network change after the attack. The results show that high-order networks are significantly more fragile than low-order networks.

A REVIEW OF DIFFERENT TECHNIQUES WHILE TRUST NODE ESTIMATION IN A HIERARCHICAL WIRELESS SENSOR NETWORKS

When we considered a wireless sensors network, the safety measures have been create depending on non-realistic trusted environment. Some malicious sensor nodes may illegally drop the data packets and disturb network communication. Hence, internal networks attacks of malicious nodes becoming a challenging research topic under on its trust and security solutions for WSN. After analysing the trust mechanism schemes for wireless sensor networks, it has been found that there are different trust mechanism schemes are present. Every scheme has several advantages for finding a better path for source to destination node. In this paper, a review on different trust estimation schemes in WSNs is carried out which are classified.