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Abstract The Underwater Sensor Network (UWSN) comprises sensor nodes with sensing, data processing, and communication capabilities. Due to the limitation of underwater radio wave propagation, nodes rely on acoustic signals to communicate. The data gathered by these nodes is transmitted to coordinating nodes or ground stations for additional processing and analysis. The characteristics of UWSN with underwater channels make them vulnerable to malicious attacks. UWSN communication networks are particularly susceptible to malicious attacks owing to high bit error rates, significant propagation delay variations, and low bandwidth. Moreover, because of the challenging and erratic underwater conditions, limited bandwidth, slow data transmission speed, and power constraints of underwater sensor nodes establishing secure communication in UWSN presents a significant challenge. To address the issues mentioned above, we have introduced the Multipath Malicious Avoidance Routing Protocol (M2ARP) and Foldable Matrix based Padding Rail Fence Encryption Scheme (FM-PRFES) methods to enhance secure communication in UWSNs. The proposed FM-PRFES method encrypts the input data to prevent unauthorized access during transmission within the network. Subsequently, the proposed Energy Efficiency Node Selection (EENS) method is used to identify the significant nodes in the network. Additionally, the Cuckoo Search Optimization (CSO) method is utilized to select the Cluster Head (CH) for data transmission. Subsequently, M2ARP is employed to analyze various routes and avoid adversarial nodes in the network. As a result, the proposed experimental analysis yields more efficient results regarding security, Packet Delivery Ratio (PDR), and throughput performance than traditional approaches.

Opportunistic Network (OppNet) is an enhanced network in the mobile ad hoc network family and has outstanding and updated qualities in the field of Network Technology. The main benefit of OppNet is its ability to store messages that need to be sent to intermediate nodes until the moment of successful communication without imposing a limited time. Developing routes with the best cluster head selection and security are difficult tasks in oppnet. This study uses an improved Secure Fuzzy Trust-Based C-Mean Clustering-based Machine Learning Model (IFTCC) to determine trustworthy nodes. At the same time, an Energy Efficient Harris-Hawks-Remora Routing Protocol (EEHHRR) is proposed to route the cluster head using the secure identify path detection. When selecting the cluster head, the most reliable and energy-efficient node is taken into account. The optimal cluster head among the nodes is chosen using the fuzzy c-means clustering technique. The protection of the intrusive node and the secure transport of data from the source to the destination are the goals of this approach. The best path is found by routing using the EEHHRR. The proposed model finds the safe path using a hybrid optimization technique known as the Harris Hawks and Remora Optimization Algorithm by tracking the node positions and computing the objective function. The proposed model is assessed and contrasted with prevailing methods. The results section shows that PDR, delay, power consumption, message loss, and overhead ratio are 98.8%, 0.02 s, 50.4j, 11,431, 31.15, and 15.14, respectively. [ABSTRACT FROM AUTHOR]

Nowadays, wireless sensor networks (WSNs) are utilised in military-based applications like border surveillance. However, existing border surveillance methods have difficulties with energy efficiency, latency, security, connectivity, optimal path selection and coverage. In this paper, a Voronoi Modified Red Fox (VMRF) algorithm is proposed as a solution to these problems. Initially, secure cluster head (CH) selection and clustering is performed using Secure Spatial Intelligence-Enhanced Voronoi Clustering (SIEVC) to boost energy efficiency, security, and extend network coverage and connectivity. The SIEVC algorithm dynamically selects CHs based on past and present trust, identity trust, and energy trust to identify malicious nodes and form optimal clusters for improved network coverage and connectivity. It also employs dynamic cluster size adjustment to maintain proximity between CHs and cluster members and utilizes node alternation to ensure equitable cluster sizes. This approach minimizes energy depletion, enhances network longevity, and improves load balancing. The algorithm introduces a node alternation mechanism to balance cluster sizes and prevent energy holes. This approach ensures secure and efficient CH selection and promotes even energy distribution. Then the proposed modified red fox (MRF) optimization method, based on the fitness metric, computes the energy-efficient and safe path for data transmission. Trust, energy, distance, link quality and traffic intensity are the factors that the fitness function takes into account. Finally, the data is transmitted to the base station (BS) through CH along the path with the highest fitness value. Then the proposed VMRF algorithm is evaluated using the NS-2 platform, and the outcomes are compared with existing protocols. Based on the evaluations, the VMRF algorithm performs better than existing ones in terms of delay, energy consumption, throughput, packet delivery ratio (PDR), malicious node detection ratio, and residual energy. [ABSTRACT FROM AUTHOR]

There has been a rise in the use of Wireless Sensor Networks (WSNs) in several fields. Depending on the malicious actions, attacks on WSN can take many forms. This research demonstrates how to leverage request and response data from each node to pinpoint the location of malicious nodes. If a node is malevolent, it will prohibit data from being sent to other nodes. They do an excellent job of pretending to be someone else. To enhance route quality, packet dropping can be used to perform a Denial of Service (DoS) assault by rejecting data packets and cutting them off from their destinations. The primary goal of this research is to expose malicious nodes within the network. To avoid being attacked by the false information fed by the adversary through compromised nodes, it is crucial to discover and isolate the nodes that have been compromised. Unfortunately, flat topology networks are difficult to keep safe because of their limited scalability and substantial communication overhead. By computing the packet drop and throughput values with valid nodes, efficient security architecture for the WSN can be proposed, allowing for the detection of malicious activity. If sensor networks are susceptible to attacks, they will be unable to fulfill their duty in a variety of event detection applications. Malicious nodes can cause unacceptable drops in event detection effectiveness and spikes in false alarm rates by producing fake readings and deceptive information. In this research, a method for identifying malicious nodes in WSNs is proposed. This research considers a Trusted Head Node (THN) for monitoring the nodes behaviour in the network to detect the malicious actions in the network. This research proposes a Trusted Head Node for Node Behaviour Analysis for Malicious Node Detection (THN-NBA-MND) in WSN. The proposed model based on node behaviour analysis in frequent time intervals; detect the malicious nodes to improve the network performance in data transmission. The proposed model when contrasted with the existing model performs better in malicious node detection.

Abstract In wireless networking, the security of flying ad hoc networks (FANETs) is a major issue, and the use of drones is growing every day. A distributed network is created by a drone network in which nodes can enter and exit the network at any time. Because malicious nodes generate bogus identifiers, FANET is unstable. In this research study, we proposed a threat detection method for detecting malicious nodes in the network. The proposed method is found to be most effective compared to other methods. Malicious nodes fill the network with false information, thereby reducing network performance. The secure ad hoc on-demand distance vector (AODV) that has been suggested algorithm is used for detecting and isolating a malicious node in FANET. In addition, because temporary flying nodes are vulnerable to attacks, trust models based on direct or indirect reliability similar to trusted neighbors have been incorporated to overcome the vulnerability of malicious/selfish harassment. A node belonging to the malicious node class is disconnected from the network and is not used to forward or forward another message. The FANET security performance is measured by throughput, packet loss and routing overhead with the conventional algorithms of AODV (TAODV) and reliable AODV secure AODV power consumption decreased by 16.5%, efficiency increased by 7.4%, and packet delivery rate decreased by 9.1% when compared to the second ranking method. Reduced packet losses and routing expenses by 9.4%. In general, the results demonstrate that, in terms of energy consumption, throughput, delivered packet rate, the number of lost packets, and routing overhead, the proposed secure AODV algorithm performs better than the most recent, cutting-edge algorithms.

The main objective of this paper is to propose a consensus mechanism, based on trust, for permissioned private blockchain networks. The proposal shows how the use of the cooperativeness of the network nodes is fundamental for the development of a control system, in which the behavior of the nodes is monitored by other nodes. The monitoring of the reputation score is constant in the network and through voting, the node considered malicious to the network is expelled. For the mechanism to work, it is necessary to define rigidity criteria to be applied to actions identified as malicious what are the confidence threshold adopted and what grade receives a node that has not yet been evaluated by the network. Also, determine how many nodes are needed to monitor the behavior of a Malicious Node (MN), in order to identify and expel it more efficiently. The objectives of this work are also to present the performance evaluation of two platforms for the development of permissioned private blockchains in relation to the validation time of a transaction, the response time to chain searches and the transaction mining time and to propose a use case of blockchain technology for application in the storage of electronic medical records in a hybrid approach, which combines the application of public key infrastructure with blockchain technology, to comply with the storage requirements of medical data and offer patient-centric data privacy and access control. [ABSTRACT FROM AUTHOR]

Based on trust management, the service establishment between IoT/User devices and fog networks reduces the impact of malicious data and nodes. Hence, building a trusted environment in the IoT and fog layers is very important. There is no assurance that the generated data or task is standard data; it may also have abnormal data to modify its originality. Similarly, the nodes in the fog environment also have malicious nodes during task offloading. For this, the novel Two-way IoT data & Fog Trust management (TwI-FTM) algorithm is proposed to identify the abnormal data with the help of the Trusted Data Measurement (TDM) and Fog Access Trust Evaluation (FATE) method for identifying the malicious fog access nodes in fog network. Identifying these abnormal data and malicious fog access nodes is done by continuously monitoring fog control nodes with the threshold level. Once it is identified, they will automatically be removed from the IoT-FoG network. The obtained trust measurements are analyzed and compared with other existing methods; it is identified that the proposed TwI-FTM outperforms and has a higher trust degree of 0.68 compared with others. It achieved higher detection accuracy of malicious fog access nodes in fog networks at 21 %, 16 %, and 14 % compared with OTM, TTM, and SLA-Trust, respectively. Finally, the task completion time was also reduced, and it was achieved at 4.5 ms, 5 ms, and 13 ms compared with TTM, OTM, and SLA-Trust, respectively.

The emergence of a technological age, in which nearly everything is linked and accessible from anywhere, is largely due to the Internet. Hence, traditional Networks continue to be difficult and complex to operate despite their widespread deployment. As a consequence, it is challenging to set up the networks in accordance with the established protocols and respond to variations in load and defects by reconfiguring the structure. The data and control layers are packaged together in the contemporary systems, further complicating problems by vertically integrating them. Because of their limited resources and unsecured transmission channel, wireless sensor networks (WSNs) are particularly vulnerable to persistent security attacks. Many hundreds of selforganized and resource-constrained sensor nodes make up the WSN. It is possible to build an ad-hoc network of sensors without a specified architecture or centralised control because these sensor nodes are often organised in a scattered form. The underlying problem is to strengthen the privacy enforcement in these systems because WSNs will have power over real time applications, where unauthorized conduct might lead to significant harm. In order to address this issue, the software-defined network (SDN) technology was developed. With the creation of SD, network operators now have more legitimacy and influence over the networks. Based on a global perspective and centralised management of the network topology, SDN has increased the rigour of its enhancing security. This study describes the software-defined network, outlining its fundamental ideas, how it differs from conventional networking, and its architectural tenets. Also, it highlighted the key benefits of SDN while emphasizing availability, maliciousness, packet delivery and duplication ratio, and efficiency. [ABSTRACT FROM AUTHOR]

In social aware networks, the routing protocol possesses a considerable amount of sensitive user information during node transmission. When selecting the next hop node for transmitting information in social sensing networks, there is an inherent risk of privacy breach due to malicious nodes attacking innocent nodes within the network. To address the issue of user privacy leakage, this study presents a novel privacy-preserving access control algorithm, termed Attribute Trust-based Security (ATST), which is designed to protect sensitive information. The algorithm, based on encrypting the plaintext message to ensure message security, employs the user's attributes to create an attribute trust measurement model and a network access control structure. This process identifies a trusted next hop node for information transmission. Subsequently, the access control tree model derived from the attribute is utilized during decryption. After the message reached its destination node, only the user who satisfied the access control criteria of the decryption module was authorized to decrypt the ciphertext. By verifying the trust measurement and access control of nodes through the dual trust mechanism, it prevented malicious nodes from entering the network, causing damage to the network and affecting the performance of the network. In addition, the clear text message was encrypted and transmitted, and the final receiving node was verified to ensure the secure transmission of the message to the destination node. The simulation outcomes demonstrate that the ATST algorithm effectively mitigates the effects of malevolent nodes on the network, ensuring message transfer efficiency and reducing the average delay. [ABSTRACT FROM AUTHOR]

Dynamic graph neural networks (DGNNs) have demonstrated their extraordinary value in many practical applications. Nevertheless, the vulnerability of DNNs is a serious hidden danger as a small disturbance added to the model can markedly reduce its performance. At the same time, current adversarial attack schemes are implemented on static graphs, and the variability of attack models prevents these schemes from transferring to dynamic graphs. In this paper, we use the diffused attack of node injection to attack the DGNNs, and first propose the node injection attack based on structural fragility against DGNNs, named Structural Fragility-based Dynamic Graph Node Injection Attack (SFIA). SFIA firstly determines the target time based on the period weight. Then, it introduces a structural fragile edge selection strategy to establish the target nodes set and link them with the malicious node using serial inject. Finally, an optimization function is designed to generate adversarial features for malicious nodes. Experiments on datasets from four different fields show that SFIA is significantly superior to many comparative approaches. When the graph is injected with 1% of the original total number of nodes through SFIA, the link prediction Recall and MRR of the target DGNN link decrease by 17.4% and 14.3% respectively, and the accuracy of node classification decreases by 8.7%.

Irrespective of whether the environment is wired or wireless, routing is an important challenge in networks. Since mobile ad hoc networks (MANETs) are flexible and decentralized wireless networks, routing is very difficult. Furthermore, malicious nodes existing in the MANET can damage the routing performance of the network. Recently, reinforcement learning has been proposed to address these problems. Being a reinforcement learning algorithm, the Q-learning mechanism is suitable for an opportunistic routing approach because it not only adapts to changing networks, but also mitigates the effect of malicious nodes on packet transmission. In this study, we propose a new reinforcement learning routing protocol for MANETs called reputation opportunistic routing based on Q-learning (RORQ). Using this protocol, which works based on game theory, a reputation system can detect and exclude malicious nodes in a network for efficient routing. Thus, our method can find a routing path more effectively in an environment attacked by malicious nodes. The simulation results showed that the proposed method could achieve superior routing performance compared with other state-of-the-art routing protocols. Concretely, compared to other algorithms, the proposed method demonstrated performance gains significantly, in terms of packet loss, average end-to-end delay, energy efficiency in both the blackhole attack scenario and the gray hole attack scenario.

Vehicle Ad Hoc Networks (VANETs) can help reduce traffic accidents and improve road safety by broadcasting Emergency Messages (EMs) between vehicles in advance so that the vehicle can take action to avoid accidents. However, its advantages are often compromised by factors such as high mobility, uneven nodes distribution, and signal attenuation, resulting in lower reliability and higher delay in the delivery of EMs. Besides, because of its open and mobility, VANETs are vulnerable to cyber security threats and are prone to multiple malicious attacks in the network. Malicious nodes can join the set of candidate forwarding nodes through collusion and identity forgery, which poses a serious challenge to EMs forwarding. In order to resolve the problems above, this paper proposes a geographic routing strategy to deliver EMs based on trusted nodes, focusing on measuring the reliability of link quality and node quality. The link quality between nodes is evaluated by measuring the actual transmission cost and the link signal-to-noise ratio to minimize the possible link interruption; at the same time, the node trust value is introduced to measure the credibility of the node and filter out the possible malicious nodes in the network. The research results show that the protocol is suitable for dense and sparse traffic conditions, can detect and identify malicious nodes, and has significant performance improvements in message delivery rate, end-to-end delay, and network throughput.

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The network model (MANET) is just a wireless system that has no established infrastructure. These nodes seem to be able to communicate without a central authority. MANETs are ideal for emergency circumstances, vehicle networks, including military activities. However, the MANET's flexibility makes it vulnerable to attacks like black hole attacks. The black hole attack is one of the most common threats to MANET. In this attack, an unauthorized node claims to have the best path to a target node, causing data packets to be misdirected and then dropped. Several fixes have been made now. An overview of black hole attack prevention measures and conclusion are presented in this work. [ABSTRACT FROM AUTHOR]

The accurate identification of malicious nodes in vehicular social networks (VSN) can ensure the secure and efficient operation of the mobile network. The roadside unit (RSU) undertakes various tasks in the internet of vehicles (IoV) and processes a large amount of data. It plays an indispensable and crucial role in the IoV. Therefore, the damage and scope of the attack on RSU are more significant. This paper proposes a trust model for detecting malicious RSUs (TMDMR) to realize the trust decision problem of the roadside unit in VSN. The trust management model is constructed by designing two main decision indicators (QoS trust and social trust) in vehicular social networks, and the algorithm is intended to complete the calculation of global trust. Finally, the global trust server implements the trust decision of RSU. The simulation results demonstrate that TMDMR is better than the comparison scheme in terms of Precision and Recall. When the malicious number of nodes is 45% during OA attack, the Precision of TMDMR is 5.3% and 26.2% higher than comparison schemes, and Recall value is also 4.7% and 30.1% higher than them, respectively. When the malicious number is 45%, TMDMR has the lowest packet dropping rate (23.8% and 44.7%) to the comparison schemes. Its end-to-end delay is 42% and 51.3% lower than other two schemes. It also has advantages in terms of response time to complete a round of detection. [ABSTRACT FROM AUTHOR]

Mobile Ad hoc Network (MANET) is decentralized wireless network and can communicate without existing infrastructure in many areas. MANET is vulnerable to various attacks that affect its performance such as blackhole attack. Blackhole attacker, inject fault routing information to persuade the source node to select the path with malicious node as the shortest path. To eliminate malicious nodes from launching any collaborative attack. A cooperative Trapping Approach (CTA) was proposed based on modifying Ad-hoc On-demand Distance Vector (AODV) routing protocol and trapping the malicious nodes by responding to the trap request message. The approach aims to eliminate and rule out both single and collaborative malicious blackhole nodes from any attack. The approach realizes a backward tracking mechanism to perform the elimination process. The proposed algorithm (CTA) was executed using NS-2 network simulator. The performance metrics that has been considered to evaluate the performance of the proposed algorithm such as throughput, end to end delay, packet delivery ratio, and consuming energy. The experimental results have shown the performance metrics of the proposed approach outperformed other state of at algorithms. [ABSTRACT FROM AUTHOR]

Sixth-generation (6G) wireless networking studies have begun with the global implementation of fifth-generation (5G) wireless systems. It is predicted that multiple heterogeneity applications and facilities may be supported by modern wireless communication networks (MWCNs) with improved effectiveness and protection. Nevertheless, a variety of trust-related problems that are commonly disregarded in network architectures prevent us from achieving this objective. In the current world, MWCN transmits a lot of sensitive information. It is essential to protect MWCN users from harmful attacks and offer them a secure transmission to meet their requirements. A malicious node causes a major attack on reliable data during transmission. Blockchain offers a potential answer for confidentiality and safety as an innovative transformative tool that has emerged in the last few years. Blockchain has been extensively investigated in several domains, including mobile networks and the Internet of Things, as a feasible option for system protection. Therefore, a blockchain-based modal, Transaction Verification Denied conflict with spurious node (TVDCSN) methodology, was presented in this study for wireless communication technologies to detect malicious nodes and prevent attacks. In the suggested mode, malicious nodes will be found and removed from the MWCN and intrusion will be prevented before the sensitive information is transferred to the precise recipient. Detection accuracy, attack prevention, security, network overhead, and computation time are the performance metrics used for evaluation. Various performance measures are used to assess the method's efficacy, and it is compared with more traditional methods. [ABSTRACT FROM AUTHOR]

The vehicular ad hoc network (VANET) constitutes a key technology for realizing intelligent transportation services. However, VANET is characterized by diverse message types, complex security attributes of communication nodes, and rapid network topology changes. In this case, how to ensure safe, efficient, convenient, and comfortable message services for users has become a challenge that should not be ignored. To improve the flexibility of routing matching multiple message types in VANET, this paper proposes a secure intelligent message forwarding strategy based on deep reinforcement learning (DRL). The key supporting elements of the model in the strategy are reasonably designed in combination with the scenario, and sufficient training of the model is carried out by deep Q networks (DQN). In the strategy, the state space is composed of the distance between candidate and destination nodes, the security attribute of candidate nodes and the type of message to be sent. The node can adaptively select the routing scheme according to the complex state space. Simulation and analysis show that the proposed strategy has the advantages of fast convergence, well generalization ability, high transmission security, and low network delay. The strategy has flexible and rich service patterns and provides flexible security for VANET message services. [ABSTRACT FROM AUTHOR]

The exposure of IoT nodes to the internet makes them vulnerable to malicious attacks and failures. These failures affect the survivability, integrity, and connectivity of the network. Thus, the detection and elimination of attacks in a timely manner become an important factor to maintain network connectivity. Trust-based techniques are used in understanding the behavior of nodes in the network. The proposed conventional trust models are power-hungry and demand large storage space. Succeeding this Hidden Markov Models have also been developed to calculate trust but the survivability of the network achieved from them is low. To improve survivability, selfish and malicious nodes present in the network are required to be treated separately. Hence, in this paper, an improved Hidden Markov Trust (HMT) model is developed, which accurately detects the selfish and malicious nodes that illegally intercept the network. The proposed model comprises the Learning Module which aims to understand the behavior of nodes and compute trust using HMT with the expected output. The probability parameters of the HMT model are derived from the data flow rate and the residual energy of the nodes. Next, in Decision-Module, the actual nature of the node is obtained with the help of the evaluated node's likelihood functions. If the node is selfish and is close to crashed state then, is isolated from the routing function, while the selfish node with sufficient energy is immediately destroyed from the network. On the other hand, malicious nodes are provided with a time-based opportunity to reset themselves before being knocked down. Finally, if the node is legitimate, then the function continues smoothly. At last, the Path-Formation-Module establishes the trusted optimal routing path. Further, comparative analysis for attacks such as black-hole, grey-hole, and sink-hole has been done and performance parameters have been extended to survivability-rate, power consumption, delay, and false-alarm-rate, for different network sizes and vulnerability. Simulation result on average provides a 10% higher PDR, 29% lower overhead, and 17% higher detection rate when compared to a Futuristic Cooperation Evaluation Model, Futuristic Trust Coefficient-based Semi-Markov Prediction Model, Opportunistic Data Forwarding Mechanism, and Priority-based Trust Efficient Routing using Ant Colony Optimization trust models presented in the literature. [ABSTRACT FROM AUTHOR]

In the future era of intelligent networks, communication technology and network architecture need to be further developed to provide users with high-quality services. The Space-Air-Ground Integrated Networks (SAGIN) is seen as a potential architecture to provide ubiquitous communication and drive the era of the intelligent global network. The space and air segments in SAGIN can assist in offloading traffic from the ground segment. However, in a highly dynamic and heterogeneous network like SAGIN, offloading decisions are easily affected by the incorporated/malicious nodes. How to ensure security and improve network performance becomes a critical problem. In this paper, we address the above problem by jointly using blockchain and federated reinforcement learning (FRL). Firstly, we propose a blockchain-based secure federated learning framework that combines topology information chain and model chain to assist traffic offloading. Then, we propose a node security evaluation and an enhanced practical byzantine fault tolerance (EPBFT) algorithm to secure the traffic offloading process. Furthermore, we describe the traffic offloading problem as a Markov decision problem (MDP) and employ the Blockchain-based Federated Asynchronous Advantage Actor-Critic (BFA3C) algorithm to solve this problem. Finally, the simulation results show that the BFA3C-based algorithm used in SAGIN with/without malicious nodes achieves superior performance in terms of latency and security. [ABSTRACT FROM AUTHOR]

Mobile Ad hoc Networks (MANETs) have always been vulnerable to Sybil attacks in which users create fake nodes to trick the system into thinking they're authentic. These fake nodes need to be detected and deactivated for security reasons, to avoid harming the data collected by various applications. The MANET is an emerging field that promotes trust management among devices. Transparency is becoming more essential in the communication process, which is why clear and honest communication strategies are needed. Trust Management allows for MANET devices with different security protocols to connect. If a device finds difficulty in sending a message to the destination, the purpose of the communication process won't be achieved and this would disappoint both that device and all of your devices in general. This paper presents, the Two-Tier Multi-Trust based Algorithm for Preventing Sybil Attacks in MANETs (TMTACS). The TMTACS provides a two-tier security mechanism that can grant or revoke trust in the Nodes of the MANET. It's a smart way to identify Sybil nodes in the system. A proficient cluster head selection algorithm is also defined, which selects cluster head efficiently and does load balancing to avoid resource consumption from a single node only. Also, for routing efficient path is selected to deteriorate energy consumption and maximize throughput. The recent technique is compared with Secured QoS aware Energy Efficient Routing (SQEER), Adaptive Trust-Based Routing Protocol (ATRP), and Secure Trust-Aware Energy-Efficient Adaptive Routing (STEAR) in terms of Packet Delivery Ratio (PDR), consumption of energy etc. The simulation was performed on MATrix LABoratory (MATLAB) and the results achieved by the present scheme are better than existing techniques. [ABSTRACT FROM AUTHOR]

Framing a Mobile ad-hoc network (MANET) has various inbuilt complexities that lack Throughput and pack delivery ratio. Clustering based MANET improve the management of the network in terms of easy route construction and routing table size. However, the cluster head (CH) faces more load mainly due to inter and intra-cluster formation. This cause rapid exhausting of Throughput and CH death, leading to network division followed by reducing MANET throughput and Packet Delivery Ratio (PDR). To perform effective prediction of malicious nodes such as Denial service attack (DOS) and data transfer with end to end delay loss and high PDR and Throughput, the proposed introduced a novel Clustering Practical Swarm Optimization-Shortest Distance Calculation by Central Force Optimization (CPSO -SCFO) method. The proposed framework utilize the Particle Swarm Optimization (PSO) algorithm for the selection of CH based on its energy level, mobility, and trust level. The study predicts the malicious node by the trust model estimation method. Following that shortest routing path was detected with the central force optimization (CFO) method. It helps to achieve network availability without disturbance and additional security. The CPSO-SCFO method outperforms the existing methods such as EESSC, EWCA, and MEESC in terms of low modification in CH over simulation time and speed. The CPSO-SCFO framework experienced higher efficiency of PDR 99%, Throughput of 79%kbps and low end to end delay of 15ms and packet loss results of 9%, compared with the AOMDV and modified AOMDV. [ABSTRACT FROM AUTHOR]

This paper presents the use of Compressive Sensing (CS) in the reduction of resource consumption to minimize battery and bandwidth usage. It also focuses on how attacks and misbehavior can be nullified. The proposed Neighborhood Compressive Sensing (NCS) model compresses the neighborhood sparse data such as routing table updates, advertisement and trust information. It minimizes resource consumption because major computations are performed by the leader node. The use of compressive sensing gives the reduction in resource consumption because it reduces the amount of transmitting data in the network. It also prevents a network from unwanted advertisement and attacks because the neighborhood nodes do not accept the advertisements and updates directly, rather it uses leader node’s processed information. The proposed NCS model is implemented in “GloMoSim” on top of the DSR protocol, resulting its effectiveness, as compared to the DSR protocol when the network is misconducting for its selfish needs. Simulation result shows that the proposed NCS model is outperformed DSR in terms of the energy consumption, network lifetime and packet dropping ratio. This work is the extended version of Reduction in Resource Consumption to enhance cooperation in MANET using Compressive Sensing (Akhtar and Sahoo, 2015) [68].

Securing communication in vehicular ad hoc networks (VANETs) is hampered by numerous constraints, making it more difficult. First, traditional security schemes cannot be directly applied in VANET because they consider fixed topology. Second, VANET enables dynamic spectrum access where nodes constantly change frequencies due to their high degree of mobility, resulting in severe consequences on network performance. Third, an effective security scheme in VANET needs local and continual knowledge of nodes. Last, the presence of malicious nodes and their misbehaving activities impair the safety of the drivers since they might alter the content of the sent safety alerts. With these constraints in mind, this paper presents a unique security strategy that utilizes node behaviour during message exchange as a security metric to address these issues. Through the message alert exchange phase, node behaviour is measured through the fuzzy logic framework to generate a rank for each node called trust level (BL), which describes the node's reliability in exchanging safety messages correctly. Moreover, all messages in VANET are encrypted using the existing cryptography techniques. The proposed scheme is developed to enhance communication security in VANET, minimize the effects of malicious nodes, and improve resource utilization in VANET. Evaluation of the proposed scheme shows that it improves the performance of VANET in terms of end-to-end delay, packet delivery ratio, and packet loss ratio. According to the results, our scheme improves throughput by up to 23% and reduces end-to-end delay by up to 60%. [ABSTRACT FROM AUTHOR]

INTRODUCTION: A method for diagnosing abnormal body temperature based on deep neural network is proposed. OBJECTIVES: To improve the diagnostic accuracy, reduce the false alarm rate, and improve the diagnostic level of abnormal body temperature. METHODS: According to the weight of the temperature sensor node itself and its neighbor nodes, the network trust relationship is established, and the node trust value is output through the combination of decision-making. Use trust value and double threshold to identify and remove malicious nodes, and optimize the network structure. The optimized temperature sensor network is used to collect human body temperature data. RESULTS: A deep neural network is used to construct a diagnosis model of abnormal body temperature, so as to realize the diagnosis of abnormal body temperature. CONCLUSION: The experimental results show that the method in this paper has high diagnostic accuracy, low false positive rate and high diagnostic efficiency, and can improve the diagnostic level of abnormal body temperature. [ABSTRACT FROM AUTHOR]

Mobile Ad-hoc Networks (MANET) are decentralized wireless networks that communicate without pre-existing infrastructure. MANETs are vulnerable to the most popular types of attacks and threats, such as wormhole attacks. A wormhole attacks is very challenging issues that records the packets from one location of the network and tunnels them to another location to undermines the performance of the wireless network and disrupt the most routing protocol. However, the existing solutions have been developed to overcome the wormhole attack, but still suffering from additional hardware, incur high delay delivery, or fail to provide high throughput, packet delivery ratio as well as consume higher energy. In this paper a hybrid wormhole attack detection (HWAD) algorithm is proposed, which is able to detect both in-band wormholes through performs round trip time (RTT) based on its hop count, and packet delivery ratio (PDR), also out-of-band wormholes through performs transmission range between successive nodes in a more optimistic manner than existing solutions. HWAD reduce the delay and energy through avoids performing wormhole detections for all available nodes in the network. HWAD does not rely on any special hardware and middleware. The proposed algorithm HWAD was executed using NS-2 network simulator. The performance metrics was taking into consideration to evaluate the performance of the proposed algorithm the throughput, end to end delay, packet delivery ratio, and consuming energy. The proposed algorithm utilized Ad-hoc On-Demand Distance Vector (AODV) routing protocol to improve the detection method. The experimental results have shown the performance metrics of the proposed approach HWAD outperformed in wormhole detection compared with other algorithms.