Relevant bibliographies by topics / Malicious node

Academic literature on the topic 'Malicious node'

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Published: 14 March 2023

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Journal articles on the topic "Malicious node"

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Muruganandam, D., and J. Martin Leo Manickam. "Detection and Countermeasure of Packet Misrouting in Wireless Adhoc Networks." Sensor Letters 17, no. 9 (September 1, 2019): 696–700. http://dx.doi.org/10.1166/sl.2019.4127.

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A MANET is an infrastructure-less type network, which consists of number of mobile nodes connected through wireless network interfaces. The Communication among nodes is made successfully when the nodes dynamically set up route among one another. The open nature and infrastructureless type of such networks causes the attacker's interest to penetrate through the network and decrease the network performance. Thus Security becomes a major concern for protected communication between mobile nodes. Packet misrouting stops the packet from reaching the destination by a malicious intermediate node. But the malicious node makes the intuition to its neighbors that it has done the genuine packet forwarding action. Moreover the malicious node makes the neighbours to suspect the normal node as malicious one. The proposed work ensures the detection of malicious nodes and avoids suspecting the trustworthy.
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Bhardwaj, Indu, Sibaram Khara, and Priestly Shan. "A Framework to Systematically Analyse the Trustworthiness of Nodes for Securing IoV Interactions." Scalable Computing: Practice and Experience 21, no. 3 (August 1, 2020): 451–62. http://dx.doi.org/10.12694/scpe.v21i3.1743.

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Trust plays essential role in any securing communications between Vehicles in IOV. This motivated us to design a trust model for IoV communication. In this paper, we initially review literature on IoV and Trust and present a hybrid trust model that separates the malicious and trusted nodes to secure the interaction of vehicle in IOV. Node segregation is done using value of statistics (St). If St of each node lies in the range of mean (m) plus/minus 2 standard deviation (SD) of PDR then nodes behaviour is considered as normal otherwise malicious. The simulation is conducted for different threshold values. Result depicts that PDR of trusted node is 0.63 that is much higher than the PDR of malicious node that is 0.15. Similarly, the average no. of hops and trust dynamics of trusted nodes are higher than that of malicious node. So, on the basis of values of PDR, number of available hops and trust dynamics, the malicious nodes can be clearly identified and discarded.
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Zhang, Zhao Hui, Ming Ming Hu, Dong Li, and Xiao Gang Qi. "Distributed Malicious Nodes Detection in Wireless Sensor Networks." Applied Mechanics and Materials 519-520 (February 2014): 1243–46. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.1243.

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Data theft and node attack in wireless sensor networks causes great damage to the networks and the attacker destroys network and obtains the data of the network by malicious nodes distributed in the network. Therefore, it is necessary to detect these malicious nodes and to eliminate their influence. We propose a distributed malicious nodes detection protocol which called BMND based on Bayesian voting, every node determine its suspected malicious nodes by its request message and abnormal behavior. Also, we determine the malicious nodes by Bayesian voting, so that the network can protect itself from such malicious nodes influence. The simulation results show that our algorithm has good performance in both the detection rate and false positive rate.
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Zhang, Zhiming, Yu Yang, Wei Yang, Fuying Wu, Ping Li, and Xiaoyong Xiong. "Detection and Location of Malicious Nodes Based on Homomorphic Fingerprinting in Wireless Sensor Networks." Security and Communication Networks 2021 (September 24, 2021): 1–12. http://dx.doi.org/10.1155/2021/9082570.

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The current detection schemes of malicious nodes mainly focus on how to detect and locate malicious nodes in a single path; however, for the reliability of data transmission, many sensor data are transmitted by multipath in wireless sensor networks. In order to detect and locate malicious nodes in multiple paths, in this paper, we present a homomorphic fingerprinting-based detection and location of malicious nodes (HFDLMN) scheme in wireless sensor networks. In the HFDLMN scheme, using homomorphic fingerprint and coding technology, the original data is divided into n packets and sent to the base station along n paths, respectively; the base station determines whether there are malicious nodes in each path by verifying the validity of the packets; if there are malicious nodes in one or more paths, the location algorithm of the malicious node is implemented to locate the specific malicious nodes in the path; if all the packets are valid, the original data is recovered. The HFDLMN scheme does not need any complex evaluation model to evaluate and calculate the trust value of the node, nor any monitoring nodes. Theoretical analysis results show that the HFDLMN scheme is secure and effective. The simulation results demonstrate promising outcomes with respect to key parameters such as the detection probability of the malicious path and the locating probability of the malicious node.
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Subramanian, Ananda Kumar, Aritra Samanta, Sasmithaa Manickam, Abhinav Kumar, Stavros Shiaeles, and Anand Mahendran. "Linear Regression Trust Management System for IoT Systems." Cybernetics and Information Technologies 21, no. 4 (December 1, 2021): 15–27. http://dx.doi.org/10.2478/cait-2021-0040.

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Abstract This paper aims at creating a new Trust Management System (TMS) for a system of nodes. Various systems already exist which only use a simple function to calculate the trust value of a node. In the age of artificial intelligence the need for learning ability in an Internet of Things (IoT) system arises. Malicious nodes are a recurring issue and there still has not been a fully effective way to detect them beforehand. In IoT systems, a malicious node is detected after a transaction has occurred with the node. To this end, this paper explores how Artificial Intelligence (AI), and specifically Linear Regression (LR), could be utilised to predict a malicious node in order to minimise the damage in the IoT ecosystem. Moreover, the paper compares Linear regression over other AI-based TMS, showing the efficiency and efficacy of the method to predict and identify a malicious node.
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Zhang, Bo, Qianqian Song, Tao Yang, Zhonghua Zheng, and Huan Zhang. "A Fuzzy Collusive Attack Detection Mechanism for Reputation Aggregation in Mobile Social Networks: A Trust Relationship Based Perspective." Mobile Information Systems 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/5185170.

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While the mechanism of reputation aggregation proves to be an effective scheme for indicating an individual’s trustworthiness and further identifying malicious ones in mobile social networks, it is vulnerable to collusive attacks from malicious nodes of collaborative frauds. To conquer the challenge of detecting collusive attacks and then identifying colluders for the reputation system in mobile social networks, a fuzzy collusive attack detection mechanism (FCADM) is proposed based on nodes’ social relationships, which comprises three parts: trust schedule, malicious node selection, and detection traversing strategy. In the first part, the trust schedule provides the calculation method of interval valued fuzzy social relationships and reputation aggregation for nodes in mobile social networks; further, a set of fuzzy valued factors, that is, item judgment factor, node malicious factor, and node similar factor, is given for evaluating the probability of collusive fraud happening and identifying single malicious nodes in the second part; and moreover, a detection traversing strategy is given based on random walk algorithm under the perspectives of fuzzy valued nodes’ trust schedules and proposed malicious factors. Finally, our empirical results and analysis show that the proposed mechanism in this paper is feasible and effective.
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Alkhalidy, Muhsen, Atalla Fahed Al-Serhan, Ayoub Alsarhan, and Bashar Igried. "A New Scheme for Detecting Malicious Nodes in Vehicular Ad Hoc Networks Based on Monitoring Node Behavior." Future Internet 14, no. 8 (July 26, 2022): 223. http://dx.doi.org/10.3390/fi14080223.

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Vehicular ad hoc networks have played a key role in intelligent transportation systems that considerably improve road safety and management. This new technology allows vehicles to communicate and share road information. However, malicious users may inject false emergency alerts into vehicular ad hoc networks, preventing nodes from accessing accurate road information. In order to assure the reliability and trustworthiness of information through the networks, assessing the credibility of nodes has become a critical task in vehicular ad hoc networks. A new scheme for malicious node detection is proposed in this work. Multiple factors are fed into a fuzzy logic model for evaluating the trust for each node. Vehicles are divided into clusters in our approach, and a road side unit manages each cluster. The road side unit assesses the credibility of nodes before accessing vehicular ad hoc networks. The road side unit evicts a malicious node based on trust value. Simulations are used to validate our technique. We demonstrate that our scheme can detect and evict all malicious nodes in the vehicular ad hoc network over time, lowering the ratio of malicious nodes. Furthermore, it has a positive impact on selfish node participation. The scheme increases the success rate of delivered data to the same level as the ideal cases when no selfish node is present.
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Sivamurugan, D., and L. Raja. "SECURE ROUTING IN MANET USING HYBRID CRYPTOGRAPHY." International Journal of Research -GRANTHAALAYAH 5, no. 4 (April 30, 2017): 83–91. http://dx.doi.org/10.29121/granthaalayah.v5.i4.2017.1798.

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Mobile ad hoc network (MANET) is a group of mobile nodes that communicates with each other without any supporting infrastructure. These networks have some unique features such as dynamic mobility, open nature, lack of infrastructure, limited physical security and they are vulnerable to several security threats. Malicious node can drop all or partial received packets instead of forwarding them to the next hop through the path. In order to find the malicious nodes, an initial transmission is made between the source and destination nodes. Using fuzzy rules, the trust value of each node is computed and it varies from 0 to 1. A common threshold value is set for each node and by using this threshold value, every node in the network can be identified as either a malicious node or a regular node. After identifying the malicious nodes, these nodes are eliminated by muting the power to off state. As the malicious nodes are eliminated between source and destination nodes, source node can select another trusted path to its destination node. For security and authentication of routing information, hybrid cryptography is employed, using advanced encryption standard (AES) and elliptic curve cryptography (ECC) algorithms. AES algorithm is used as symmetric algorithm to encrypt the routing information and ECC algorithm is used as asymmetric algorithm to encrypt the public key. During encryption, the original plain text is converted into cipher text with encrypted public key and similarly during decryption cipher text is converted into original plain text with decrypted private keys. So the proposed method involves both AES and ECC algorithms which provides security mechanism as efficient and sufficient one. The experimental simulations are carried for the proposed model using network simulator 2 (NS-2) for Throughput, Delay, Packet delivery ratio, Packet overhead and Packet drop.
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Alsarhan, Ayoub, Abdel-Rahman Al-Ghuwairi, Esra'a Alshdaifat, Hasan Idhaim, and Omar Alkhawaldeh. "A Novel Scheme for Malicious Nodes Detection in Cloud Markets Based on Fuzzy Logic Technique." International Journal of Interactive Mobile Technologies (iJIM) 16, no. 03 (February 10, 2022): 136–50. http://dx.doi.org/10.3991/ijim.v16i03.27933.

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Cloud security vulnerabilities have recently become more prevalent around the world, posing a threat to cloud service providers' (CSPs) ability to respond to client demands. In cloud market, the requests are announced by the client nodes to their CSP. A malicious node can alter a client's request, resulting in the next cloud market collapse, decreased reliability, and data leaking.To identify malicious nodes in the cloud market, a novel fuzzy multiple criterion decision making scheme is suggested. Authentication test, trust level, traffic size, and node activity levels are all taken into consideration simultaneously as the major criteria for identifying malicious nodes. For each node, the CSP uses fuzzy Integral to generate a composite value based on these criteria. The malicious node is then removed from the cloud market using this composite value. The simulation results demonstrated the potential of the proposed method to prevent nodes in the cloud market from running malware or software that can be used to degrade quality of service by exhausting resources in the cloud market.
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Chen, Jing, Tong Li, and Rui Zhu. "Analysis of Malicious Node Identification Algorithm of Internet of Vehicles under Blockchain Technology: A Case Study of Intelligent Technology in Automotive Engineering." Applied Sciences 12, no. 16 (August 21, 2022): 8362. http://dx.doi.org/10.3390/app12168362.

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False messages sent by malicious or selfish vehicle nodes will reduce the operation efficiency of the Internet of Vehicles, and can even endanger drivers in serious cases. Therefore, it is very important to detect malicious vehicle nodes in the network in a timely manner. At present, the existing research on detecting malicious vehicle nodes in the Internet of Vehicles has some problems, such as difficulties with identification and a low detection efficiency. Blockchain technology cannot be tampered with or deleted and has open and transparent characteristics. Therefore, as a shared distributed ledger in decentralized networking, blockchain can promote collaboration between transactions, processing and interaction equipment, and help to establish a scalable, universal, private, secure and reliable car networking system. This paper puts forward a block-network-based malicious node detection mechanism. Using blockchain technology in a car network for malicious node identification algorithm could create a security scheme that can ensure smooth communication between network vehicles. A consensus on legal vehicle identification, message integrity verification, false message identification and malicious vehicle node identification form the four parts of the security scheme. Based on the public–private key mechanism and RSA encryption algorithm, combined with the malicious node identification algorithm in the Internet of Vehicles, the authenticity of the vehicle’s identity and message is determined to protect the vehicle’s security and privacy. First, a blockchain-based, malicious node detection architecture is constructed for the Internet of vehicles. We propose a malicious node identification algorithm based on the blockchain consensus mechanism. Combined the above detection architecture with the consensus mechanism, a comprehensive and accurate verification of vehicle identity and message authenticity is ensured, looking at the four aspects of vehicle identification, accounting node selection, verification of transmission message integrity and identification of the authenticity of transmission messages. Subsequently, the verification results will be globally broadcast in the Internet of Vehicles to suppress malicious behavior, further ensure that reliable event messages are provided for the driver, improve the VANET operation environment, and improve the operation efficiency of the Internet of Vehicles. Comparing the proposed detection mechanism using simulation software, the simulation results show that the proposed blockchain-based trust detection mechanism can effectively improve the accuracy of vehicle node authentication and identification of false messages, and improve network transmission performance in the Internet of Vehicles environment.
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Dissertations / Theses on the topic "Malicious node"

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Zia, Tanveer. "A Security Framework for Wireless Sensor Networks." University of Sydney, 2008. http://hdl.handle.net/2123/2258.

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Doctor of Philosophy (PhD)
Sensor networks have great potential to be employed in mission critical situations like battlefields but also in more everyday security and commercial applications such as building and traffic surveillance, habitat monitoring and smart homes etc. However, wireless sensor networks pose unique security challenges. While the deployment of sensor nodes in an unattended environment makes the networks vulnerable to a variety of potential attacks, the inherent power and memory limitations of sensor nodes makes conventional security solutions unfeasible. Though there has been some development in the field of sensor network security, the solutions presented thus far address only some of security problems faced. This research presents a security framework WSNSF (Wireless Sensor Networks Security Framework) to provide a comprehensive security solution against the known attacks in sensor networks. The proposed framework consists of four interacting components: a secure triple-key (STKS) scheme, secure routing algorithms (SRAs), a secure localization technique (SLT) and a malicious node detection mechanism. Singly, each of these components can achieve certain level of security. However, when deployed as a framework, a high degree of security is achievable. WSNSF takes into consideration the communication and computation limitations of sensor networks. While there is always a trade off between security and performance, experimental results prove that the proposed framework can achieve high degree of security with negligible overheads.
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Singamsetty, Ratna Sireesha. "Detection of Malicious Nodes in Mobile Ad hoc Networks." University of Toledo / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1321043030.

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KALLAS, KASSEM. "A Game-Theoretic Approach for Adversarial Information Fusion in Distributed Sensor Networks." Doctoral thesis, Università di Siena, 2017. http://hdl.handle.net/11365/1005735.

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Every day we share our personal information through digital systems which are constantly exposed to threats. For this reason, security-oriented disciplines of signal processing have received increasing attention in the last decades: multimedia forensics, digital watermarking, biometrics, network monitoring, steganography and steganalysis are just a few examples. Even though each of these fields has its own peculiarities, they all have to deal with a common problem: the presence of one or more adversaries aiming at making the system fail. Adversarial Signal Processing lays the basis of a general theory that takes into account the impact that the presence of an adversary has on the design of effective signal processing tools. By focusing on the application side of Adversarial Signal Processing, namely adversarial information fusion in distributed sensor networks, and adopting a game-theoretic approach, this thesis contributes to the above mission by addressing four issues. First, we address decision fusion in distributed sensor networks by developing a novel soft isolation defense scheme that protects the network from adversaries, specifically, Byzantines. Second, we develop an optimum decision fusion strategy in the presence of Byzantines. In the next step, we propose a technique to reduce the complexity of the optimum fusion by relying on a novel nearly-optimum message passing algorithm based on factor graphs. Finally, we introduce a defense mechanism to protect decentralized networks running consensus algorithm against data falsification attacks.
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Haddadou, Nadia. "Réseaux ad hoc véhiculaires : vers une dissémination de données efficace, coopérative et fiable." Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1023/document.

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Les réseaux ad hoc véhiculaires (VANETs) permettent le partage de différents types de données entre les véhicules, de manière collaborative. Dans cette thèse, nous nous sommes tout particulièrement intéressés aux applications de sûreté et de sécurité routière, dédiées à l'échange des informations sur l'état de l'environnement routier. Les contraintes de ces applications en termes de qualité de services sont des plus rigoureuses, car l'acheminent de leurs données doit être exhaustif et ne souffrir d'aucun retard pour assurer une information utile et en temps opportun au profit de tous les usagers concernés. Cet acheminement doit faire face aux difficultés induites par la dispersion et la forte mobilité des véhicules, l'absence ou l'insuffisance d'infrastructure, la densité variable du réseau, la surcharge en informations à envoyer et l'étendue des zones géographiques à couvrir. En effet, la problématique de diffusion des données dans les VANETs s'avère non-triviale et de nombreux verrous scientifiques doivent être levés pour permettre un support efficace, collaboratif et fiable pour les applications de sûreté et de sécurité routière.Plus précisément, nous aborderons la problématique de la dissémination collaborative en se posant trois questions : “comment disséminer les données ? À quel moment le faire ? Mais aussi quoi disséminer et comment inciter à le faire ? ” Nous avons apporté des réponses à travers les trois contributions de cette thèse. La première consiste à proposer une stratégie de dissémination efficace, qui soit adaptée à l'importance de l'information échangée et à sa durée de vie, permettant ainsi d'éviter un processus de diffusion intensif. Celui-ci est inapproprié dans ce cas de figure, car il génère de la congestion et beaucoup de redondance. Une étude de performances par simulation est réalisée, laquelle montre une diminution de 90% du taux de messages redondants par rapport au cas de la diffusion par inondation. Afin d'améliorer plus encore les performances du processus de diffusion des messages de sûreté, nous proposons, dans un second temps, un ordonnanceur pour l'accès au canal de communication qui a pour objectif de réduire le nombre de collisions dues aux synchronisations afférentes à l'utilisation du multi-canal dans le standard IEEE 802.11p/1609.4 et donc élever le taux de réception des données. Nous basons notre proposition sur la théorie de l'arrêt optimal, qui décide du moment opportun pour l'envoi d'une information en conciliant occupation du canal, efficacité de l'envoi et délai d'ajournement toléré par une information. Dans notre cas, la théorie de l'arrêt optimal est formulée par un processus de décision Markovien (MDP). Nous montrons ainsi par simulation une amélioration substantielle du taux de réception (de 25%) et une diminution importante des pertes (de 47%).Après s'être intéressé à l'aspect quantitatif des performances du réseau, nous nous intéresserons ensuite à l'amélioration de la fiabilité du processus de diffusion. Cette fiabilité est obtenue grâce à l'incitation des véhicules à la coopération et à l'exclusion des véhicules malicieux de celui-ci. Ceci est réalisé au travers de la proposition d'un modèle de confiance, inspiré des jeux de signaux. Le modèle crée une situation d'équilibre, tel que les différentes parties le composant ne soient pas tentées de le contourner, ainsi découle une auto-sélection des véhicules, laquelle est rapide et peu coûteuse. À notre connaissance, notre modèle est le seul à s'attaquer aux effets néfastes des deux types de véhicules, malicieux et égoïstes, en même temps. Comme précédemment, nous évaluons les performances de notre solution au travers d'une modélisation par une chaîne de Markov et divers jeux de simulation. Ceci a permis de montrer que 100% des véhicules malicieux sont exclus, avec le maintien d'un taux de coopération élevé dans le réseau, soit une amélioration de 42%
Vehicular Ad Hoc Networks (VANETs) allow sharing different kinds of data between vehicles in a collaborative way. In this thesis, we are particularly interested in road safety applications, designed for the exchange of information on road traffic and conditions. This kind of applications have strict Quality of Service (QoS) requirements, as data must be routed thoroughly and without any delays so for assuring the timely delivery of useful information to the drivers. In this context, data routing must face several issues raised by the high mobility and dispersion of vehicles, inadequate or completely lacking infrastructure, a variable network density, network saturation due to the large of information to deliver, and the size of the geographical areas to cover. Indeed, the problem of data dissemination in VANETs is non-trivial, and several research challenges must be solved in order to provide an efficient, collaborative, and reliable support for road safety applications. Specifically, we will address the problem of collaborative data dissemination through the following three questions: “How to perform data dissemination?”, “When should we do it?”, and “What must be disseminated?” We have provided answers to these questions through the three contributions of this thesis. Our first contribution is an efficient dissemination strategy, specifically tailored to the importance of the exchanged information as well as its lifespan, which is able to avoid the intensive dissemination process that generates network congestion and data redundancy. We confirm our statements and validate the performance of our solution by modeling it using a discrete-time Markov chain, which demonstrates the number of necessary retransmissions for all concerned vehicles to receive information. Moreover, we performed extensive simulations that show a reduction of up to 90% of redundant messages with respect to message flooding dissemination strategies. Next, in order to further improve the road safety message dissemination process, we propose a communications channel access scheduler, which aims at reducing the number of collisions caused by IEEE 802.11p/1609.4 multi-channel synchronizations, and thus improving the data reception rate. We base our solution on the optimal stopping theory, which chooses the right moment to send information by balancing the channel occupancy rate, the data delivery efficiency, and the maximum deferment delay tolerated by the information. To this end, we formulate the optimal stopping theory through a Markov decision process (MDP). We show through simulation-based evaluations an improvement of the reception rate of up to 25% and a reduction of up to 47% of message losses. Finally, after being interested in the quantitative aspect of network performance, we centered our efforts on improving the reliability of the dissemination process, which is obtained by motivating vehicles to cooperate and evicting malicious vehicles from the process. To this end, we propose a trust model inspired on signaling games, which are a type of dynamic Bayesian games. Through the use of our model, equilibrium is achieved, thus resulting in a fast and low-cost vehicle self-selection process. We define the parameters of our trust model through a discrete-time Markov chain model. To the best of our knowledge, our solution is the only existing solution that tackles the negative effects introduced by the presence of both malicious and selfish vehicles in a VANET. We evaluated the performance of our solution by modeling it using a Markov chain, and a set of simulations. Our results show that up to 100% of malicious vehicles are evicted while keeping a high cooperation rate, thus achieving an improvement of 42% when compared to other similar solutions
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Langer, André, and Tom Kühnert. "Security issues in Address Autoconfiguration Protocols." Universitätsbibliothek Chemnitz, 2007. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200700491.

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Dynamic address assignment is one of the most important features in wireless ad hoc networks if nodes should be enabled to join and to work in the network by automatically configuring all necessary settings. Different approaches have been developed throughout the last years to achieve this objective of Dynamic Address Autoconfiguration but research primarily focused on efficiency and correctness, less on security issues. Whereas Duplicate Address Detection has become reliable in commonplace scenarios, it is still relatively easy to suspend the whole network functionality in extraordinary situations within the boundaries of a Dynamic Address Configuration Protocol. In this paper, we therefore want to point out shortcomings and weaknesses in existing protocol solutions which address dynamic IP address assignment. We concentrate on a leader-based approach called ODACP and want to propose several solutions which improve the original protocol in such a way that it is safer against malicious host activities. Finally, we will demonstrate the improvements of our solution in a separate test scenario.
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chen, stanley, and 陳嘉融. "The Malicious Node Detection and Identification Mechanisms for Wireless Sensor Networks." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/63265720978978639169.

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碩士
長庚大學
資訊管理研究所
94
Wireless Sensor Network (WSN) is a new developing type of wireless networks. Comparing with Wireless Local Area Network (WLAN), WSN has more restrictions on hardware specification of sensor node and deployment environment. The application of WSN has been growing in recent years. Many security protocols that designed for this particular network had been developed, simulated, verified and successfully implemented. However, the security challenge and difficulty that WSN has to faced will not be able to be solved completely in a few years. This study will begin with improving the whole of security of WSN to propose a suite of malicious node detection and identification mechanisms for WSN. These mechanisms include key pre-load mechanism, neighbor discovery mechanism, authentication key distribution mechanism and node authentication mechanism. The first three mechanisms may be regard as pre-processing to execute the last mechanism. After pre-processing, node in the network can easily detect whether malicious node in its communication range exists. If malicious node exists, it could be identified simultaneously.
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Li, Yang-Tang, and 李泱瑭. "The Study of Active Malicious Nodes Detection in Mobile Ad Hoc Networks." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/08131356805533374286.

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碩士
中華大學
資訊工程學系碩士班
101
In mobile ad hoc networks (MANET), network security problems emerge in an endless stream. For example, malicious nodes may become immediate nodes of routing paths first by replying spoof routing information. Then data packets might be stolen, modified, and even dropped by malicious nodes. These kinds of behavior interfere or interrupt communication between nodes, wasting unnecessary bandwidth resource. In the literature, there exists many works on solving malicious nodes problems in MANET. We can classify them into three categories. The methods of the first class are to find more safety routing paths by modifying original routing protocols. In the second class, the proposed solutions are trying to identify spoof routing information by adding new detecting protocols. In the third class, neighboring nodes help to identify malicious nodes by eavesdropping whether packets are transmitted or not. But these solutions proposed in the literature can not avoid extra overhead in each node. In addition, it's hard to be practicable by modifying or adding protocols to solve malicious nodes problems. In this paper, we proposed a new method to detect malicious nodes actively. Without modification and addition of original routing protocols, only few pairs of detection nodes are needed to identify and isolate malicious nodes. In our simulation, the results show that packets delivery rate can be improved to 23% by one pair of detection nodes and the average overhead of each node is only increased by 0.1 KB/s.
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FanChiang, Chun-Chih, and 范姜群志. "Robust transmission algorithm in network coding domain with the presence of malicious nodes." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/00663336860989909666.

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碩士
國立交通大學
網路工程研究所
99
Network coding has become an important file transfer method in P2P networks. But it suffers from the jamming attack when compared with store and forward method. In this thesis, we propose a system that reduces the drawbacks of malicious attacks. In our system, we use the reputation system to separate good users from bad ones and take homomorphic hash function to verify the received content and find a proper check rate for received blocks so that the transmitted data from a peer can reach a predefined correct rate.
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Tsai, Hsiao-Chien, and 蔡効謙. "On Design of Reputation Mechanisms to Detect Malicious Nodes in Mobile Ad Hoc Networks." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/445m4d.

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博士
國立臺灣科技大學
資訊管理系
99
A mobile ad hoc network (MANET) is a wireless communication network formed by a group of mobile devices. In a MANET environment, fixed infrastructure is not supported. When two nodes want to establish communication channel between each other, they require other intermediate nodes, which may move themselves during the communication session, to cooperatively help them to dynamically construct a route. There is no central authority in a MANET, therefore, a node cannot monitor and enforce other nodes to cooperatively provide reliable communication services. Any intermediate node in a data routing path may arbitrarily decide what action it will perform when receiving a route request or a forwarding data packet. Hence, for selfish or malicious reasons, malicious actions such as packet dropping and false information dissemination may be performed by a mobile node easily. Therefore, how to dynamically detect malicious nodes such that normal communications will not be disrupted or delayed and false information will not be spread, has become a critical issue and a challenging research topic in MANETs. This dissertation proposed several mechanisms based on different concepts to detect malicious nodes in MANETs. First of all, a fuzzy inference engine for nodes in a MANET is proposed. The engine installed inside a node can infer the trust level of a target node based on observing reports from its neighboring nodes. Secondly, a node reputation system, which dynamically changes its trust evaluation models based on the current status of MANET environment, is introduced. Finally, a reputation calibration mechanism for general reputation systems is derived. The most challenge issue of malicious node detection in MANETs is that the dynamics of node mobility and everlastingly changed network status make trust evaluation of a target node inaccurate. The proposed reputation calibration mechanism can correct inaccurate trust value and let reputation system effectively detect malicious nodes in error-prone networks. The proposed mechanisms are all extensively evaluated by network simulations. The simulation results show that the reputation calibration mechanism is a promising way to detect malicious nodes in highly mobile and unstable networks. The lesson we learned is that there is no clear rule to define whether a detected node behavior is based on malicious motivation in MANETs. Using fixed rules to detect malicious node behaviors is not always suitable. Instead, by adopting calibration mechanism, we can easily detect whether a node behavior is more toward to misbehaved direction or not. Dynamically constructing detection rules based on the behaviors of neighboring nodes is a promising way to effectively detect malicious nodes.
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Tsou, Po-Chun, and 鄒博鈞. "An Efficient Hybrid Defense Architecture to Prevent Malicious Nodes in Mobile Ad hoc Networks." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/38033338680083169390.

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碩士
國立宜蘭大學
資訊工程研究所碩士班
99
With the widespread availability of mobile devices, and rapid development of wireless technologies, the number of users of Mobile Ad hoc Networks (MANETs) is increasing at a quick rate. Due to the fact that infrastructure is not needed for MANETs, it can be deployed fast and conveniently in any environment, which makes it suitable for military operations, emergent preparedness and response operations. However, due to the dynamic nature of its network topology, its infrastructure-less property and the lack of certificate authority, achieving security of data and routing in MANETs is an ongoing challenge. Common routing protocols in MANETs such as DSR, AODV, to name a few, do not implement mechanisms for detection and responses to attacks. Focusing on possible attacks by malicious nodes based on the DSR protocol, this thesis presents a mechanism (referred to as Cooperative Bait Detection Scheme (CBDS)) that can be used to effectively detect malicious nodes launching black-hole/gray-hole attacks as well as cooperative black-hole attacks. CBDS integrates the advantages of both proactive and reactive defense architectures, and implements a reverse tracing technique to help achieving the stated goal. Simulation results are given to validate the stated goal, showing that under malicious nodes attacks, CBDS outperforms the DSR, 2ACK, and BFTR protocols in terms of packet delivery ratio and routing overhead, chosen as performance metrics.
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Books on the topic "Malicious node"

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Jaiswal, Anurag Kumar. Malicious Behavior in Mobile Adhoc Networks: An Scheme to detect Misbehaving Nodes in Mobile Ad-Hoc Network MANET. LAP Lambert Academic Publishing, 2012.

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Book chapters on the topic "Malicious node"

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Dumka, Ankur, Alaknanda Ashok, Parag Verma, Anuj Bhardwaj, and Navneet Kaur. "Malicious Node Detection Mechanisms." In Security Issues for Wireless Sensor Networks, 213–40. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003257608-8.

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Rahim, Aneel, and Fahad bin Muyaha. "Impact of Malicious Node on Broadcast Schemes." In Security Technology, 86–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10847-1_11.

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Mondal, Subhash, Subinoy Mukherjee, and Suharta Banerjee. "Machine Learning Based Malicious Node Detection in IoT Environment." In Advanced Techniques for IoT Applications, 316–26. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4435-1_30.

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Yang, Hongyu, Xugao Zhang, and Fang Cheng. "A Novel Wireless Sensor Networks Malicious Node Detection Method." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 697–706. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21373-2_59.

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Anjana, K. P., and K. G. Preetha. "A Novel Approach for Malicious Node Detection in MANET." In Advances in Intelligent Systems and Computing, 163–73. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28031-8_14.

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Ganesan, A., and A. Kumar Kompaiya. "Discovering the Performance of MANET with Malicious and Non-malicious Node Using Newton–Raphson Method." In Smart Innovation, Systems and Technologies, 423–39. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7447-2_38.

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Zheng, Minzhen, Shudong Li, Danna Lu, Wei Wang, Xiaobo Wu, and Dawei Zhao. "Structural Vulnerability of Power Grid Under Malicious Node-Based Attacks." In Communications in Computer and Information Science, 446–53. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1304-6_36.

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Dabhade, Vaibhav, and A. S. Alvi. "Malicious Node Detection and Prevention for Secured Communication in WSN." In Computer Networks, Big Data and IoT, 121–36. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0898-9_10.

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Rathod, Punit, Nirali Mody, Dhaval Gada, Rajat Gogri, Zalak Dedhia, Sugata Sanyal, and Ajith Abraham. "Security Scheme for Malicious Node Detection in Mobile Ad Hoc Networks." In Distributed Computing - IWDC 2004, 541–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30536-1_68.

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Liu, Yuanzhen, Yanbo Yang, Jiawei Zhang, and Baoshan Li. "Design of Anti Machine Learning Malicious Node System Based on Blockchain." In Cyberspace Safety and Security, 358–73. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18067-5_26.

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Conference papers on the topic "Malicious node"

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Y Al Yahmadi, Faisal, and Muhammad R Ahmed. "Malicious Node Detection in Smart Grid Networks." In 11th International Conference on Computer Science and Information Technology (CCSIT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110716.

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Many countries around the world are implementing smart grids and smart meters. Malicious users that have moderate level of computer knowledge can manipulate smart meters and launch cyber-attacks. This poses cyber threats to network operators and government security. In order to reduce the number of electricity theft cases, companies need to develop preventive and protective methods to minimize the losses from this issue. In this paper, we propose a model based on software that detects malicious nodes in a smart grid network. The model collects data (electricity consumption/electric bill) from the nodes and compares it with previously obtained data. Support Vector Machine (SVM) model is implemented to classify nodes into good or malicious nodes by (high dimensional) giving the statues of 1 for good nodes and status of -1 for malicious (abnormal) nodes. The detection model also displays the network graphically as well as the data table. Moreover, this model displays the detection error in each cycle. It has a very low false alarm rate (2%) and a high detection rate as high as (98%). Future developments can trace the attack origin to eliminate or block the attack source minimizing losses before human control arrives.
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Anceaume, Emmanuelle, Yann Busnel, and Bruno Sericola. "Uniform node sampling service robust against collusions of malicious nodes." In 2013 43rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, 2013. http://dx.doi.org/10.1109/dsn.2013.6575363.

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Atassi, A., N. Sayegh, I. Elhajj, A. Chehab, and A. Kayssi. "Malicious Node Detection in Wireless Sensor Networks." In 2013 Workshops of 27th International Conference on Advanced Information Networking and Applications (WAINA). IEEE, 2013. http://dx.doi.org/10.1109/waina.2013.135.

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Ahmad, Asma'a, Majeed Alajeely, and Robin Doss. "Reputation based malicious node detection in OppNets." In 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, 2016. http://dx.doi.org/10.1109/jcsse.2016.7748925.

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Hardy, Tyler J., Richard K. Martin, and Ryan W. Thomas. "Malicious node detection via physical layer data." In 2010 44th Asilomar Conference on Signals, Systems and Computers. IEEE, 2010. http://dx.doi.org/10.1109/acssc.2010.5757772.

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Li, Na, and David Lee. "Network Court Protocol and Malicious Node Conviction." In 2007 IEEE International Conference on Network Protocols. IEEE, 2007. http://dx.doi.org/10.1109/icnp.2007.4375869.

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Mahbooba, Basim, Mohan Timilsina, and Martin Serrano. "Trusting Machine Learning Algorithms in Predicting Malicious Nodes Attacks." In 9th International Conference on Artificial Intelligence and Applications (AIAP 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120408.

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Identifying network attacks is a very crucial task for Internet of things (IoT) security. The increasing amount of IoT devices is creating a massive amount of data and opening new security vulnerabilities that malicious users can exploit to gain access. Recently, the research community in IoT Security has been using a data- driven approach to detect anomaly, intrusion, and cyber-attacks. However, getting accurate IoT attack data is time-consuming and expensive. On the other hand, evaluating complex security systems requires costly and sophisticated modeling practices with expert security professionals. Thus, we have used simulated datasets to create different possible scenarios for IoT data labeled with malicious and non-malicious nodes. For each scenario, we tested off a shelf machine learning algorithm for malicious node detection. Experiments on the scenarios demonstrate the benefits of the simulated datasets to assess the performance of the ML algorithms.
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Pongaliur, Kanthakumar, Li Xiao, and Alex X. Liu. "CENDA: Camouflage Event Based Malicious Node Detection Architecture." In 2009 IEEE 6th International Conference on Mobile Adhoc and Sensor Systems (MASS). IEEE, 2009. http://dx.doi.org/10.1109/mobhoc.2009.5337045.

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Padmaja, P., and G. V. Marutheswar. "Detection of Malicious Node in Wireless Sensor Network." In 2017 IEEE 7th International Advance Computing Conference (IACC). IEEE, 2017. http://dx.doi.org/10.1109/iacc.2017.0052.

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Griswold, Richard L., and Sirisha R. Medidi. "Malicious node detection in ad-hoc wireless networks." In AeroSense 2003, edited by Raghuveer M. Rao, Soheil A. Dianat, and Michael D. Zoltowski. SPIE, 2003. http://dx.doi.org/10.1117/12.485911.

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