A Lightweight Algorithm against Replica Node Attack in Mobile Wireless Sensor Networks using Learning Agents

Jamshidi, Mojtaba; Poor, Shokooh Sheikh Abooli; Qader, Nooruldeen Nasih; Esnaashari, Mehdi; Mohammad, Reza Meybodi

doi:10.5573/ieiespc.2019.8.1.058

Cited by 11 publications

(8 citation statements)

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“…Jamshidi et al proposed a new algorithm in [41] to detect the cloned nodes in a mobile WSN in which watchdog nodes use the learning agent. e watchdog nodes monitor the movement of nodes as well as network traffic.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Three-Phase Fuzzy Logic Clone Node Detection Model

Lalar

Bhushan

Jangra

et al. 2021

Security and Communication Networks

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Wireless sensor networks have been deployed in the open and unattended environment where the attacker can capture the sensors and create the replica of captured nodes. As the clone nodes have been considered legitimate nodes, clone nodes can initiate different network attacks. We have designed a three-phase clone node detection method named fuzzy logic clone node detection (FLCND). The first phase of FLCND checks whether any node is missing from the network or not. In the next phase, FLCND finds out whether any missing node has arisen in the network in a stipulated time. If any missing node is alive, there is a possibility the node may be cloned. The information of suspected nodes is entered into the Hot-List, which has been maintained in the network. Phase III uses the suspected list and finds out the possibility of clone node using fuzzy logic. Two different scenarios have been simulated in NS2 to evaluate FLCND. The simulation result shows that the proposed method increases the packet delivery ratio (PDR) and reduces packet loss, end-to-end delay, and energy consumption. The simulation results illustrate that the FLCND method reduces the average power consumption by 27% and increases the detection rate by 46% compared to the existing techniques.

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Section: Related Workmentioning

confidence: 99%

An Efficient Three-Phase Fuzzy Logic Clone Node Detection Model

Lalar

Bhushan

Jangra

et al. 2021

Security and Communication Networks

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“…The penalty area of QL is to absorb a policy, which expresses an agent pardons action to take under what surroundings that does not even necessitate a model of the environment and it can grip difficulties with stochastic transitions and plunders, deprived from necessitating adaptations [20], [120]. [23] IoT representation annotation [24] Data-driven management [25] Data and Feedback validation [26] Visualization and understanding [27] Learning environment detection [28] Fraud detection [29] Prediction of the performance [50] Classification of capability [51] Tolerance related acquisition [52] IoT crime forensics [53] Fraud detection in IoT application [54] IoT decision process and making [55] LA Intrusion prediction [30] IoT representation annotation [31] Data-driven management [32] Data and Feedback validation [33] Visualization and understanding [34] Learning environment detection [35] Fraud detection [36] Predicting Software Defects on IoTs [56] Prediction of behavioral changes [57] Signature verification [58] Analysis and decisions [59] Auto-selection of IoT task [60] Traffic incident detection [61] Telecommunication [62] Internet networks [63] MDP Intrusion prediction [37] IoT representation annotation [38] Data-driven management [39] Data and Feedback validation [40] Visualization and understanding [41] Learning environment detection [42] Fraud detection [43] Re...…”

Section: Q-learningmentioning

confidence: 99%

“…Learning largely involves adjustments to the synaptic connections that exist between the neurons with entities including Interconnections, learning rules [21]. ANN holds five basic categories of neuron connection that include a single-layer feed-forward network, a multilayer feed-forward network is a single node with its own feedback, a single-layer recurrent network, and lastly multilayer recurrent network [22]. In table 1, we present a summarized classification of the IoT aspects based on the IoT issues and application.…”

Section: A Reinforcement Learning Applicabilitymentioning

confidence: 99%

Reinforcement Learning Rebirth, Techniques, Challenges, and Resolutions

Shafik

Matinkhah

Etemadinejad

et al. 2020

JOIV : Int. J. Inform. Visualization

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Reinforcement learning (RL) is a new propitious research space that is well-known nowadays on the internet of things (IoT), media and social sensing computing are addressing a broad and pertinent task through making decisions sequentially by deterministic and stochastic evolutions. The IoTs extend world connectivity to physical devices like electronic devices network by use interconnect with others over the Internet with the possibility of remotely being supervised and meticulous. In this paper, we comprehensively survey an in-depth assessment of RL techniques in IoT systems focusing on the main known RL techniques like artificial neural network (ANN), Q-learning, Markov Decision Process (MDP), Learning Automata (LA). This study examines and analyses learning technique with focusing on challenges, models performance, similarities and the differences in IoTs accomplish with most correlated proposed state of the art models. The results obtained can be used as a foundation for designing, a model implementation based on the bottlenecks currently assessed with an evaluation of the most fashionable hands-on utility of current methods for reinforcement learning.

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“…In [15], an intelligent, and lightweight algorithm based on learning agents and watchdog nodes is proposed to detect clone nodes in mobile WSNs. This algorithm employs some watchdog nodes, each one equipped with a learning agent that monitors the network traffic and nodes' movements to identify potential replica nodes in the network.…”

Section: A Related Workmentioning

confidence: 99%

“…Additionally, since sensor nodes are mobile, they should periodically (after each t time unit, or when they reach a new location in the network) broadcast a Hello message, a route request, send data or send a keep-alive message [2,3,10,[15][16][17]. This broadcasting is one of the requirements of a mobile WSN.…”

Section: B System Assumptions and Symbolsmentioning

confidence: 99%

Advanced Extremely Efficient Detection of Replica Nodes in Mobile Wireless Sensor Networks

Safari

Bahmani

Jamshidi

et al. 2019

JOIV : Int. J. Inform. Visualization

Self Cite

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Today, wireless sensor networks (WSNs) are widely used in many applications including the environment, military, and explorations. One of the most dangerous attacks against these networks is node replication. In this attack, the adversary captures a legal node of the network, generates several copies of the node (called, replica nodes) and injects them in the network. Various algorithms have been proposed to handle replica nodes in stationary and mobile WSNs. One of the most well-known algorithms to handle this attack in mobile WSNs is eXtremely Efficient Detection (XED). The main idea of XED is to generate and exchange random numbers among neighboring nodes. The XED has some drawbacks including high communication and memory overheads and low speed in the detection of replica nodes. In this paper, an algorithm is presented to improve XED. The proposed algorithm is called Advanced XED (AXED) in which each node observes a few numbers of nodes and whenever two nodes meet, a new random number is generated and exchanged. The efficiency of the proposed algorithm is evaluated in terms of the memory and communication overheads and its results are compared with existing algorithms. The comparison results show that the proposed algorithm imposes lower overheads to the nodes. In addition, the proposed algorithm is simulated and the simulation results show that the proposed algorithm is able to detect replica nodes faster than XED.

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A Lightweight Algorithm against Replica Node Attack in Mobile Wireless Sensor Networks using Learning Agents

Cited by 11 publications

References 0 publications

An Efficient Three-Phase Fuzzy Logic Clone Node Detection Model

An Efficient Three-Phase Fuzzy Logic Clone Node Detection Model

Reinforcement Learning Rebirth, Techniques, Challenges, and Resolutions

Advanced Extremely Efficient Detection of Replica Nodes in Mobile Wireless Sensor Networks

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