Mitigation of Black Hole and Gray Hole Attack Using Swarm Inspired Algorithm With Artificial Neural Network

Rani, Pooja; Kavita, .; Verma, Sahil; Nguyen, Gia Nhu

doi:10.1109/access.2020.3004692

Cited by 74 publications

(31 citation statements)

References 47 publications

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“…The scheme can detect black hole attacks and selective forwarding attacks with high accuracy without depleting the nodes of their energy. [35] proposed a protection mechanism against black hole and selective forwarding attacks based on an artificial neural network as a deep learning algorithm along with the swarmbased artificial bee colony optimization technique. The mechanism performs better in contrast to others under black holes as well as selective forwarding attack condition for a mobile ad hoc network.…”

Section: Ai-based Schemesmentioning

confidence: 99%

“…But the transient fluctuation of channels may result in the rapid change of the threshold values, and then draw mistake detection results. The AI-based detection schemes [34][35][36][37][38][39][40][41][42][43][44][45] find malicious nodes by actively learning their malicious behaviors which are different from those of normal nodes. But these schemes need a long learning process or game process to model the malicious behaviors.…”

Section: Introductionmentioning

confidence: 99%

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Employ DBSCAN and Neighbor Voting to Screen Selective Forwarding Attack Under Variable Environment in Event-Driven Wireless Sensor Networks

Liu

2021

IEEE Access

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In the event-driven wireless sensor networks (EWSNs), the event of interests occurs irregularly and at random in the network. Then, sensor nodes near the event sense the event and send out data packets of the event. Next, router nodes (RNs) forward those packets to the sink node (SN) by multi-hop communications. Compromised RNs would become malicious and launch selective forwarding attacks by dropping part of or all the packets from other nodes. On the other hand, a harsh environment makes the channel poor, so the routing nodes under a harsh environment have low packet forwarding rates because they sometimes have to give up forwarding the current packets after many tries to forward them due to poor channel. If the malicious nodes' forwarding rates become close to those of nodes under a harsh environment, the schemes based on packet forwarding rates for detecting selective forwarding attack may fail because they cannot differentiate the low data packet forwarding rates resulting from the malicious behaviors or harsh environment. To solve this problem, we provide a combined scheme for detecting selective forwarding attack in wireless sensor networks (WSNs) under harsh environments. This scheme employs a data clustering algorithm (DCA) to screen the malicious nodes out by clustering their cumulative forwarding rates (CFRs) and designs a voting decision method to protect the nodes under a harsh environment from being judged as malicious nodes. The simulation results show that our scheme has a low false detection rate (FDR) of 1% and a low missed detection rate (MDR) of 5% respectively with negligible energy consumption in WSNs under a local variable harsh environment.INDEX TERMS event-driven wireless sensor networks, selective forwarding attack, cumulative forwarding rate, data clustering algorithm

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Section: Ai-based Schemesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Employ DBSCAN and Neighbor Voting to Screen Selective Forwarding Attack Under Variable Environment in Event-Driven Wireless Sensor Networks

Liu

2021

IEEE Access

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“…Some malicious attacks can re-transmit RREQ and RREP (e.g. gray hole attacks [14]), however, these types of attacks have a different impact on the network to BH attacks and require different detection methods. The current work focus on BH attacks exclusively.…”

Section: Rrep Construction and Collectionmentioning

confidence: 99%

Collaborative Defense Techniques Against MANETs Malicious Nodes

Terai

Yoshida

Ramonet

et al. 2021

IJNC

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Blackhole (BH) attacks are among the most significant threats in mobile ad-hoc networks. A BH is a security attack in which a malicious node absorbs data packets and sends fake routing information to neighboring nodes. BH attacks have been widely studied. However, existing defense methods wrongfully assume that BH attacks cannot overcome the most common defense approaches. A new wave of BH attacks is known as smart BH attacks. In this study, we used a highly aggressive type of BH attack that can predict sequence numbers to overcome traditional detection methods that set a threshold to sequence numbers. To protect the network from this type of BH attack, we propose a collaborative defense method that uses local information collected from neighboring nodes. We evaluated the performance of our defense method against a smart BH attack and a collaborative attack that uses the collaboration of another malicious node. Our results show that the proposed method successfully detects and contains these threats to some degree. Consequently, the smart BH attack success rate decreases.

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“…The main research contributions of proposed work are as follows: We proposed a prediction model called attention‐based convolution neural network 12 long short‐term memory (attention‐based CNN‐LSTM) model for flow prediction. Attention‐based concept improves the prediction accuracy 13 by learning the significance of preceding traffic flow automatically. This method considers the spatial and temporal characteristics of traffic data with the help of CNN and LSTM. The model is designed with three 1D CNN with leaky rectified linear unit (ReLU) activation function and three LSTM layers with each layer having neurons such as 128, 64, and 64.…”

Section: Introductionmentioning

confidence: 99%

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

Vijayalakshmi¹,

Ramar²,

Jhanjhi

et al. 2020

Int J Communication

Self Cite

101

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Summary In the development of smart cities, the intelligent transportation system (ITS) plays a major role. The dynamic and chaotic nature of the traffic information makes the accurate forecasting of traffic flow as a challengeable one in ITS. The volume of traffic data increases dramatically. We enter the epoch of big data. Hence, a 1deep architecture is necessary to process, analyze, and inference such a large volume of data. To develop a better traffic flow forecasting model, we proposed an attention‐based convolution neural network long short‐term memory (CNN‐LSTM), a multistep prediction model. The proposed scheme uses the spatial and time‐based details of the traffic data, which are extracted using CNN and LSTM networks to improve the model accuracy. The attention‐based model helps to identify the near term traffic details such as speed that is very important for predicting the future value of flow. The results show that our attention‐based CNN‐LSTM prediction model provides better accuracy in terms of prediction during weekdays and weekend days in the case of peak and nonpeak hours also. We used data from the largest traffic data set the California Department of Transportation (Caltrans) for our prediction work.

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Mitigation of Black Hole and Gray Hole Attack Using Swarm Inspired Algorithm With Artificial Neural Network

Cited by 74 publications

References 47 publications

Employ DBSCAN and Neighbor Voting to Screen Selective Forwarding Attack Under Variable Environment in Event-Driven Wireless Sensor Networks

Employ DBSCAN and Neighbor Voting to Screen Selective Forwarding Attack Under Variable Environment in Event-Driven Wireless Sensor Networks

Collaborative Defense Techniques Against MANETs Malicious Nodes

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

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