Detection and prevention of DDOS attack in WSN for AODV and DSR using battery drain

Upadhyay, Raksha; Khan, Salman; Tripathi, Harendra; Bhatt, Uma Rathore

doi:10.1109/coconet.2015.7411224

Cited by 13 publications

(6 citation statements)

References 6 publications

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“…Battery Exhaustion Battery Exhaustion Attacks [99]- [102] IDS frameworks such as MVP-IDS and B-SIPS [101], [103]. IDS-based solutions designed to protect against battery exhaustion [104], [105].…”

Section: E-iot Devices Layer: Attacks and Vulnerabilitiesmentioning

confidence: 99%

Survey on Enterprise Internet-of-Things Systems (E-IoT): A Security Perspective

Rondon¹,

Babun²,

Arış³

et al. 2021

Preprint

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As technology becomes more widely available, millions of users worldwide have installed some form of smart device in their homes or workplaces. These devices are often off-theshelf commodity systems, such as Google Home or Samsung SmartThings, that are installed by end-users looking to automate a small deployment. In contrast to these "plug-and-play" systems, purpose-built Enterprise Internet-of-Things (E-IoT) systems such as Crestron, Control4, RTI, Savant offer a smart solution for more sophisticated applications (e.g., complete lighting control, A/V management, security). In contrast to commodity systems, E-IoT systems are usually closed source, costly, require certified installers, and are overall more robust for their use cases. Due to this, E-IoT systems are often found in expensive smart homes, government and academic conference rooms, yachts, and smart private offices. However, while there has been plenty of research on the topic of commodity systems, no current study exists that provides a complete picture of E-IoT systems, their components, and relevant threats. As such, lack of knowledge of E-IoT system threats, coupled with the cost of E-IoT systems has led many to assume that E-IoT systems are secure. To address this research gap, raise awareness on E-IoT security, and motivate further research, this work emphasizes E-IoT system components, E-IoT vulnerabilities, solutions, and their security implications. In order to systematically analyze the security of E-IoT systems, we divide E-IoT systems into four layers: E-IoT Devices Layer, Communications Layer, Monitoring and Applications Layer, and Business Layer. We survey attacks and defense mechanisms, considering the E-IoT components at each layer and the associated threats. In addition, we present key observations in state-of-the-art E-IoT security and provide a list of open research problems that need further research.

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Section: E-iot Devices Layer: Attacks and Vulnerabilitiesmentioning

confidence: 99%

Survey on Enterprise Internet-of-Things Systems (E-IoT): A Security Perspective

Rondon¹,

Babun²,

Arış³

et al. 2021

Preprint

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“…During network attack, it mainly refers to the influence on the opponent's network , the significance of node subject to intentional attack in the opponent's network and impact on the global situation after being attacked and paralyzed. Hence, attack strategy is determined [4,5].…”

Section: A Establishment Of Index Systemmentioning

confidence: 99%

An Algorithm of Tracking and Controlling Network Attack Node based on Adaptive Neural Networks

Wang

2020

International Journal of Circuits, Systems and Signal Processing

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In order to obtain certain and comprehensive information for formulating network attack strategy, a complex network attack method is proposed in this paper. The attackers’ income, loss, cost and encountered risk in network attack are analyzed and index system is established to evaluate attack effect of network node with dynamic Bayesian network. This method can overcome defects of static evaluation which is relied on single index of network topology. Simulation experiment shows that this method combines more nodes and observation during the attack. It can avoid the gap between actual attack effect and theoretical expectation when attack is implemented by relying on static evaluation. In the meanwhile, it is more accurate in attack precision and of high attack efficiency

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“…This signal is very strong and disrupts the functioning of whole network. By monitoring and finding that frequency the attacker transmit signal on that particular frequency which is very powerful that network can be disrupted by it [5]. In Collision Attack, the attacker will find frequency and then sends data on that same frequency which will occur in collision of packets and data need to be retransmitted again.…”

Section: Fig 1: Wireless Sensor Networkmentioning

confidence: 99%

Node Validation Technique to Increase Security of Wireless Sensor Networks

2019

IJITEE

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A distributed sort of network in which sensor nodes can join or leave the network when they want is identified as wireless sensor network. Due to self-arranging of the network, attacker nodes make their entry inside networks and launch different types of active and passive intrusions. The active attacks can be divided into various sub categories and one of them is misdirection attack. This attack increases delay in the network. The available attacker hub will trigger attack. In order to recognize and disengage malicious nodes a novel strategy is proposed in this work. The malicious nodes are recognized from the networks which are in charge of triggering the node. The attacker or malevolent node launches sinkhole intrusion. This intrusion streams fake recognition information within the network. This study proposes a verification approach for detecting attacker nodes present in the network. The performance of introduced approach is tested in NS2. It is scrutinized that performance is improved as per various parameters.

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Detection and prevention of DDOS attack in WSN for AODV and DSR using battery drain

Cited by 13 publications

References 6 publications

Survey on Enterprise Internet-of-Things Systems (E-IoT): A Security Perspective

Survey on Enterprise Internet-of-Things Systems (E-IoT): A Security Perspective

An Algorithm of Tracking and Controlling Network Attack Node based on Adaptive Neural Networks

Node Validation Technique to Increase Security of Wireless Sensor Networks

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