A hybrid protection approaches for denial of service (DoS) attacks in wireless sensor networks

Gunasekaran, M.; Periakaruppan, Subathra

doi:10.1080/00207217.2017.1279230

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…Based on the trained features, incoming new network feature is analyzed and detected by applying the sigmoid activation function that is computed as follows. (7) Then the final classification is evaluated by applying the inverse operation technique to the output of the hidden layer. From the output, the network status has been identified easily also the neural network is trained by effective method which used to detect the intermediate attack with effective manner.…”

Section: Energy Efficient Extreme Learning Neural Network (Eeelnn) Based Dos Detectionmentioning

confidence: 99%

“…The main legacy of the paper is to eliminate DoS attack from the network by applying the effective technique because the resource and CIA security policy [7] must be managed while making the information broadcast due to the importance of the DoS attack in sensor network. So, in this paper analyze the energy efficiency routing protocol along with the extreme neural network [8] for analyzing the resources of the sensor nodes in sensor networks.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Denial of Service Attack in Wireless Sensor Network using Energy Efficient Extreme Learning Neural Network (EEELNN)

Venkatesh¹,

Asha²

2019

IJRTE

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One of the effective communication technology is wireless sensor network technology which helps to monitor the surrounding information by sensed nodes. The effective utilization of sensed nodes is utilized in different applications such as military, health information, environmental monitoring, disaster relief and target analyze. The application requires the collection of information which may be collected from one location and transferred to the other location for making their process so easier. During the information transformation process, the network may affect by several intermediate attack, in which denial of service is one of the serious attack because it affects the entire network resources such as network energy, power, bandwidth. The unavailability of the resources reduces the entire sensor network performance. For managing the attack related issues, in this paper introduces the Energy Efficient Extreme Learning Neural Network (EEELNN) approach for overcoming the attack related issues. Initially the network transmitted zone is computed along with energy, power, bandwidth, neighboring node information and lifetime for eliminating the attack in sensor network. The computed information is processed and trained by extreme learning neural network that successfully predict the attack related data, node and network zone with effective manner that leads to improve the overall network performance. At last system efficiency is evaluated using simulation results such as detection rate, classification accuracy, false alarm rate and detection time.

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Section: Energy Efficient Extreme Learning Neural Network (Eeelnn) Based Dos Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting Denial of Service Attack in Wireless Sensor Network using Energy Efficient Extreme Learning Neural Network (EEELNN)

Venkatesh¹,

Asha²

2019

IJRTE

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“…Since these sensors are battery‐operated devices, they are vulnerable to Denial of Service (DoS), which is caused by malicious attacks such as vampire attack, black hole attack, directional attack, and selective forwarding attack. The prevention techniques of these attacks are studied in Gunasekaran and Periakaruppan and Osanaiye et al Vampire attacks are not protocoled specific, hence difficult to detect.…”

Section: Introductionmentioning

confidence: 99%

Trust‐based secure routing and the prevention of vampire attack in wireless ad hoc sensor network

Sajan

J²

2020

Int J Communication

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Summary The recent development in technologies is the driving force for development in sensors, especially in military applications. Due to the openness nature of the ad hoc sensor network, the system gets easily affected, which may lead to some packet drop, transmission delay, high network overhead, and more energy consumption. In this paper, the security during data transmission is provided to the network by using the proposed Secure Atom Search Routing (SASR) algorithm, which is adopted from the behavior of molecular dynamics. For global optimization problems, this algorithm provides an effective solution based on the constraint and interaction force of atoms. Moreover, the performance of SASR is improved by providing a proper balance between exploitation and exploration. Since the knowledge base processes all the data, the computational complexity is reduced and the lifetime of the network is increased. The simulation and performance are carried out for the proposed Knowledge and Intrusion Detection based Secure Atom Search Routing (KID‐SASR) protocol and is compared with the existing methods based on the metrics trust, delay, throughput, energy, packet delivery ratio, network lifetime, trust detection rate, and communication cost. The results obtained show improvement in the overall performance of the system.

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“…For the limited resource of sensor nodes, DoS attack has been the main threat and challenge for the security of WSN. There are many kinds of DoS attacks in WSN, such as Jamming, Node destruction, Spoofing, replaying, Denial of sleep, Hello floods, Homing, SYN flood, Reprogramming attacks [1]. In WSN, most routing protocols are simple and efficient, so the routing protocol of WSN is vulnerable to DoS attacks.…”

Section: Introductionmentioning

confidence: 99%

Novel LDoS attack detection by Spark‐assisted correlation analysis approach in wireless sensor network

Chen

Caixia

et al. 2020

IET Information Security

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A hybrid protection approaches for denial of service (DoS) attacks in wireless sensor networks

Cited by 7 publications

References 16 publications

Detecting Denial of Service Attack in Wireless Sensor Network using Energy Efficient Extreme Learning Neural Network (EEELNN)

Detecting Denial of Service Attack in Wireless Sensor Network using Energy Efficient Extreme Learning Neural Network (EEELNN)

Trust‐based secure routing and the prevention of vampire attack in wireless ad hoc sensor network

Novel LDoS attack detection by Spark‐assisted correlation analysis approach in wireless sensor network

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