A novel density estimation based intrusion detection technique with Pearson’s divergence for Wireless Sensor Networks

Gavel, Shashank; Raghuvanshi, Ajay Singh; Tiwari, Sudarshan

doi:10.1016/j.isatra.2020.11.016

Cited by 29 publications

(14 citation statements)

References 33 publications

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“…Next, it is placed on the cloud and utilizes RF multiclass classifiers for an indepth analysis of the investigated packet. Gavel et al [22] proposed a method which employs the integration of multivarying kernel density estimate using distributed computing. This integration analyses the single possibility of the survival of data and calculates the global value of the PDF.…”

Section: Related Workmentioning

confidence: 99%

Robust Node Localization with Intrusion Detection for Wireless Sensor Networks

Punithavathi¹,

Selvi²,

Latha³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Wireless sensor networks comprise a set of autonomous sensor nodes, commonly used for data gathering and tracking applications. Node localization and intrusion detection are considered as the major design issue in WSN. Therefore, this paper presents a new multi-objective manta ray foraging optimization (MRFO) based node localization with intrusion detection (MOMRFO-NLID) technique for WSN. The goal of the MOMRFO-NLID technique is to optimally localize the unknown nodes and determine the existence of intrusions in the network. The MOMRFO-NLID technique encompasses two major stages namely MRFO based localization of nodes and optimal Siamese Neural Network (OSNN) based intrusion detection. The OSNN technique involves the hyperparameter tuning of the traditional SNN using the MRFO algorithm and consequently increases the detection rate. In order to assess the enhanced performance of the MOMRFO-NLID technique, a series of simulations take place and the results reported superior performance compared to existing techniques interms of distinct evaluation parameters.

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Section: Related Workmentioning

confidence: 99%

Robust Node Localization with Intrusion Detection for Wireless Sensor Networks

Punithavathi¹,

Selvi²,

Latha³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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“…Finally the third part is used to verify the legitimacy of the messages before sending to the head nod in the witness ring. Novel intrusion detection technique [27] with PD(Pearson's Divergence) is used to detect the intrusion that usually compromises the node. The compromised node exists for a long time in the network so that it can affect and collapse the system.…”

Section: Related Workmentioning

confidence: 99%

FBDR-Fuzzy Based DDoS Attack Detection and Recovery Mechanism for Wireless Sensor Networks

Pajila¹,

Julie

Robinson

2021

Wireless Pers Commun

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Wireless sensor networks (WSN) is considering as one of the exploring technology.WSN has a large number of sensor nodes, which sense the environment and collect the data. The collected data are sending to the sink through the intermediate nodes. Since the sensors node data are exposed to the internet, there is a possibility of vulnerability in the WSN. The common attack that affects most of the sensor nodes is the DDoS attack. In this paper aims to identify the DDoS attack quickly and to recover sensors using the fuzzy logic mechanism. In the Fuzzy based DDoS attack Detection and Recovery mechanism (FBDR) method uses type 1 fuzzy-logic to detect the occurrence of DDoS attack in a node. Similarly fuzzy-type 2 is used for recovery DDoS attack.Both the type 1 fuzzy-based rule and type 2 fuzzy-based rule perform well in terms of identifying the DDoS attack and recover the DDoS attack. It also helps to reduce the energy consumption of each node and improves the lifetime of the network. The proposed FBDR scheme is compared with other related schemes. The experimental results represent that the FBDR method works better than other similar schemes.

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“…It contains the latest and updated pattern of both intrusive and normal data similar to the current wireless network scenario. 5 This is the latest dataset that contains many features related to the wireless network with a large number of data records.…”

Section: Description Of Datasetmentioning

confidence: 99%

“…These approaches oppose developing an efficient anomaly-based intrusion detection scheme (AIDS). [1][2][3][4][5] Various reasons include the high value of FAR (false alarm rate), unavailability of reliable data, and longevity of utilized data for training. These demerits can lead any network toward a situation of inaccurate and inefficient detection of a fault.…”

Section: Introductionmentioning

confidence: 99%

Feature reduction scheme for anomaly‐based intrusion detection in wireless networks: Building of hybrid model

Gavel

Singh

Shukla

et al. 2021

Trans Emerging Tel Tech

Self Cite

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Anomaly-based intrusion detection plays a crucial and essential role in providing security to computer networks. However, some concerns still exist for the sustainability and feasibility of existing approaches over modern networks. Specifically, these concerns relate to increasing many features from a network, thus making it difficult for a user to achieve high detection accuracy. This article presents a novel and efficient feature reduction scheme for fault-based intrusion detection by efficiently handling the large feature from a network. Monarch butterfly optimization is used with a new correlation-based fitness function to reduce features. Twin support vector machine is utilized with its unique classification in the proposed scheme. The proposed method is analyzed and evaluated with NSL-KDD, CICIDS2017, AWID network security, and standard fault network datasets. The obtained results are verified using standard performance measures, that is, overall detection accuracy, F-measure, FPR, and AUC score with time complexity. The results show the superiority and stability of the proposed scheme over the existing techniques.

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A novel density estimation based intrusion detection technique with Pearson’s divergence for Wireless Sensor Networks

Cited by 29 publications

References 33 publications

Robust Node Localization with Intrusion Detection for Wireless Sensor Networks

Robust Node Localization with Intrusion Detection for Wireless Sensor Networks

FBDR-Fuzzy Based DDoS Attack Detection and Recovery Mechanism for Wireless Sensor Networks

Feature reduction scheme for anomaly‐based intrusion detection in wireless networks: Building of hybrid model

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