A comparative performance evaluation of intrusion detection techniques for hierarchical wireless sensor networks

Soliman, Hassan; Hikal, Noha A.; Sakr, Nehal A.

doi:10.1016/j.eij.2012.10.003

Cited by 31 publications

(10 citation statements)

References 17 publications

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“…Machine learning techniques are widely adopted in the existing reputation mechanism. Among them, the most popular intrusion detection schemes are based on Fuzzy -Means clustering, Backpropagation Neural Network, Self-Organizing Maps, Wavelets, Agglomerative Clustering, and Bayesian classifier [34]. In particular, Self-Organizing Maps (SOM) emerge as a technique suitable for constrained and unattended environments, like WSN, in which the node behavior may be not predictable a priori.…”

Section: Discussionmentioning

confidence: 99%

“…The focus, with GoNe, is on the last approach, due to the potentialities of learning algorithms and neural networks in recognizing in an inexpensive and fast way the misbehavior happening within the WSN. Note that such a feature also concerns large-scale environments, as demonstrated in [34], which shows a performance comparison among different IDS techniques. It reveals that the approaches based on neural networks outperform the others.…”

Section: Malicious Node Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Performance Comparison of Reputation Assessment Techniques Based on Self-Organizing Maps in Wireless Sensor Networks

Sicari

Grieco

Coen‐Porisini

2017

Wireless Communications and Mobile Computing

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Many solutions based on machine learning techniques have been proposed in literature aimed at detecting and promptly counteracting various kinds of malicious attack (data violation, clone, sybil, neglect, greed, and DoS attacks), which frequently affect Wireless Sensor Networks (WSNs). Besides recognizing the corrupted or violated information, also the attackers should be identified, in order to activate the proper countermeasures for preserving network's resources and to mitigate their malicious effects. To this end, techniques adopting Self-Organizing Maps (SOM) for intrusion detection in WSN were revealed to represent a valuable and effective solution to the problem. In this paper, the mechanism, namely, Good Network (GoNe), which is based on SOM and is able to assess the reliability of the sensor nodes, is compared with another relevant and similar work existing in literature. Extensive performance simulations, in terms of nodes' classification, attacks' identification, data accuracy, energy consumption, and signalling overhead, have been carried out in order to demonstrate the better feasibility and efficiency of the proposed solution in WSN field.

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Section: Discussionmentioning

confidence: 99%

Section: Malicious Node Detectionmentioning

confidence: 99%

Performance Comparison of Reputation Assessment Techniques Based on Self-Organizing Maps in Wireless Sensor Networks

Sicari

Grieco

Coen‐Porisini

2017

Wireless Communications and Mobile Computing

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“…When the ID system is switched either detection or prevention mode from learning mode, it begins to compare the usual traffic with the initially created profile, and any detected anomalous activity deviating from the base line profile will trigger an alarm cautioning the administrator to possible intrusion or otherwise prevent it if set to prevention mode. With specific traffic behaviors, user defined profiles can also be generated, for example the amount of e-mails published by a user and user access attempts [7] Examples are Snort, and BroIDS are detection system based on anomaly for ID systems [6].…”

Section: ) Based On Known or Unknown Attack Patterns I-anomaly Based Intrusion Detection Systemmentioning

confidence: 99%

A Review on Network Intrusion Detection System Using Machine Learning

Kagara

Siraj

2020

Int J Innov Comp

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The quality or state of being secure is the crucial concern of our daily life usage of any network. However, with the rapid breakthrough in network technology, attacks are becoming more trailblazing than defenses. It is a daunting task to design an effective and reliable intrusion detection system (IDS), while maintaining minimal complexity. The concept of machine learning is considered an important method used in intrusion detection systems to detect irregular network traffic activities. The use of machine learning is the current trend in developing IDS in order to mitigate false positives (FP) and False Negatives (FN) in the anomalous IDS. This paper targets to present a holistic approach to intrusion detection system and the popular machine learning techniques applied on IDS systems, bearing In mind the need to help research scholars in this continuous burgeoning field of Intrusion detection (ID).

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“…Different types of attacks was examined for detecting and rectification of the outliers. Soliman et al [11] compare and asses the most commonly used outlier based intrusion detecting system in hierarchical WSNs and also determined some of the strengthens and weaknesses of each technique. Rassam et al [12] have presented data reduction by transforming the raw data to another space in WSNs using an adaptive and efficient Principle Component Analysis (PCA).…”

Section: Related Workmentioning

confidence: 99%

High Rate Outlier Detection in Wireless Sensor Networks: A Comparative Study

Shia¹,

Tawfeeq²,

Mahmoud³

2019

IJMECS

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The rapid development of Smart Cities and the Internet of Thinks (IoT) is largely dependent on data obtained through Wireless Sensor Networks (WSNs). The quality of data gathered from sensor nodes is influenced by abnormalities that happen due to different reasons including, malicious attacks, sensor malfunction or noise related to communication channel. Accordingly, outlier detection is an essential procedure to ensure the quality of data derived from WSNs. In the modern utilizations of WSNs, especially in online applications, the high detection rate for abnormal data is closely correlated with the time required to detect these data. This work presents an investigation of different outlier detection techniques and compares their performance in terms of accuracy, true positive rate, false positive rate, and the required detection time. The investigated algorithms include Particle Swarm Optimization (PSO), Deferential Evolution (DE), One Class Support Vector Machine (OCSVM), K-means clustering, combination of Contourlet Transform and OCSVM (CT-OCSVM), and combination of Discrete Wavelet Transform and OCSVM (DWT-OCSVM). Real datasets gathered from a WSN configured in a local lab are used for testing the techniques. Different types and values of outliers have been imposed in these datasets to accommodate the comparison requirements. The results show that there are some differences in the accuracy, detection rate, and false positive rate of the outlier detections, except K-means clustering which failed to detect outlier in some cases. The required detection time for both PSO and DE is very long as compared with the other techniques meanwhile, the CT-OCSVM and DWT-OCSVM required short time and also they can achieve high performance. On the other hand CT and DWT technique has the ability to compress its used dataset where in this paper, CT can extract much less number of coefficients as compared DWT. This makes CT-OCSVM more efficient to be utilized in detecting outliers in WSNS.

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A comparative performance evaluation of intrusion detection techniques for hierarchical wireless sensor networks

Cited by 31 publications

References 17 publications

Performance Comparison of Reputation Assessment Techniques Based on Self-Organizing Maps in Wireless Sensor Networks

Performance Comparison of Reputation Assessment Techniques Based on Self-Organizing Maps in Wireless Sensor Networks

A Review on Network Intrusion Detection System Using Machine Learning

High Rate Outlier Detection in Wireless Sensor Networks: A Comparative Study

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