Intrusion Detection System in Wireless Sensor Networks - A Comprehensive Survey

Farooq, Yumna; Beenish, Hira; Fahad, Muhammad

doi:10.1109/intellect47034.2019.8954984

Cited by 14 publications

(6 citation statements)

References 30 publications

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“…Moreover, some of the most popular attacks that may degrade the performance of WSNs are also discussed in this work, which includes eavesdropping, packet analysis, replay, impersonation, and DoS. Farooq, et al [26] presented a comprehensive survey to investigate the different types of security breaches in WSNs. Here, some of the recent data mining techniques such as NB, LR, DT, SVM, RF and etc have been discussed for developing an IDS.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security

et al. 2023

ijcsm

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Wireless sensor networks (WSNs) are targets of intrusion, which seeks to make these networks less capable of performing their duties or even completely eradicate them. The Intrusion Detection System (IDS) is highly important for WSN, since it aids in the identification and detection of harmful attacks that impair the network's regular functionality. In order to strengthen the security of WSN, several machine learning and deep learning approaches are employed in the traditional works. However, its main drawbacks are computational burden, system complexity, poor network performance outcomes, and high false alarms. Therefore, the goal of this study is to develop an intelligent IDS framework for significantly enhancing WSN security through the use of deep learning model. Here, the min-max normalization and data discretization operations are carried out to produce the preprocessed dataset. Then, an Intelligent Prairie Dog Optimization (IPDO) algorithm is used to reduce the dimensionality of features by identifying the best optimal solution with a higher convergence rate. Moreover, a Deep Auto-Neural Network (DANN) based classification method is used to properly forecast the class of data with less false alarms and higher detection rate. For evaluation, a thorough analysis is conducted to evaluate the performance and detection results of the proposed IPDO-DANN model.

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Section: Related Workmentioning

confidence: 99%

An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security

et al. 2023

ijcsm

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show abstract

“…The wired IDS is not suitable for analyzing WN because most of the ad hoc network device is using a wireless connection point and connected with a WN. To further argue on that some of the problems of detecting intrusions in wireless network are Inconsistency of the strength of signal, difficulty in detecting unofficial user, interference of Radio Frequency, numerous channels, various type of protocols and location of sensors [52].…”

Section: Mobile Agents For Idsmentioning

confidence: 99%

Intrusion Detection Systems in Internet of Things and Mobile Ad-Hoc Networks

Ponnusamy¹,

Humayun²,

Jhanjhi³

et al. 2022

Computer Systems Science and Engineering

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Internet of Things (IoT) devices work mainly in wireless mediums; requiring different Intrusion Detection System (IDS) kind of solutions to leverage 802.11 header information for intrusion detection. Wireless-specific traffic features with high information gain are primarily found in data link layers rather than application layers in wired networks. This survey investigates some of the complexities and challenges in deploying wireless IDS in terms of data collection methods, IDS techniques, IDS placement strategies, and traffic data analysis techniques. This paper's main finding highlights the lack of available network traces for training modern machine-learning models against IoT specific intrusions. Specifically, the Knowledge Discovery in Databases (KDD) Cup dataset is reviewed to highlight the design challenges of wireless intrusion detection based on current data attributes and proposed several guidelines to future-proof following traffic capture methods in the wireless network (WN). The paper starts with a review of various intrusion detection techniques, data collection methods and placement methods. The main goal of this paper is to study the design challenges of deploying intrusion detection system in a wireless environment. Intrusion detection system deployment in a wireless environment is not as straightforward as in the wired network environment due to the architectural complexities. So this paper reviews the traditional wired intrusion detection deployment methods and discusses how these techniques could be adopted into the wireless environment and also highlights the design challenges in the wireless environment. The main wireless environments to look into would be Wireless Sensor Networks (WSN), Mobile Ad Hoc Networks (MANET) and IoT as this are the future trends and a lot of attacks have been targeted into these networks. So it is very crucial to design an IDS specifically to target on the wireless networks.

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“…So, security is a primary issue in WSN for keeping the network safe from breaches. 20 Sensor nodes are prone to malware injection by attackers, and nodes never prevent such fake data due to energy and memory constraints. Detection of fake data in WSN needs efficient methods and algorithms to block the injection of data in nodes.…”

Section: Related Workmentioning

confidence: 99%

Detection of unique delete attack in wireless sensor network using gradient thresholding‐long short‐term memory algorithm

Selvi

Shen

2022

Concurrency and Computation

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Wireless sensor network (WSN) is widely used in many fields, including agriculture, military, healthcare, monitoring, and surveillance. WSN application increases day by day due to growth in embedded and internet technologies. However, data security in WSN is challenging, and different types of attacks are increasing. Traditional intrusion detection methods analyze and identify attacks such as Denial of Service, anomaly detection, wormhole, sybil, and blackhole. Traditional methods never addressed the application layer's novel attack, such as the delete attack. Unique DELETE attack (DA) forms in the application layer and targets web servers and applications. Since unique DA use uniform resource locator requests, distinguishing them from legitimate traffic becomes difficult. The unique DA classify as unique (or) single, multiple, and repeated unique DELETE attacks; for detecting the unique DA types, gradient thresholding-long short-term memory (GT-LSTM) algorithm is proposed using packet per second and traffic rate data in WSN nodes. GT-LSTM algorithm detects unique DA through different thresholding at the training option layer in LSTM. Different gradient thresholding values in LSTM reduce the exploding gradients which fail to detect the unique DA nodes. Gradient thresholding in LSTM layers lies 1 to 3 for detecting unique DELETE attack nodes. The proposed method reduces detection time, improves accuracy, and identifies the hidden node that performs a unique DA in WSN. The unique DELETE Attack identification and performance through the proposed GT-LSTM is analyzed in the NS2 simulation environment and the test bed of WSN. Forms the experimental and simulation results, GT-LSTM performs better than fuzzy, KNN, and linear regression-based intruder detection systems. The proposed algorithm achieves 99% accuracy in detecting the DELETE attack nodes in WSN.

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Intrusion Detection System in Wireless Sensor Networks - A Comprehensive Survey

Cited by 14 publications

References 30 publications

An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security

An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security

Intrusion Detection Systems in Internet of Things and Mobile Ad-Hoc Networks

Detection of unique delete attack in wireless sensor network using gradient thresholding‐long short‐term memory algorithm

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