A Study on Intrusion Detection System in Wireless Sensor Networks

Godala, Sravanthi; Vaddella, Rama Prasad V.

doi:10.17762/ijcnis.v12i1.4429

Cited by 20 publications

(10 citation statements)

References 56 publications

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“…The RTT (round trip time) is calculated by using Equation ( 6). RTT = t 2 _i−t 1 (6) where t 1 is the route request time by the sender and t 2 _i is the route reply time by the receiver.…”

Section: Mathematical Model For Mids Algorithmmentioning

confidence: 99%

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Secure Cooperative Routing in Wireless Sensor Networks

Batool,

Bibi,

Alhazmi

et al. 2024

Applied Sciences

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In wireless sensor networks (WSNs), sensor nodes are randomly distributed to transmit sensed data packets to the base station periodically. These sensor nodes, because of constrained battery power and storage space, cannot utilize conventional security measures. The widely held challenging issues for the network layer of WSNs are the packet-dropping attacks, mainly sinkhole and wormhole attacks, which focus on the routing pattern of the protocol. This thesis presents an improved version of the second level of the guard to the system, intrusion detection systems (IDSs), to limit the hostile impact of these attacks in a Low Energy Adaptive Clustering Hierarchy (LEACH) environment. The proposed system named multipath intrusion detection system (MIDS) integrates an IDs with ad hoc on-demand Multipath Distance Vector (AOMDV) protocol. The IDS agent uses the number of packets transmitted and received to calculate intrusion ratio (IR), which helps to mitigate sinkhole attacks and from AOMDV protocol round trip time (RTT) is computed by taking the difference between route request and route reply time to mitigate wormhole attack. MATLAB simulation results show that this cooperative model is an effective technique due to the higher packet delivery ratio (PDR), throughput, and detection accuracy. The proposed MIDS algorithm is proven to be more efficient when compared with an existing LEACH-based IDS system and MS-LEACH in terms of overall energy consumption, lifetime, and throughput of the network.

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Section: Mathematical Model For Mids Algorithmmentioning

confidence: 99%

“…The security issues become more critical when the WSNs are deployed in a remote or threatening environment where sensors are vulnerable to failures [3,4]. This paper focuses on the security issues in the network layer, specifically on routing attacks that compromise data integrity, confidentiality, and network availability [5,6].…”

Section: Introductionmentioning

confidence: 99%

Secure Cooperative Routing in Wireless Sensor Networks

Batool,

Bibi,

Alhazmi

et al. 2024

Applied Sciences

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“…Additionally presented are possible lines of inquiry for further research on IDS in WSN. In WSN, the IDS are not presented in a straightforward manner [15].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning model-based malicious node detection system in wireless multimedia sensor Network

Jayadhas

Roslin

Wilfred

2023

Preprint

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Wireless sensor networks (WMSNs) are becoming increasingly popular in many fields, from academia to transportation, environmental monitoring, wildlife preservation, and military espionage. Therefore, examining potential threats, power consumption, vulnerability recognition, and systemic vulnerability characteristics is essential to develop a reliable information security approach for WSNs. As a result, it is becoming increasingly crucial for the technical community to conduct intrusion recognition method evaluations. Since this is the case, using deep learning techniques in creating intrusion identification and mitigation systems for wireless multimedia sensor networks is essential. This article examines how well different machine learning and deep learning algorithms perform in attack identification systems. Testing the efficacy of different methods on the WMSN-DS database through experimentation is essential. In this work, we combine the power of a Convolutional Neural Network classifier with a Random forest. In order to accomplish this, a Convolutional Neural Network with a Random Forest Classifier is used. The intrusion detection system (IDS) is a crucial technique proposed in this study for WMSN. To address this issue, the current study proposal uses deep Learning with a Random Forest classifier to detect and prevent attacks and to promote efficient forwarding in WMSNs. Multiple WMSN assaults have been investigated, and the results of these investigations have been critically evaluated.

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“…The performance of an IDS is to have high accuracy, high detection rate and with lower false alarm rate [33].We employed these metrics as performance measure in this paper with respect to the confusion matrix [42].…”

Section: Table 3 Confusion Matrixmentioning

confidence: 99%

Feature Selection with IG-R for Improving Performance of Intrusion Detection System

Saheed¹,

Usman-Hamza²

2022

Int. j. commun. netw. inf. secur.

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As the popularity of the internet computer continued to grow and become an indispensable in human life, the security of computer network has become an important issue in computer security field. The Intrusion Detection System (IDS) is a system used in computer security for network security. The feature selection stage of IDS is considered to be the most critical stage in IDS. This stage is very costly both in efforts and time. However, many machine learning approaches have been presented to improve this stage in order to improve the performance of an IDS. However, these approaches did not give desirable results with respect to the detection accuracy in the IDS. A novel technique is proposed in this paper combining the Information Gain and Ranker (IG+R) method as the feature selection strategy with Naïve Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) as the classifiers. The performance of these IG+R-NB, IG+R-SVM, and IG+R-KNN was evaluated on NSLKDD dataset. The experimental results of our proposed method gave high accuracy and low false alarm rate. The results obtained was compared and benchmarked with existing works. The results of this paper outperformed the existing approaches in terms of the detection accuracy.

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A Study on Intrusion Detection System in Wireless Sensor Networks

Cited by 20 publications

References 56 publications

Secure Cooperative Routing in Wireless Sensor Networks

Secure Cooperative Routing in Wireless Sensor Networks

Deep Learning model-based malicious node detection system in wireless multimedia sensor Network

Feature Selection with IG-R for Improving Performance of Intrusion Detection System

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