Intrusion Detection in Wireless Sensor Networks: Issues, Challenges and Approaches

Ghosal, Amrita; Halder, Subir

doi:10.1007/978-3-642-36169-2_10

Cited by 16 publications

(8 citation statements)

References 53 publications

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“…If the node found to be malicious, then it is simply removed from network. Based on this methodology, so many authors tried to develop an efficient intrusion detection mechanism to prevent the network from several attacks [12].…”

Section: Literature Surveymentioning

confidence: 99%

Multi-Strategic Trust Evaluation for Intrusion Detection in Wireless Sensor Networks

Srividya¹,

Devi²

2021

IJIES

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Wireless Sensor Networks (WSNs) have gained a huge research interest due to their nature of deployment and widespread applicability in several applications. However, the distributed deployment nature of WSNs poses several security challenges. In this paper, an Intrusion (Outlier) detection system is proposed based on multi-strategic trust metrics, which is flexible for constantly varying WSN characterized by changes in the variations in direct trust value, recommendations and capacities. Under the multiple strategic trust metrics, this approach considered three trust metrics; they are subjective trust, witness trust and Capacity trust. Furthermore, the capacity trust is modeled as the combination of fault tolerance trust and stability trust. Each node in the network measures the overall trust of its neighbor nodes based on these three metrics and decides whether it is malicious or not. Similarly, among the available routes, one route is finalized which has more Route Trust. Extensive Simulations are conducted over the proposed intrusion detection mechanism and the performance is evaluated through Malicious Detection Rate, False Positive Rate and Packet Delivery Ratio. From the results, we have noticed that the average Malicious Detection Rats of proposed model is 93.4012% while the False Positive rate and Packet Delivery Ratio are observed as 5.5000% and 74.1745% respectively. The obtained metrics indicate that the proposed method shows an outstanding performance in Intrusions detection and packet deliveries.

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Section: Literature Surveymentioning

confidence: 99%

Multi-Strategic Trust Evaluation for Intrusion Detection in Wireless Sensor Networks

Srividya¹,

Devi²

2021

IJIES

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“…4) A console is used for many following functions where it provides connection between users and administrators to check the updated data of the system monitored, collect alert messages, inspect events and constitute the system. Figure .1 Basic IDS block diagram [1] Intrusion Detection Systems Ashwini V. Jatti, V. J. K. Kishor Sonti Based on six criteria IDS techniques are classified as follows:…”

Section: Intrusion Detection Systemmentioning

confidence: 99%

Intrusion Detection Systems

2019

IJRTE

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Intrusion Detection System is competent to detect the intrusions and alerting the administrator of system about the signs of possible intrusions. This paper presents a detailed review of the intrusion detection techniques used in WSNs. More specifically, the existing methods for blackhole and sinkhole attacks detection are reviewed. However, it is noted that most intrusion detection schemes proposed in the literature are either inefficient or have low detection rates/high false positive rates. This survey also highlights the research gap in this domain and provides better scope for the advanced work.

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“…Nowadays wireless sensors network (WSN) [1] is a standout amongst the most rising and quickly developing fields in the world [2][3]. At first, it was designed to accelerate and facilitate military operations but later its application has been extended to health, traffic, industrial areas and threat detection [4].…”

Section: Introductionmentioning

confidence: 99%

Data mining approach to analyzing intrusion detection of wireless sensor network

Rezvi

Moontaha

Trisha

et al. 2021

IJEECS

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<span>Wireless sensor network (WSN) is a collection of wireless sensor nodes which are distributed in nature and a base station where the dispersed nodes are used to monitor and the physical conditions of the environment is recorded and then these data are organized into the base. Its application has been reached out from critical military application such as battlefield surveillance to traffic, health, industrial areas, intruder detection, security and surveillance. Due to various features in WSN it is very prone to various types external attacks. Preventing such attacks, intrusion detection system (IDS) is very important so that attacker cannot steal or manipulate data. Data mining is a technique that can help to discover patterns in large dataset. This paper proposed a data mining technique for different types of classification algorithms to detect denial of service (DoS) attacks which is of four types. They are Grayhole, Blackhole, Flooding and TDMA. A number of data mining techniques, such as KNN, Naïve Bayes, Logistic Regression, support vector machine (SVM) and ANN algorithms are applied on the dataset and analyze their performance in detecting the attacks. The analysis reveals the applicability of these algorithms for detecting and predicting such attacks and can be recommended for network specialist and analysts. </span>

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Intrusion Detection in Wireless Sensor Networks: Issues, Challenges and Approaches

Cited by 16 publications

References 53 publications

Multi-Strategic Trust Evaluation for Intrusion Detection in Wireless Sensor Networks

Multi-Strategic Trust Evaluation for Intrusion Detection in Wireless Sensor Networks

Intrusion Detection Systems

Data mining approach to analyzing intrusion detection of wireless sensor network

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