ML Based Intrusion Detection Scheme for various types of attacks in a WSN using C4.5 and CART classifiers

Gite, Pratik; Chouhan, Kuldeep; Krishna, K. Murali; Nayak, Chinmay Kumar; Soni, Mukesh

doi:10.1016/j.matpr.2021.07.378

Cited by 22 publications

(13 citation statements)

References 35 publications

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“…Meanwhile, Xiangfei et al proved that the C4.5 algorithm could better handle datasets with continuous attributes in this study is an online electrical voltage stability assessment dataset [9]. Gite et al, in their research on machine learning-based intrusion detection for various types of attacks on the Wireless Sensor Network, proved that the C4.5 algorithm produces higher accuracy than CART [10]. A different research topic conducted by Sundaramurthy et al tested the C4.5 algorithm in dealing with the Rheumatoid Arthritis disease dataset with a predicted percentage value of 86.36% [11].…”

Section: Introductionmentioning

confidence: 77%

Improving the Accuracy of C4.5 Algorithm with Chi-Square Method on Pure Tea Classification Using Electronic Nose

Barata¹,

Noersasongko²,

Purwanto³

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Tea is one of the plantation products within the Ministry of Agriculture of the Republic of Indonesia, which plays an essential role as a mainstay commodity that boosts the Indonesian economy. Each type of tea has different properties, and the aroma of each type of tea can measure the quality of the tea. The human sense of smell is still very limited in classifying pure types of tea. Therefore, a device is needed to help measure the aroma of tea from an electronic nose. The devices attached to several gas sensors help humans take data from the smell of pure tea and calculate the value of each type of tea to test datasets with data mining algorithms. This study uses the C4.5 algorithm as a classification method with advantages over noise data, missing values, and handling variables with discrete and continuous types. Meanwhile, Chi-square is used to perform attribute severing in the data preprocessing process to increase the accuracy of dataset testing. Testing a pure tea dataset with four whole attributes, namely CO2, CO, H2, and CH4, using the C4.5 algorithm resulted in an accuracy of 93.65% and an increase in the accuracy performance of the C4.5 algorithm by 94.27% with dataset testing using Chi-Square feature selection with the two highest value attributes.

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Section: Introductionmentioning

confidence: 77%

Improving the Accuracy of C4.5 Algorithm with Chi-Square Method on Pure Tea Classification Using Electronic Nose

Barata¹,

Noersasongko²,

Purwanto³

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

View full text Add to dashboard Cite

show abstract

“…On the other hand, IDPS can be deployed within the WBAN to monitor network traffic, detect suspicious activities, and prevent or mitigate potential attacks [129]- [133]. Intrusion detection techniques, including anomaly detection or signature-based detection, can be used to identify potential security breaches and trigger appropriate responses [134], [135].…”

Section: Figure 4 Wban Security Mechanismsmentioning

confidence: 99%

Current security and privacy posture in wireless body area networks

Auko¹

2023

World J. Adv. Res. Rev.

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Wireless Body Area Networks (WBANs) have emerged as a promising technology for remote health monitoring and healthcare applications. However, ensuring the security and privacy of sensitive health data in WBANs is crucial to foster user trust and prevent unauthorized access or data breaches. This paper provides an overview of the key challenges, techniques, and research gaps in WBAN security and privacy. The findings indicate that the challenges in WBAN security and privacy include resource constraints, compatibility issues, privacy concerns, dynamic network environments, security and usability trade-offs, emerging threat landscape, and user awareness and education. To address these challenges, various security techniques have been developed, such as authentication and authorization mechanisms, encryption, access control, secure communication protocols, intrusion detection systems, and privacy-preserving data handling techniques. Despite the progress made, there are research gaps that require further investigation. These research gaps include the development of secure and lightweight authentication mechanisms, privacy-preserving data analysis techniques, trust and security management frameworks, resilience to insider threats, security of data aggregation and fusion, user-centric security designs, and addressing legal and ethical considerations. Addressing these research gaps and challenges requires collaboration between researchers, device manufacturers, policymakers, and end-users. Ongoing research and innovation are necessary to develop robust security techniques, privacy-enhancing technologies, and user-friendly solutions tailored for WBANs. Additionally, compliance with privacy regulations, user education, and awareness are critical for responsible and ethical use of WBANs.

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“…The research of Ref. [36] presented a supervised-based machine learning algorithm for intrusion detection. It uses decision tree algorithms named C4.5 and CART to identify network patterns.…”

Section: Artificial Immune Systems and Machine Learning-based Systemsmentioning

confidence: 99%

“…Artificial intelligence-and ML-based schemes can be used to solve all the identified state-of-the-art problems [26][27][28][29][30][31][32][33][34][35][36]. The first challenge, as mentioned in Table 7, is neighbourbased discovery schemes.…”

Section: Ai-and Ml-based Schemes As Optimal Solutions To State-of-the...mentioning

confidence: 99%

AI-Based Wormhole Attack Detection Techniques in Wireless Sensor Networks

et al. 2022

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The popularity of wireless sensor networks for establishing different communication systems is increasing daily. A wireless network consists of sensors prone to various security threats. These sensor nodes make a wireless network vulnerable to denial-of-service attacks. One of them is a wormhole attack that uses a low latency link between two malicious sensor nodes and affects the routing paths of the entire network. This attack is brutal as it is resistant to many cryptographic schemes and hard to observe within the network. This paper provides a comprehensive review of the literature on the subject of the detection and mitigation of wormhole attacks in wireless sensor networks. The existing surveys are also explored to find gaps in the literature. Several existing schemes based on different methods are also evaluated critically in terms of throughput, detection rate, low energy consumption, packet delivery ratio, and end-to-end delay. As artificial intelligence and machine learning have massive potential for the efficient management of sensor networks, this paper provides AI- and ML-based schemes as optimal solutions for the identified state-of-the-art problems in wormhole attack detection. As per the author’s knowledge, this is the first in-depth review of AI- and ML-based techniques in wireless sensor networks for wormhole attack detection. Finally, our paper explored the open research challenges for detecting and mitigating wormhole attacks in wireless networks.

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ML Based Intrusion Detection Scheme for various types of attacks in a WSN using C4.5 and CART classifiers

Cited by 22 publications

References 35 publications

Improving the Accuracy of C4.5 Algorithm with Chi-Square Method on Pure Tea Classification Using Electronic Nose

Improving the Accuracy of C4.5 Algorithm with Chi-Square Method on Pure Tea Classification Using Electronic Nose

Current security and privacy posture in wireless body area networks

AI-Based Wormhole Attack Detection Techniques in Wireless Sensor Networks

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