Voting Classifier-Based Intrusion Detection for IoT Networks

Khan, Muhammad Almas; Khan, Muazzam A.; Latif, Shahid; Shah, Awais Aziz; Rehman, Mujeeb Ur; Boulila, ‪Wadii; Driss, Maha; Ahmad, Jawad

doi:10.1007/978-981-16-5559-3_26

Cited by 47 publications

(19 citation statements)

References 26 publications

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“…From table 3, it could be observed that out of the five states-of-the-art IDSs considered in the study none of them reports on the model or the memory consumption of their proposed technique. Although [40,41] both report 100% accuracy, our proposed model outperforms the methods proposed in those studies for the following reasons:…”

Section: Resultsmentioning

confidence: 82%

A Lightweight Intrusion Detection for Internet of Things Using Incremental Ensemble Learning

Agbedanu¹,

Musabe²,

Rwigema³

et al. 2022

Preprint

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Computers have evolved over the years and as the evolution continues, we have been ushered into an era where high-speed internet has made it possible for devices in our homes, hospital, energy and industry to communicate with each other. This era is what is known as the Internet of Things (IoT). IoT has several benefits in the health, energy, transportation and agriculture sectors of a country’s economy. These enormous benefits coupled with the computational constraint of IoT devices which makes it difficult to deploy enhanced security protocols on them make IoT devices a target of cyber-attacks. One approach that has been used in traditional computing over the years to fight cyber-attacks is Intrusion Detection System (IDS). However, it is practically impossible to deploy IDS meant for traditional computers in IoT environments because of the computational constraint of these devices. In this regard, this study proposes a lightweight IDS for IoT devices using an incremental ensemble learning technique. We used Gaussian Naive Bayes and Hoeffding tree to build our incremental ensemble model. The model was then evaluated on the TON IoT dataset. Our proposed model was compared with other state-of-the-art methods proposed and evaluated using the same dataset. The experimental results show that the proposed model achieved an average accuracy of 99.98%. We also evaluated the memory consumption of our model which showed that our model achieved a lightweight model status of 650.11KB as the highest memory consumption and 122.38KB as the lowest memory consumption.

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Section: Resultsmentioning

confidence: 82%

A Lightweight Intrusion Detection for Internet of Things Using Incremental Ensemble Learning

Agbedanu¹,

Musabe²,

Rwigema³

et al. 2022

Preprint

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“…The authors of [24] propose a detection method for DoS/DDoS attacks against the IoT using machine learning. The approach aims to detect and apply the mitigation scenarios in the situation of DoS/DDoS attacks.…”

Section: The State-of-the-artmentioning

confidence: 99%

IoT Multi-Vector Cyberattack Detection Based on Machine Learning Algorithms: Traffic Features Analysis, Experiments, and Efficiency

et al. 2022

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Cybersecurity is a common Internet of Things security challenge. The lack of security in IoT devices has led to a great number of devices being compromised, with threats from both inside and outside the IoT infrastructure. Attacks on the IoT infrastructure result in device hacking, data theft, financial loss, instability, or even physical damage to devices. This requires the development of new approaches to ensure high-security levels in IoT infrastructure. To solve this problem, we propose a new approach for IoT cyberattack detection based on machine learning algorithms. The core of the method involves network traffic analyses that IoT devices generate during communication. The proposed approach deals with the set of network traffic features that may indicate the presence of cyberattacks in the IoT infrastructure and compromised IoT devices. Based on the obtained features for each IoT device, the feature vectors are formed. To conclude the possible attack presence, machine learning algorithms were employed. We assessed the complexity and time of machine learning algorithm implementation considering multi-vector cyberattacks on IoT infrastructure. Experiments were conducted to approve the method’s efficiency. The results demonstrated that the network traffic feature-based approach allows the detection of multi-vector cyberattacks with high efficiency.

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“…For limited dataset, transfer learning is well suited in intrusion detection system. Latif et al [13] introduced an innovative light weight random neural network is used for predicting cyber security attacks such as probing, denial of service, spying and malicious control. The neural network is enriched with the training dataset and detects the unknown patterns of abnormal packets.…”

Section: Related Workmentioning

confidence: 99%

Enriched intuitionistic fuzzy rule pruning using butterfly optimization for rule pruning in intrusion detection

Soundarraj

Ravichandran

2022

ijhs

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The process of detecting and reacting to hostile activities against the computer and network resources is known as intrusion detection. Rather of relying just on expertise and experience, a systematic and automated IDS creation process is required. This drives researchers to look into data mining-based intrusion detection frameworks. Though there are many algorithms are developed for detection intrusion, the issue of inconsistency in rule-based classification model is controlling the volume of data and removing irrelevant rules are the major problems focused in this work to improve the process of IDS. This paper proposed a framework which handles the hesitancy in classifying the unknown traffic pattern data packets by designing evolutionary algorithm optimized intuitionistic fuzzy inference system. The Intuitionistic fuzzy inference system represents each instances of KDD cup 99 dataset with the degree of membership and non-membership. With these two degrees, the hesitancy degree is computed to overcome the issue of inconsistency and uncertainty in analysing the unknown pattern of abnormal packets. In conventional Intuitionistic fuzzy System (IFS), the rules generated by the inference engine is applied for classification directly without determine the validation of it.

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Voting Classifier-Based Intrusion Detection for IoT Networks

Cited by 47 publications

References 26 publications

A Lightweight Intrusion Detection for Internet of Things Using Incremental Ensemble Learning

A Lightweight Intrusion Detection for Internet of Things Using Incremental Ensemble Learning

IoT Multi-Vector Cyberattack Detection Based on Machine Learning Algorithms: Traffic Features Analysis, Experiments, and Efficiency

Enriched intuitionistic fuzzy rule pruning using butterfly optimization for rule pruning in intrusion detection

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