Network Intrusion Detection System using Supervised Learning based Voting Classifier

Sridevi, S.; Prabha, R.; Reddy, K. N.; Monica, K. M.; Senthil, G. A.; Razmah, M

doi:10.1109/ic3iot53935.2022.9767903

Cited by 16 publications

(4 citation statements)

References 31 publications

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“…It ensures the communication protocol to be followed during communication and converts the data into the desired type of protocol in which it needs to be transmitted [20]. The transmitted data is sent to the cloud server where it analyses the data using the deep learning algorithm defined below and sends an alert notification to the forest official regarding the intrusion of the wild animal into the residential area and required action will be taken by the forest officials [21], [22].…”

Section: Figure 2 Data Flow Diagram For Vision Images Using Iot Devicementioning

confidence: 99%

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An IoT Integrated Smart Prediction of Wild Animal Intrusion in Residential Areas Using Hybrid Deep Learning with Computer Vision

G. A.,

Prabha,

Aishwarya

et al. 2024

EAI Endorsed Trans IoT

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INTRODUCTION: The conversion of forests into human lands causes the intrusion of wild animals into the residential area. There is a necessity to prevent the intrusion of such wild animals which causes damage to properties and harm or kill humans. Human population growth leads to an increase in the exploitation of forest areas and related resources for residential and other settlement purposes. There is a need for a system to detect the entry of such animals into habitats. OBJECTIVES: This paper proposes that conversion of forests into human lands causes the intrusion of wild animals into the residential area. METHODS: Deep learning technology combined with Internet of Things (IoT) devices can be deployed in the process of restricting the entry of wild animals into residential areas. The proposed system uses deep learning techniques with the use of various algorithms like DenseNet 201, ResNet50 and You Only Look Once (YOLO). These deep-learning algorithms predict wild animals through image classification. This is done using IoT devices placed in such areas. The role of IoT devices is to transmit the computer vision images to the deep learning module, receive the output, and alarm the residents of the area. RESULTS: The main results are implementation prediction of animals for image processing Datasets used for the prediction and classification indulge the use of cloud modules. It stores the dataset for the prediction process and transfers it whenever needed. As the proposed system is a hybrid model that uses more than one algorithm, the accuracy obtained from the prediction for DenseNet 201, ResNet50 and You Only Look Once (YOLO) algorithm is 82%,92%, and 98%. CONCLUSION: The prediction of those animals is done by a deep learning model which comprises three algorithms are DenseNet 201, ResNet50 and YOLOv3. Comparing the accuracy of an algorithm with higher accuracy is considered efficient and accurate.

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Section: Figure 2 Data Flow Diagram For Vision Images Using Iot Devicementioning

confidence: 99%

“…Web application also shows the previous intrusion data of the wild animal in the particular residential zone. Depending on the past data, the forest department can make preventive measures for the repeated zone of intrusion [23], [24].…”

Section: Cloudmentioning

confidence: 99%

An IoT Integrated Smart Prediction of Wild Animal Intrusion in Residential Areas Using Hybrid Deep Learning with Computer Vision

G. A.,

Prabha,

Aishwarya

et al. 2024

EAI Endorsed Trans IoT

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“…Along with the development of research related to IDS, machine learning is also developing quite rapidly. Research conducted by [8] developed a machine learning based on a voting classifier, which was implemented to detect anomalies in the network. Basically, a voting classifier represents a machine learning method that aggregates predictions from various individual models to produce a final prediction [9].…”

Section: Introductionmentioning

confidence: 99%

“…While soft voting is generally more effective when working with a wide range of models, Hard Voting is easier to execute [16]. Research done in [8] concluded that the performance of machine learning with a voting classifier was better than that of a single classifier model.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Weight-Based Voting Classifier for Intrusion Detection System

2024

IJIES

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The evolution of technology and the internet has accelerated the pace of communication and information exchange. Despite technological advancements, the significant weakness lies in the persistent threat of cybercrime, manifesting in various forms like malware, phishing, and ransomware. To solve the cybercrime problems, this research aims to create an intrusion detection system model using a novel framework. In general, the proposed method consists of 3 stages: Data preprocessing, feature selection using ANOVA F-value combined with cross validation, and classification using weight-based voting classifier. Some machine learning methods used in the weight-based voting classifier are random forest, K-nearest neighbour, and logistic regression. The experiment results show that weight order and weight combination affect the detection performance. The proposed method produces an excellent precision value of 98.66%, higher than the single voting classifier.

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Secure Private Data Storage and File Sharing Using Homomorphic and Hybrid Hash-Based Cryptographic Technique in Cloud

Senthil,

Prabha,

Monica

et al. 2024

Lecture Notes in Networks and Systems

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Network Intrusion Detection System using Supervised Learning based Voting Classifier

Cited by 16 publications

References 31 publications

An IoT Integrated Smart Prediction of Wild Animal Intrusion in Residential Areas Using Hybrid Deep Learning with Computer Vision

An IoT Integrated Smart Prediction of Wild Animal Intrusion in Residential Areas Using Hybrid Deep Learning with Computer Vision

Analysis of Weight-Based Voting Classifier for Intrusion Detection System

Secure Private Data Storage and File Sharing Using Homomorphic and Hybrid Hash-Based Cryptographic Technique in Cloud

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