Identification of Remote IoT Users Using Sensor Data Analytics

Batool, Samera; Saqib, Nazar Abbas; Khattack, Muazzam Khan; Hassan, Ali

doi:10.1007/978-3-030-12388-8_24

Cited by 11 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While the IoT environment includes several devices and objects, it allows these objects to communicate and be interconnected. These connected IoT objects communicate and exchange data via the internet and can be remotely accessible, which puts them at high risk of being targeted by malicious attacks and unauthorized access [ 27 ].…”

Section: Related Workmentioning

confidence: 99%

Ensemble-Learning Framework for Intrusion Detection to Enhance Internet of Things’ Devices Security

Alotaibi

Ilyas

2023

Sensors

View full text Add to dashboard Cite

The Internet of Things (IoT) comprises a network of interconnected nodes constantly communicating, exchanging, and transferring data over various network protocols. Studies have shown that these protocols pose a severe threat (Cyber-attacks) to the security of data transmitted due to their ease of exploitation. In this research, we aim to contribute to the literature by improving the Intrusion Detection System (IDS) detection efficiency. In order to improve the efficiency of the IDS, a binary classification of normal and abnormal IoT traffic is constructed to enhance the IDS performance. Our method employs various supervised ML algorithms and ensemble classifiers. The proposed model was trained on TON-IoT network traffic datasets. Four of the trained ML-supervised models have achieved the highest accurate outcomes; Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbor. These four classifiers are fed to two ensemble approaches: voting and stacking. The ensemble approaches were evaluated using the evaluation metrics and compared for their efficacy on this classification problem. The accuracy of the ensemble classifiers was higher than that of the individual models. This improvement can be attributed to ensemble learning strategies that leverage diverse learning mechanisms with varying capabilities. By combining these strategies, we were able to enhance the reliability of our predictions while reducing the occurrence of classification errors. The experimental results show that the framework can improve the efficiency of the Intrusion Detection System, achieving an accuracy rate of 0.9863.

show abstract

Section: Related Workmentioning

confidence: 99%

Ensemble-Learning Framework for Intrusion Detection to Enhance Internet of Things’ Devices Security

Alotaibi

Ilyas

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…By automating the working environment to decrease human participation and increase system efficiency, the Internet of Things (IoT) is a new technology [16]. As IoT technology continues to advance, new forms of cyberattacks are being developed daily.…”

Section: Literature Reviewmentioning

confidence: 99%

Voting Classifier and Metaheuristic Optimization for Network Intrusion燚etection

Khafaga¹,

Karim²,

Abdelhamid³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Managing physical objects in the network's periphery is made possible by the Internet of Things (IoT), revolutionizing human life. Open attacks and unauthorized access are possible with these IoT devices, which exchange data to enable remote access. These attacks are often detected using intrusion detection methodologies, although these systems' effectiveness and accuracy are subpar. This paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic optimization. The employed metaheuristic optimizer is a new version of the whale optimization algorithm (WOA), which is guided by the dipper throated optimizer (DTO) to improve the exploration process of the traditional WOA optimizer. The proposed voting classifier categorizes the network intrusions robustly and efficiently. To assess the proposed approach, a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack categorization. The dataset records are balanced using the locality-sensitive hashing (LSH) and Synthetic Minority Oversampling Technique (SMOTE). The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach's effectiveness, stability, and significance. The achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

show abstract

“…Physiological characteristics include those retrieved from the human body, such as iris [4], faces [5,6], retinas, veins [7], fingerprints [8][9][10][11], palm prints, finger knuckle print, and DNA, ECG [12][13][14][15], while behavioral characteristics include voice, stride, signature, and keystroke [16][17][18][19][20]. Biometrics traits are commonly employed in systems such as Security in IoT [21][22][23][24][25], e-banking [26][27][28][29], cloud security [30][31][32][33], access control [34][35][36][37], network security systems [38][39][40][41][42], and ID cards [43][44][45][46][47], and other applications related to IoT [48][49][50]…”

Section: Introductionmentioning

confidence: 99%

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

et al. 2022

View full text Add to dashboard Cite

Biometric technology has received a lot of attention in recent years. One of the most prevalent biometric traits is the finger-knuckle print (FKP). Because the dorsal region of the finger is not exposed to surfaces, FKP would be a dependable and trustworthy biometric. We provide an FKP framework that uses the VGG-19 deep learning model to extract deep features from FKP images in this paper. The deep features are collected from the VGG-19 model’s fully connected layer 6 (F6) and fully connected layer 7 (F7). After applying multiple preprocessing steps, such as combining features from different layers and performing dimensionality reduction using principal component analysis (PCA), the extracted deep features are put to the test. The proposed system’s performance is assessed using experiments on the Delhi Finger Knuckle Dataset employing a variety of common classifiers. The best identification result was obtained when the Artificial neural network (ANN) classifier was applied to the principal components of the averaged feature vector of F6 and F7 deep features, with 95% of the data variance preserved. The findings also demonstrate the feasibility of employing these deep features in an FKP recognition system.

show abstract

Identification of Remote IoT Users Using Sensor Data Analytics

Cited by 11 publications

References 18 publications

Ensemble-Learning Framework for Intrusion Detection to Enhance Internet of Things’ Devices Security

Ensemble-Learning Framework for Intrusion Detection to Enhance Internet of Things’ Devices Security

Voting Classifier and Metaheuristic Optimization for Network Intrusion燚etection

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

Contact Info

Product

Resources

About