scholarly journals An Anomaly Mitigation Framework for IoT Using Fog Computing

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1565
Author(s):  
Muhammad Aminu Lawal ◽  
Riaz Ahmed Shaikh ◽  
Syed Raheel Hassan
Keyword(s):  
Smart Cities ◽  
Fog Computing ◽  
Multiclass Classification ◽  
Attack Detection ◽  
Gradient Boosting ◽  
Ip Address ◽  
Computing Paradigm ◽  
Security Challenges ◽  
Extreme Gradient Boosting ◽  
Boosting Algorithm

The advancement in IoT has prompted its application in areas such as smart homes, smart cities, etc., and this has aided its exponential growth. However, alongside this development, IoT networks are experiencing a rise in security challenges such as botnet attacks, which often appear as network anomalies. Similarly, providing security solutions has been challenging due to the low resources that characterize the devices in IoT networks. To overcome these challenges, the fog computing paradigm has provided an enabling environment that offers additional resources for deploying security solutions such as anomaly mitigation schemes. In this paper, we propose a hybrid anomaly mitigation framework for IoT using fog computing to ensure faster and accurate anomaly detection. The framework employs signature- and anomaly-based detection methodologies for its two modules, respectively. The signature-based module utilizes a database of attack sources (blacklisted IP addresses) to ensure faster detection when attacks are executed from the blacklisted IP address, while the anomaly-based module uses an extreme gradient boosting algorithm for accurate classification of network traffic flow into normal or abnormal. We evaluated the performance of both modules using an IoT-based dataset in terms response time for the signature-based module and accuracy in binary and multiclass classification for the anomaly-based module. The results show that the signature-based module achieves a fast attack detection of at least six times faster than the anomaly-based module in each number of instances evaluated. The anomaly-based module using the XGBoost classifier detects attacks with an accuracy of 99% and at least 97% for average recall, average precision, and average F1 score for binary and multiclass classification. Additionally, it recorded 0.05 in terms of false-positive rates.

An intelligent evolutionary extreme gradient boosting algorithm development for modeling scour depths under submerged weir

2021 ◽  
Author(s):  
Hai Tao ◽  
Maria Habib ◽  
Ibrahim Aljarah ◽  
Hossam Faris ◽  
Haitham Abdulmohsin Afan ◽  
...  
Keyword(s):  
Gradient Boosting ◽  
Algorithm Development ◽  
Extreme Gradient Boosting ◽  
Boosting Algorithm

scholarly journals T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm

2020 ◽  
Vol 11 ◽  
Author(s):  
Tianhang Chen ◽  
Xiangeng Wang ◽  
Yanyi Chu ◽  
Yanjing Wang ◽  
Mingming Jiang ◽  
...  
Keyword(s):  
Gradient Boosting ◽  
Type Iv ◽  
Extreme Gradient Boosting ◽  
Boosting Algorithm

scholarly journals Property Rental Price Prediction Using the Extreme Gradient Boosting Algorithm

2020 ◽  
Vol 3 (2) ◽  
pp. 54-59
Author(s):  
Marco Febriadi Kokasih ◽  
Adi Suryaputra Paramita
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Gradient Boosting ◽  
Fair Price ◽  
Price Prediction ◽  
Online Marketplace ◽  
Extreme Gradient Boosting ◽  
Property Owners ◽  
Boosting Algorithm

Online marketplace in the field of property renting like Airbnb is growing. Many property owners have begun renting out their properties to fulfil this demand. Determining a fair price for both property owners and tourists is a challenge. Therefore, this study aims to create a software that can create a prediction model for property rent price. Variable that will be used for this study is listing feature, neighbourhood, review, date and host information. Prediction model is created based on the dataset given by the user and processed with Extreme Gradient Boosting algorithm which then will be stored in the system. The result of this study is expected to create prediction models for property rent price for property owners and tourists consideration when considering to rent a property. In conclusion, Extreme Gradient Boosting algorithm is able to create property rental price prediction with the average of RMSE of 10.86 or 13.30%.

scholarly journals Extreme gradient boosting regression model for soil thermal conductivity

2021 ◽  
Vol 25 (Spec. issue 1) ◽  
pp. 1-7
Author(s):  
Ahmet Yurttakal
Keyword(s):  
Thermal Conductivity ◽  
Soil Moisture ◽  
Regression Model ◽  
Test Data ◽  
Granular Structure ◽  
Gradient Boosting ◽  
Soil Thermal Conductivity ◽  
Extreme Gradient Boosting ◽  
Complicated Process ◽  
Boosting Algorithm

The thermal conductivity estimation for the soil is an important step for many geothermal applications. But it is a difficult and complicated process since it involves a variety of factors that have significant effects on the thermal conductivity of soils such as soil moisture and granular structure. In this study, regression was performed with the extreme gradient boosting algorithm to develop a model for estimating thermal conductivity value. The performance of the model was measured on the unseen test data. As a result, the proposed algorithm reached 0.18 RMSE, 0.99 R2, and 3.18% MAE values which state that the algorithm is encouraging.

scholarly journals Development of an IoT Architecture Based on a Deep Neural Network against Cyber Attacks for Automated Guided Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8467
Author(s):  
Mahmoud Elsisi ◽  
Minh-Quang Tran
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Online Monitoring ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
Automated Guided Vehicles ◽  
Gradient Boosting ◽  
Machine Model ◽  
Extreme Gradient Boosting

This paper introduces an integrated IoT architecture to handle the problem of cyber attacks based on a developed deep neural network (DNN) with a rectified linear unit in order to provide reliable and secure online monitoring for automated guided vehicles (AGVs). The developed IoT architecture based on a DNN introduces a new approach for the online monitoring of AGVs against cyber attacks with a cheap and easy implementation instead of the traditional cyber attack detection schemes in the literature. The proposed DNN is trained based on experimental AGV data that represent the real state of the AGV and different types of cyber attacks including a random attack, ramp attack, pulse attack, and sinusoidal attack that is injected by the attacker into the internet network. The proposed DNN is compared with different deep learning and machine learning algorithms such as a one dimension convolutional neural network (1D-CNN), a supported vector machine model (SVM), random forest, extreme gradient boosting (XGBoost), and a decision tree for greater validation. Furthermore, the proposed IoT architecture based on a DNN can provide an effective detection for the AGV status with an excellent accuracy of 96.77% that is significantly greater than the accuracy based on the traditional schemes. The AGV status based on the proposed IoT architecture with a DNN is visualized by an advanced IoT platform named CONTACT Elements for IoT. Different test scenarios with a practical setup of an AGV with IoT are carried out to emphasize the performance of the suggested IoT architecture based on a DNN. The results approve the usefulness of the proposed IoT to provide effective cybersecurity for data visualization and tracking of the AGV status that enhances decision-making and improves industrial productivity.

scholarly journals IoT Botnet Attack Detection Based on Optimized Extreme Gradient Boosting and Feature Selection

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6336 ◽  
Author(s):  
Mnahi Alqahtani ◽  
Hassan Mathkour ◽  
Mohamed Maher Ben Ismail
Keyword(s):  
Feature Selection ◽  
Large Scale ◽  
Feature Selection Method ◽  
Selection Method ◽  
Attack Detection ◽  
Gradient Boosting ◽  
Fisher Score ◽  
Detection Approach ◽  
Extreme Gradient Boosting ◽  
Iot Devices

Nowadays, Internet of Things (IoT) technology has various network applications and has attracted the interest of many research and industrial communities. Particularly, the number of vulnerable or unprotected IoT devices has drastically increased, along with the amount of suspicious activity, such as IoT botnet and large-scale cyber-attacks. In order to address this security issue, researchers have deployed machine and deep learning methods to detect attacks targeting compromised IoT devices. Despite these efforts, developing an efficient and effective attack detection approach for resource-constrained IoT devices remains a challenging task for the security research community. In this paper, we propose an efficient and effective IoT botnet attack detection approach. The proposed approach relies on a Fisher-score-based feature selection method along with a genetic-based extreme gradient boosting (GXGBoost) model in order to determine the most relevant features and to detect IoT botnet attacks. The Fisher score is a representative filter-based feature selection method used to determine significant features and discard irrelevant features through the minimization of intra-class distance and the maximization of inter-class distance. On the other hand, GXGBoost is an optimal and effective model, used to classify the IoT botnet attacks. Several experiments were conducted on a public botnet dataset of IoT devices. The evaluation results obtained using holdout and 10-fold cross-validation techniques showed that the proposed approach had a high detection rate using only three out of the 115 data traffic features and improved the overall performance of the IoT botnet attack detection process.

scholarly journals Fog Computing: An Overview of Big IoT Data Analytics

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Muhammad Rizwan Anawar ◽  
Shangguang Wang ◽  
Muhammad Azam Zia ◽  
Ahmer Khan Jadoon ◽  
Umair Akram ◽  
...  
Keyword(s):  
Data Analytics ◽  
Smart Cities ◽  
Fog Computing ◽  
Computing Paradigm ◽  
Key Characteristics ◽  
Challenges And Opportunities ◽  
Cloud Framework ◽  
Iot Devices ◽  
Health Care Monitoring ◽  
Computing Structure

A huge amount of data, generated by Internet of Things (IoT), is growing up exponentially based on nonstop operational states. Those IoT devices are generating an avalanche of information that is disruptive for predictable data processing and analytics functionality, which is perfectly handled by the cloud before explosion growth of IoT. Fog computing structure confronts those disruptions, with powerful complement functionality of cloud framework, based on deployment of micro clouds (fog nodes) at proximity edge of data sources. Particularly big IoT data analytics by fog computing structure is on emerging phase and requires extensive research to produce more proficient knowledge and smart decisions. This survey summarizes the fog challenges and opportunities in the context of big IoT data analytics on fog networking. In addition, it emphasizes that the key characteristics in some proposed research works make the fog computing a suitable platform for new proliferating IoT devices, services, and applications. Most significant fog applications (e.g., health care monitoring, smart cities, connected vehicles, and smart grid) will be discussed here to create a well-organized green computing paradigm to support the next generation of IoT applications.

An Extension of the MiSCi Middleware for Smart Cities Based on Fog Computing

Fog Computing ◽  
2018 ◽  
pp. 230-250
Author(s):  
Jose Aguilar ◽  
Manuel B. Sanchez ◽  
Marxjhony Jerez ◽  
Maribel Mendonca
Keyword(s):  
Embedded Systems ◽  
Intelligent Agents ◽  
Smart City ◽  
Smart Cities ◽  
Fog Computing ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Computing Paradigm ◽  
Reflective Middleware ◽  
The People

In a Smart City is required computational platforms, which allow environments with multiple interconnected and embedded systems, where the technology is integrated with the people, and can respond to unpredictable situations. One of the biggest challenges in developing Smart City is how to describe and dispose of enormous and multiple sources of information, and how to share and merge it into a single infrastructure. In previous works, we have proposed an Autonomic Reflective Middleware with emerging and ubiquitous capabilities, which is based on intelligent agents that can be adapted to the existing dynamism in a city for, ubiquitously, respond to the requirements of citizens, using emerging ontologies that allow the adaptation to the context. In this work, we extend this middleware using the fog computing paradigm, to solve this problem. The fog extends the cloud to be closer to the things that produce and act on the smart city. In this paper, we present the extension to the middleware, and examples of utilization in different situations in a smart city.

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