Wireless Network Attack Defense Algorithm Using Deep Neural Network in Internet of Things Environment

Over the past decades, the Internet and information technologies have elevated security issues due to the huge use of networks. Because of this advance information and communication and sharing information, the threats of cybersecurity have been increasing daily. Intrusion Detection System (IDS) is considered one of the most critical security components which detects network security breaches in organizations. However, a lot of challenges raise while implementing dynamics and effective NIDS for unknown and unpredictable attacks. Consider the machine learning approach to developing an effective and flexible IDS. A deep neural network model is proposed to increase the effectiveness of intrusions detection system. This chapter presents an efficient mechanism for network attacks detection and attack classification using the Management Information Base (MIB) variables with machine learning techniques. During the evaluation test, the proposed model seems highly effective with deep neural network implementation with a precision of 99.6% accuracy rate.

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Deep Neural Network Based Matrix Completion for Internet of Things Network Localization

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp40776.2020.9053773 ◽

2020 ◽

Cited By ~ 3

Author(s):

Sunwoo Kim ◽

Luong Trung Nguyen ◽

Byonghyo Shim

Keyword(s):

Neural Network ◽

Internet Of Things ◽

Deep Neural Network ◽

Matrix Completion ◽

Network Localization

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ANALYSIS TECHNOLOGY OF ENVIRONMENTAL MONITORING DATA BASED ON INTERNET OF THINGS ENVIRONMENT AND IMPROVED NEURAL NETWORK ALGORITHM

Applied Ecology and Environmental Research ◽

10.15666/aeer/1706_1450514516 ◽

2019 ◽

Vol 17 (6) ◽

Author(s):

W ZHAI

Keyword(s):

Neural Network ◽

Internet Of Things ◽

Environmental Monitoring ◽

Monitoring Data ◽

Network Algorithm ◽

Internet Of Things Environment ◽

Neural Network Algorithm ◽

Environmental Monitoring Data

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Survey of Machine Learning Algorithms to Detect Malware in Consumer Internet of Things Devices

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213021500202 ◽

2021 ◽

Vol 30 (04) ◽

pp. 2150020

Author(s):

Luke Holbrook ◽

Miltiadis Alamaniotis

Keyword(s):

Neural Network ◽

Machine Learning ◽

Internet Of Things ◽

Deep Neural Network ◽

Learning Algorithms ◽

Cyber Attacks ◽

Machine Learning Algorithms ◽

Coefficient Of Determination ◽

Support Vector ◽

Iot Devices

With the increase of cyber-attacks on millions of Internet of Things (IoT) devices, the poor network security measures on those devices are the main source of the problem. This article aims to study a number of these machine learning algorithms available for their effectiveness in detecting malware in consumer internet of things devices. In particular, the Support Vector Machines (SVM), Random Forest, and Deep Neural Network (DNN) algorithms are utilized for a benchmark with a set of test data and compared as tools in safeguarding the deployment for IoT security. Test results on a set of 4 IoT devices exhibited that all three tested algorithms presented here detect the network anomalies with high accuracy. However, the deep neural network provides the highest coefficient of determination R2, and hence, it is identified as the most precise among the tested algorithms concerning the security of IoT devices based on the data sets we have undertaken.

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