scholarly journals A Machine Learning-Based Lightweight Intrusion Detection System for the Internet of Things

2019 ◽  
Vol 33 (3) ◽  
pp. 203-211 ◽  
Author(s):  
Samir Fenanir ◽  
Fouzi Semchedine ◽  
Abderrahmane Baadache
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
The Internet ◽  
The Internet Of Things

scholarly journals An Intrusion Detection System for the Internet of Things Based on Machine Learning: Review and Challenges

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1011
Author(s):  
Ahmed Adnan ◽  
Abdullah Muhammed ◽  
Abdul Azim Abd Ghani ◽  
Azizol Abdullah ◽  
Fahrul Hakim
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Concept Drift ◽  
The Internet ◽  
Ongoing Research ◽  
Active Research ◽  
The Internet Of Things

An intrusion detection system (IDS) is an active research topic and is regarded as one of the important applications of machine learning. An IDS is a classifier that predicts the class of input records associated with certain types of attacks. In this article, we present a review of IDSs from the perspective of machine learning. We present the three main challenges of an IDS, in general, and of an IDS for the Internet of Things (IoT), in particular, namely concept drift, high dimensionality, and computational complexity. Studies on solving each challenge and the direction of ongoing research are addressed. In addition, in this paper, we dedicate a separate section for presenting datasets of an IDS. In particular, three main datasets, namely KDD99, NSL, and Kyoto, are presented. This article concludes that three elements of concept drift, high-dimensional awareness, and computational awareness that are symmetric in their effect and need to be addressed in the neural network (NN)-based model for an IDS in the IoT.

Intrusion Detection System Through Advance Machine Learning for the Internet of Things Networks

2021 ◽  
Vol 23 (2) ◽  
pp. 58-64
Author(s):  
Tanzila Saba ◽  
Tariq Sadad ◽  
Amjad Rehman ◽  
Zahid Mehmood ◽  
Qaisar Javaid
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
The Internet ◽  
The Internet Of Things

scholarly journals Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1977 ◽  
Author(s):  
Geethapriya Thamilarasu ◽  
Shiven Chawla
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Cyber Attacks ◽  
The Internet ◽  
Critical Systems ◽  
The Internet Of Things

Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively.

scholarly journals Application of Temperature Prediction Based on Neural Network in Intrusion Detection of IoT

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xuefei Liu ◽  
Chao Zhang ◽  
Pingzeng Liu ◽  
Maoling Yan ◽  
Baojia Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Defense Mechanism ◽  
Detection System ◽  
The Internet ◽  
Adaptive Capabilities ◽  
The Neural Network ◽  
The Internet Of Things

The security of network information in the Internet of Things faces enormous challenges. The traditional security defense mechanism is passive and certain loopholes. Intrusion detection can carry out network security monitoring and take corresponding measures actively. The neural network-based intrusion detection technology has specific adaptive capabilities, which can adapt to complex network environments and provide high intrusion detection rate. For the sake of solving the problem that the farmland Internet of Things is very vulnerable to invasion, we use a neural network to construct the farmland Internet of Things intrusion detection system to detect anomalous intrusion. In this study, the temperature of the IoT acquisition system is taken as the research object. It has divided which into different time granularities for feature analysis. We provide the detection standard for the data training detection module by comparing the traditional ARIMA and neural network methods. Its results show that the information on the temperature series is abundant. In addition, the neural network can predict the temperature sequence of varying time granularities better and ensure a small prediction error. It provides the testing standard for the construction of an intrusion detection system of the Internet of Things.

scholarly journals Toward a Lightweight Intrusion Detection System for the Internet of Things

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 42450-42471 ◽  
Author(s):  
Sana Ullah Jan ◽  
Saeed Ahmed ◽  
Vladimir Shakhov ◽  
Insoo Koo
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
The Internet ◽  
The Internet Of Things
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