AN ANALYTICAL APPROACH TO ENHANCE THE INTRUSION DETECTION IN INTERNET OF THINGS NETWORK

Internet of things (IoT) is an emerging concept which aims to connect billions of devices with each other anytime regardless of their location. Sadly, these IoT devices do not have enough computing resources to process huge amount of data. Therefore, Cloud computing is relied on to provide these resources. However, cloud computing based architecture fails in applications that demand very low and predictable latency, therefore the need for fog computing which is a new paradigm that is regarded as an extension of cloud computing to provide services between end users and the cloud user. Unfortunately, Fog-IoT is confronted with various security and privacy risks and prone to several cyberattacks which is a serious challenge. The purpose of this work is to present security and privacy threats towards Fog-IoT platform and discuss the security and privacy requirements in fog computing. We then proceed to propose an Intrusion Detection System (IDS) model using Standard Deep Neural Network's Back Propagation algorithm (BPDNN) to mitigate intrusions that attack Fog-IoT platform. The experimental Dataset for the proposed model is obtained from the Canadian Institute for Cybersecurity 2017 Dataset. Each instance of the attack in the dataset is separated into separate files, which are DoS (Denial of Service), DDoS (Distributed Denial of Service), Web Attack, Brute Force FTP, Brute Force SSH, Heartbleed, Infiltration and Botnet (Bot Network) Attack. The proposed model is trained using a 3-layer BP-DNN

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Intrusion Detection in Internet of Things using Convolutional Neural Networks

10.1109/pst52912.2021.9647828 ◽

2021 ◽

Author(s):

Martin Kodys ◽

Zhi Lu ◽

Kar Wai Fok ◽

Vrizlynn L. L. Thing

Keyword(s):

Neural Networks ◽

Internet Of Things ◽

Intrusion Detection ◽

Convolutional Neural Networks

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An Automata Based Intrusion Detection Method for Internet of Things

Mobile Information Systems ◽

10.1155/2017/1750637 ◽

2017 ◽

Vol 2017 ◽

pp. 1-13 ◽

Cited By ~ 21

Author(s):

Yulong Fu ◽

Zheng Yan ◽

Jin Cao ◽

Ousmane Koné ◽

Xuefei Cao

Keyword(s):

Open Access ◽

Internet Of Things ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection Method ◽

Detection System ◽

Network Communication ◽

5G Networks ◽

Machine To Machine ◽

Security Of Iot

Internet of Things (IoT) transforms network communication to Machine-to-Machine (M2M) basis and provides open access and new services to citizens and companies. It extends the border of Internet and will be developed as one part of the future 5G networks. However, as the resources of IoT’s front devices are constrained, many security mechanisms are hard to be implemented to protect the IoT networks. Intrusion detection system (IDS) is an efficient technique that can be used to detect the attackers when cryptography is broken, and it can be used to enforce the security of IoT networks. In this article, we analyzed the intrusion detection requirements of IoT networks and then proposed a uniform intrusion detection method for the vast heterogeneous IoT networks based on an automata model. The proposed method can detect and report the possible IoT attacks with three types: jam-attack, false-attack, and reply-attack automatically. We also design an experiment to verify the proposed IDS method and examine the attack of RADIUS application.

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