Artificial Neural Networks-Based Intrusion Detection System for Internet of Things Fog Nodes

Pacheco, Jesús; Benítez, Victor H.; Félix-Herrán, Luis C.; Satam, Pratik

doi:10.1109/access.2020.2988055

Cited by 53 publications

(28 citation statements)

References 37 publications

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“…An efficient way to detect network scanning attacks is the data mining of network traffic based on a neural network approach [14][15][16][17]. This approach is based on the creation of a neural network trained with the use of data that include the signs of a network scanning attack.…”

Section: Methodsmentioning

confidence: 99%

Neural Network Model for Detecting Network Scanning Attacks

Panischev¹,

Makridin²,

Katasev³

et al. 2020

International Journal of Engineering Research and Technology

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Section: Methodsmentioning

confidence: 99%

Neural Network Model for Detecting Network Scanning Attacks

Panischev¹,

Makridin²,

Katasev³

et al. 2020

International Journal of Engineering Research and Technology

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“…Pacheco et al [ 13 ] proposed an artificial neural network-based method for implementing an adaptive IDS to detect attacks on fog nodes in IoT applications and ensure the availability of communication, allowing the nodes to continuously deliver the important information to the end users. The proposed method was able to detect the normal behavior of fog nodes and was able to detect anomalies due to different sources, such as misuses, cyber-attacks, with a high detection rate and low false alarms.…”

Section: Related Workmentioning

confidence: 99%

An Aggregated Mutual Information Based Feature Selection with Machine Learning Methods for Enhancing IoT Botnet Attack Detection

Al-Sarem

Saeed

Alkhammash

et al. 2021

Sensors

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Due to the wide availability and usage of connected devices in Internet of Things (IoT) networks, the number of attacks on these networks is continually increasing. A particularly serious and dangerous type of attack in the IoT environment is the botnet attack, where the attackers can control the IoT systems to generate enormous networks of “bot” devices for generating malicious activities. To detect this type of attack, several Intrusion Detection Systems (IDSs) have been proposed for IoT networks based on machine learning and deep learning methods. As the main characteristics of IoT systems include their limited battery power and processor capacity, maximizing the efficiency of intrusion detection systems for IoT networks is still a research challenge. It is important to provide efficient and effective methods that use lower computational time and have high detection rates. This paper proposes an aggregated mutual information-based feature selection approach with machine learning methods to enhance detection of IoT botnet attacks. In this study, the N-BaIoT benchmark dataset was used to detect botnet attack types using real traffic data gathered from nine commercial IoT devices. The dataset includes binary and multi-class classifications. The feature selection method incorporates Mutual Information (MI) technique, Principal Component Analysis (PCA) and ANOVA f-test at finely-granulated detection level to select the relevant features for improving the performance of IoT Botnet classifiers. In the classification step, several ensemble and individual classifiers were used, including Random Forest (RF), XGBoost (XGB), Gaussian Naïve Bayes (GNB), k-Nearest Neighbor (k-NN), Logistic Regression (LR) and Support Vector Machine (SVM). The experimental results showed the efficiency and effectiveness of the proposed approach, which outperformed other techniques using various evaluation metrics.

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“…A literature work has been carried out to examine recent findings in the area of IDS solutions in MEC networks [66][67][68][69][70][71][72][73][74]. A firewall architecture has been designed to protect the edge network from the insider attack [75].…”

Section: Related Workmentioning

confidence: 99%

An edge based hybrid intrusion detection framework for mobile edge computing

Singh

Chatterjee

Satapathy

2021

Complex Intell. Syst.

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The Mobile Edge Computing (MEC) model attracts more users to its services due to its characteristics and rapid delivery approach. This network architecture capability enables users to access the information from the edge of the network. But, the security of this edge network architecture is a big challenge. All the MEC services are available in a shared manner and accessed by users via the Internet. Attacks like the user to root, remote login, Denial of Service (DoS), snooping, port scanning, etc., can be possible in this computing environment due to Internet-based remote service. Intrusion detection is an approach to protect the network by detecting attacks. Existing detection models can detect only the known attacks and the efficiency for monitoring the real-time network traffic is low. The existing intrusion detection solutions cannot identify new unknown attacks. Hence, there is a need of an Edge-based Hybrid Intrusion Detection Framework (EHIDF) that not only detects known attacks but also capable of detecting unknown attacks in real time with low False Alarm Rate (FAR). This paper aims to propose an EHIDF which is mainly considered the Machine Learning (ML) approach for detecting intrusive traffics in the MEC environment. The proposed framework consists of three intrusion detection modules with three different classifiers. The Signature Detection Module (SDM) uses a C4.5 classifier, Anomaly Detection Module (ADM) uses Naive-based classifier, and Hybrid Detection Module (HDM) uses the Meta-AdaboostM1 algorithm. The developed EHIDF can solve the present detection problems by detecting new unknown attacks with low FAR. The implementation results illustrate that EHIDF accuracy is 90.25% and FAR is 1.1%. These results are compared with previous works and found improved performance. The accuracy is improved up to 10.78% and FAR is reduced up to 93%. A game-theoretical approach is also discussed to analyze the security strength of the proposed framework.

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Artificial Neural Networks-Based Intrusion Detection System for Internet of Things Fog Nodes

Cited by 53 publications

References 37 publications

Neural Network Model for Detecting Network Scanning Attacks

Neural Network Model for Detecting Network Scanning Attacks

An Aggregated Mutual Information Based Feature Selection with Machine Learning Methods for Enhancing IoT Botnet Attack Detection

An edge based hybrid intrusion detection framework for mobile edge computing

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