A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing

Khater, Belal Sudqi; Wahab, Ainuddin Wahid Abdul; Idris, Mohd Yamani Idna; Hussain, Mohammed; Ibrahim, Ashraf Ahmed

doi:10.3390/app9010178

Cited by 97 publications

(41 citation statements)

References 36 publications

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“…Dual features were applied to a 2-tier model for classifying the attacks using KNN and NB classifiers [27]. Based on space representation using vectors, a lightweight Intrusion Detection System was suggested by Khater et al [28]. The suggested model used a single hidden layer of MLP and a small number of nodes to evaluate two different datasets containing information of intrusion attacks and they belong to different domains on different applications of a particular region.…”

Section: Related Workmentioning

confidence: 99%

Intrusion Detection Based on Autoencoder and Isolation Forest in Fog Computing

2020

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Fog Computing has emerged as an extension to cloud computing by providing an efficient infrastructure to support IoT. Fog computing acting as a mediator provides local processing of the endusers' requests and reduced delays in communication between the end-users and the cloud via fog devices. Therefore, the authenticity of incoming network traffic on the fog devices is of immense importance. These devices are vulnerable to malicious attacks. All kinds of information, especially financial and health information travel through these devices. Attackers target these devices by sending malicious data packets. It is imperative to detect these intrusions to provide secure and reliable service to the user. So, an effective Intrusion Detection System (IDS) is essential for the secure functioning of fog without compromising efficiency. In this paper, we propose a method (Auto-IF) for intrusion detection based on deep learning approach using Autoencoder (AE) and Isolation Forest (IF) for the fog environment. This approach targets only binary classification of the incoming packets as fog devices are more concerned about differentiating attack from normal packets in real-time. We validate the proposed method on the benchmark NSL-KDD dataset. Our technique of intrusion detection achieves a high accuracy rate of 95.4% as compared to many other state-of-art intrusion detection methods.

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Section: Related Workmentioning

confidence: 99%

Intrusion Detection Based on Autoencoder and Isolation Forest in Fog Computing

2020

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“…The study [19] proposed the intrusion detection system for fog computing, and their proposal was based on the multilayer perceptron (MLP). Their experiments with two datasets [20,21] achieved the 94% accuracy with ADFA-LD and 74% accuracy with ADFA-WD.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture

Soe

Feng

Santosa

et al. 2020

Sensors

144

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With the rapid development and popularization of Internet of Things (IoT) devices, an increasing number of cyber-attacks are targeting such devices. It was said that most of the attacks in IoT environments are botnet-based attacks. Many security weaknesses still exist on the IoT devices because most of them have not enough memory and computational resource for robust security mechanisms. Moreover, many existing rule-based detection systems can be circumvented by attackers. In this study, we proposed a machine learning (ML)-based botnet attack detection framework with sequential detection architecture. An efficient feature selection approach is adopted to implement a lightweight detection system with a high performance. The overall detection performance achieves around 99% for the botnet attack detection using three different ML algorithms, including artificial neural network (ANN), J48 decision tree, and Naïve Bayes. The experiment result indicates that the proposed architecture can effectively detect botnet-based attacks, and also can be extended with corresponding sub-engines for new kinds of attacks.

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“…The IDS studies [5,[19][20][21][22][23][24][25][26][27] also have proposed attack detection based on Raspberry Pi. In the work of [18], the anomaly-based detection method was proposed by capturing the previous traffic patterns asbenign data for attack detection system construction.…”

Section: Raspberry Pi-based Idsmentioning

confidence: 99%

“…How to implement a lightweight and efficient detection system in the IoT environment has become a crucially important issue. The IDS on fog computing [24] was introduced using new modern datasets, namely Australian Defence Force Academy Linux Dataset (ADFA-LD) and ADFA-Windows Dataset (ADFA-WD) [3,25]. This work [24] also used Raspberry Pi to evaluate the performance of attack detecting model.…”

Section: Raspberry Pi-based Idsmentioning

confidence: 99%

“…The IDS on fog computing [24] was introduced using new modern datasets, namely Australian Defence Force Academy Linux Dataset (ADFA-LD) and ADFA-Windows Dataset (ADFA-WD) [3,25]. This work [24] also used Raspberry Pi to evaluate the performance of attack detecting model. They examined its performance by setting up two workstation computers and the Raspberry Pi devices.…”

Section: Raspberry Pi-based Idsmentioning

confidence: 99%

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Towards a Lightweight Detection System for Cyber Attacks in the IoT Environment Using Corresponding Features

et al. 2020

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The application of a large number of Internet of Things (IoT) devices makes our life more convenient and industries more efficient. However, it also makes cyber-attacks much easier to occur because so many IoT devices are deployed and most of them do not have enough resources (i.e., computation and storage capacity) to carry out ordinary intrusion detection systems (IDSs). In this study, a lightweight machine learning-based IDS using a new feature selection algorithm is designed and implemented on Raspberry Pi, and its performance is verified using a public dataset collected from an IoT environment. To make the system lightweight, we propose a new algorithm for feature selection, called the correlated-set thresholding on gain-ratio (CST-GR) algorithm, to select really necessary features. Because the feature selection is conducted on three specific kinds of cyber-attacks, the number of selected features can be significantly reduced, which makes the classifiers very small and fast. Thus, our detection system is lightweight enough to be implemented and carried out in a Raspberry Pi system. More importantly, as the really necessary features corresponding to each kind of attack are exploited, good detection performance can be expected. The performance of our proposal is examined in detail with different machine learning algorithms, in order to learn which of them is the best option for our system. The experiment results indicate that the new feature selection algorithm can select only very few features for each kind of attack. Thus, the detection system is lightweight enough to be implemented in the Raspberry Pi environment with almost no sacrifice on detection performance.

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A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing

Cited by 97 publications

References 36 publications

Intrusion Detection Based on Autoencoder and Isolation Forest in Fog Computing

Intrusion Detection Based on Autoencoder and Isolation Forest in Fog Computing

Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture

Towards a Lightweight Detection System for Cyber Attacks in the IoT Environment Using Corresponding Features

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