Intrusion detection and prevention in fog based IoT environments: A systematic literature review

Souza, Cristiano Antonio de; Westphall, Carlos Becker; Machado, Renato Bobsin; Loffi, Leandro; Westphall, Carla Merkle; Gerônimo, Guilherme Arthur

doi:10.1016/j.comnet.2022.109154

Cited by 29 publications

(17 citation statements)

References 119 publications

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“…The adoption of ML and DL approaches in EC oriented applications were presented. The advancement of intrusions and its scope under fog computing with IoT were studied by [14]. Different solutions to the intrusions for IoT architecture was studied by [15].…”

Section: Related Surveysmentioning

confidence: 99%

Intelligent Intrusion Detection Framework for Multi-Clouds – Iot Environment Using Swarm-Based Deep Learning Classifier

Nizamudeen¹

2023

Preprint

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In current era, a tremendous volume of data has been generated by the use of web technologies. The association between different devices and services have also been explored to wisely and widely use recent technologies. Due to the restriction in the available resources, the chance of security violation is increasing highly on the constrained devices. IoT backend with the multi-cloud infrastructure to extend the public services in terms of better scalability and reliability. Several users might access the multi-cloud resources that lead to data threats while handling user requests for IoT services. It poses a new challenge in proposing new functional elements and security schemes. In this paper, an intelligent Intrusion Detection Framework (IDF) is introduced to detect network and application-based attacks. The proposed framework has three phases: data pre-processing, feature selection and classification. Initially, the collected datasets are pre-processed using Integer- Grading Normalization (I-GN) technique that ensures a fair-scaled data transformation process. Secondly, Opposition-based Learning- Rat Inspired Optimizer (OBL-RIO) is designed for the feature selection phase. The progressive nature of rats chooses the significant features. The fittest value ensures the stability of the features from OBL-RIO. Finally, a 2D-Array-based Convolutional Neural Network (2D-ACNN) is proposed as the binary class classifier. The input features are preserved in a 2D-array model to perform on the convoluted set of layers. It detects the normal (or) abnormal traffic. The proposed framework is trained and tested on the Netflow-based datasets. The proposed framework yields 95.20% accuracy, 2.5% false positive rate and 97.24% detection rate.

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Section: Related Surveysmentioning

confidence: 99%

Intelligent Intrusion Detection Framework for Multi-Clouds – Iot Environment Using Swarm-Based Deep Learning Classifier

Nizamudeen¹

2023

Preprint

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“…The effectiveness of these systems is measured by the speed with which threats are detected and the speed with which they are isolated [36]. An other important parameter is the false positive.…”

Section: Intrusion Detection Systems (Ids)mentioning

confidence: 99%

“…According to [36], CISCO predicts that the number of interconnected objects could reach 500 billion by 2025.…”

Section: Introductionmentioning

confidence: 99%

Mitigation of smart black hole attacks using universal sink detection method in graph theory

MEKKAOUI

TEGGAR

2023

Preprint

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Nowadays, network security has become a very important aspect due to the increasing number of connected things and the multiple threats that become more and more intelligent. Mobile Ad hoc networks (MANET), known to be non-infrastructure and self-configured peer networks, are subject to multiple types of attacks. For this reason, it is essential to implement an Intrusion Detection System that realizes fast attack detection to alert users by any malicious activity taking place on the network. Black hole is one of the most serious threats in MANETs, witch is the origin of Denial of service attack. This type of threats has been widely studied and many solutions were proposed. Unfortunately these solutions has become inefficient against the new generation of black holes, known also as smart black holes, witch can deceive most of these solutions. To overcome smart black holes, we proposed an Intrusion Detection System based on the early detecting and isolating malicious nodes by exploiting local information shared by neighbors and using universal sink detection method in graph theory. We proved that smart black holes can defeat the sequence number threshold-based detection strategy by using leastsquare method. Simulations in NS2, showed the efficiency of the proposed approach, which can quickly detect and isolate smart black holes, improve the Packet delivery ratio (PDR) and throughput by an average of 97% and 90%, respectively, thus preserving the network performances.

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“…In addition, the data related to the aforementioned layer fail to suffice for an accurate and comprehensive training due to its access to only its own local data. Therefore, more complex detection models such as machine, Determine type of attack deep, and group learning can be implemented althougth the processing power and available data fail to suffice to apply a comprehensive model [1].…”

Section: Fig 1 Proposed Fc Architecturementioning

confidence: 99%

A Two-layer Intrusion Detection System based on Fog and Cloud using Improved KNN and MPNN

Kaffash,

Kamel,

Kheirabadi

2023

Preprint

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The concept of Internet of Things (IoT) and its countless applications are considered as an inseparable part of modern technology era. The placement of IoT- based devices and their limitations make the environment more vulnerable due to its openness. Security plays a critical role in IoT applications due to pervasiveness of the IoT in all of the aspects in daily life. from the other hand final devices such as their limited computing power, large number of devices connected to each other, and communication between devices and users do not allow for using traditional methods to solve security issues. Intrusion detection systems (IDSs) which can separate malicious traffic from normal mode are among the effective solutions in this field. the installed IDS should be highly accurate and light weight to affect accuracy. In order to bring services closer to electronic devices, a concept called "fog" has emerged. A large number of studies have been conducted to make the light IDS for IoT network utilizing various methods. The present study aims to propose two-layer hierarchical IDS based on machine learning, which detects attacks by considering the limitations of IoT resources. In order to create an efficient and accurate IDS, the combination of two improved K-nearest neighbor (KNN) algorithms and multi-layer perceptron (MLP) neural network applied in the fog and cloud to separate the attacks from normal traffic, respectively. we evaluated our proposed method using IOT23 dataset. The results prove the improvement in accuracy, compared to the previous methods.

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Intrusion detection and prevention in fog based IoT environments: A systematic literature review

Cited by 29 publications

References 119 publications

Intelligent Intrusion Detection Framework for Multi-Clouds – Iot Environment Using Swarm-Based Deep Learning Classifier

Intelligent Intrusion Detection Framework for Multi-Clouds – Iot Environment Using Swarm-Based Deep Learning Classifier

Mitigation of smart black hole attacks using universal sink detection method in graph theory

A Two-layer Intrusion Detection System based on Fog and Cloud using Improved KNN and MPNN

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