A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

Hassija, Vikas; Chamola, Vinay; Saxena, Vikas; Jain, Divyansh; Goyal, Pranav; Sikdar, Biplab

doi:10.1109/access.2019.2924045

Cited by 1,078 publications

(642 citation statements)

References 165 publications

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“…Most of the botnets launched the DDoS attacks to disrupt a service, and to make it unavailable to legitimate users [6]. DDoS attacks can occur in both the network layer and the application layer in the IoT environment [9]. Data theft and privacy issues are the specific security issues in the application layer of the IoT environment [9].…”

Section: Common Attacks In the Iot Environmentmentioning

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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Section: Common Attacks In the Iot Environmentmentioning

confidence: 99%

“…DDoS attacks can occur in both the network layer and the application layer in the IoT environment [9]. Data theft and privacy issues are the specific security issues in the application layer of the IoT environment [9]. The study [10] addressed the challenges of data disclosure and privacy violations to the users.…”

Section: Common Attacks In the Iot Environmentmentioning

confidence: 99%

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

et al. 2020

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“…Let C be the cipher text space consisting of symbols that are different from the one defined in M. The encryption mechanism is a bijective function, denoted by Ee, between M and C, which can be uniquely determined by an element {e ∈ K}, where K is the key space. Likewise, the decryption mechanism that can be defined as a Security in IoT is still a factor of concern and boundless applications of the IoT technology portend a wide range of security challenges [9,10]. Unauthorized access and data manipulation have become common threats in these networks.…”

Section: Security Preliminariesmentioning

confidence: 99%

Proof of Concept of Scalable Integration of Internet of Things and Blockchain in Healthcare

Satamraju

Malarkodi

2020

Sensors

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The advent of Internet of Things (IoT) brought innovation along with unprecedented benefits of convenience and efficacy in many operations that were otherwise very cumbersome. This innovation explosion has surfaced a new dimension of vulnerability and physical threat to the data integrity of IoT networks. Implementing conventional cryptographic algorithms on IoT devices is not future-proof as these devices are constrained in terms of computational power, performance, and memory. In this paper, we are proposing a novel framework, a unique model that integrates IoT networks with a blockchain to address potential privacy and security threats for data integrity. Smart contracts are instrumental in this integration process and they are used to handle device authentication, authorization and access-control, and data management. We further share a new design model for interfaces to integrate both platforms while highlighting its performance results over the existing models. With the incorporation of off-chain data storage into the framework, overall scalability of the system can be increased. Finally, our research concludes how the proposed framework can be fused virtually into any existing IoT applications with minimal modifications.

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“…However, changes are made so that the sum of ( ), = 1,2,3 is always constant and equal to one. The increase or decrease the probability of different conditions in LA with fixed structure as equation (5).…”

Section: -3-1 Reinforcement Signalmentioning

confidence: 99%

“…By increasing the number of selfish nodes, the average end-to-end delay is increased in the data packets and network traffic. It is generally impaired by network functionality and practicality [5][6][7]. To overcome the effects of selfish nodes, identifying and dealing with them is of particular importance.…”

Section: -Introductionmentioning

confidence: 99%

Selfish Node detection based on GA and Learning Automata in IoT

Nobahary¹,

Garakani²,

Khademzadeh³

et al. 2020

Preprint

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It is critical to increasing the network throughput on the internet of things with short-range nodes. Nodes prevent to cooperate with other nodes in the network are known as selfish nodes. Previous studies have done on the selfish nodes detection that leads to increase throughput and reduce the end to end delay. The proposed method for discovering the selfish node is based on genetic algorithm and learning automata. It consists of three phases of setup and clustering, the best routing selection based on genetic algorithm, and finally, the learning and update phase. For appropriate network performance, the clustering algorithm implemented in the first phase. Nodes are working together to send the data packet to the destination in the second phase, and the neighbor node selected for forwarding the data packet in which that node has a high value of fitness function, among others. In the third phase, each node monitors the performance of its neighbor nodes in forwarding the data packet and uses the learning automata system to identify the selfish nodes. By preventing to cooperate selfish nodes and decreasing the probability selection of selfish nodes, it increases the throughput in the network. The results of the simulation show that the detection accuracy of selfish nodes in comparison with the existing methods average 12%, and the false positive rate has decreased by 5%.

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A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

Cited by 1,078 publications

References 165 publications

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

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

Proof of Concept of Scalable Integration of Internet of Things and Blockchain in Healthcare

Selfish Node detection based on GA and Learning Automata in IoT

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