Security-Aware Routing Protocol Based on Artificial Neural Network Algorithm and 6LoWPAN in the Internet of Things

Lu, Jiangdong; Li, Dongfang; Wang, Penglong; Fen, Zheng; Wang, Meng

doi:10.1155/2022/8374473

Cited by 6 publications

(7 citation statements)

References 40 publications

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“…By choosing the accuracy feature, the sensitivity and precision of the proposed method are 99.39%, 99.31%, and 99.28%, respectively. In the diagram of Figure (7), the time of intrusion detection and training required to identify attacks in seconds is shown in the proposed method with several meta-heuristic algorithms.…”

Section: Analyzementioning

confidence: 99%

“…The task of an intrusion detection system is to discover the abnormality and attack pattern in the network tra c and report the attacks to the rewall [6]. Most intrusion detection systems today are developed with machine learning methods such as arti cial neural networks [7], support vector machines [8], deep learning [9], etc. The advantage of using machine learning methods is that they have the ability to learn and recognize the pattern of attacks and provide good accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Detection of attacks in the Internet of Things with the feature selection approach based on the whale optimization algorithm and learning by majority voting

Aliabadi¹,

Jalalian²

2023

Preprint

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Penetration into the Internet of Things network is a challenge in the security of new-generation networks and smart cities. In most cases, malware is distributed in the Internet of Things and smart objects are infected by malware. Objects infected with malware or viruses, which are called botnets, perform attacks such as DDoS against network services. DDoS attacks make network services inaccessible to users. A suitable approach to detect attacks based on malware and botnet is to use intelligent and distributed intrusion detection systems in the Internet of Things and smart cities. In other research, a centralized architecture and deep learning and machine learning method have been used to design intrusion detection systems. Centralized approaches have limited ability to process large volumes of traffic and are vulnerable to DDoS attacks. In this paper, a distributed intrusion detection system is designed with two stages dimensionality reduction and classification. In the first stage, a new and improved version of the whale optimization algorithm(WOA) has been used to select features and reduce traffic dimensions in fog nodes. In the second stage, each fog node performs the classification of the important features of the network traffic by voting and combined learning. The fog nodes share the IP address of the attacking nodes with the detection of the attacking node. Experiments showed that the improved WOA algorithm has less error in calculating the optimal solution than the optimization algorithm of the WOA algorithm. Reducing the feature selection objective function in the proposed method shows that the WOA algorithm is finding optimal features for intrusion detection and reducing the intrusion detection error. The advantage of the proposed intrusion detection system is to deal with DDoS attacks and cooperation between fog nodes to share blacklists. Tests showed that the proposed method in detecting network intrusion without feature selection has accuracy, sensitivity, and precision of 98.21%, 98.09%, and 97.88%. The proposed method with feature selection has accuracy, sensitivity, and precision of 99.39%, 99.31%, and 99.28%. The accuracy and precision of the proposed method in network intrusion detection are higher than the gray wolf algorithm, genetics and support vector machine, the binary gray wolf algorithm, and the hybridized GWO and GA algorithm. The proposed method is more accurate in intrusion detection than the GWO + PSO and firefly algorithms.

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Section: Analyzementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of attacks in the Internet of Things with the feature selection approach based on the whale optimization algorithm and learning by majority voting

Aliabadi¹,

Jalalian²

2023

Preprint

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“…The information gain value is computed for all mobile nodes with the measured values from Eqs. (11) to (14). The node with higher residual energy and load contains a higher information gain.…”

Section: Gradient Boosted Multinomial Id3 Classification Algorithmmentioning

confidence: 99%

“…The features of MANET pre-empt enormous demands based on security design due to dynamic network topologies, absence of centralized control, and self-organizing character. Collaboration among MANET nodes remains a complex issue in transmitting packets among nodes [11].…”

Section: Introductionmentioning

confidence: 99%

A Novel Gradient Boosted Energy Optimization Model (GBEOM) for MANET

Veeraiah¹,

Alotaibi²,

Alghamdi³

et al. 2023

Computer Systems Science and Engineering

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Mobile Ad Hoc Network (MANET) is an infrastructure-less network that is comprised of a set of nodes that move randomly. In MANET, the overall performance is improved through multipath multicast routing to achieve the quality of service (quality of service). In this, different nodes are involved in the information data collection and transmission to the destination nodes in the network. The different nodes are combined and presented to achieve energy-efficient data transmission and classification of the nodes. The route identification and routing are established based on the data broadcast by the network nodes. In transmitting the data packet, evaluating the data delivery ratio is necessary to achieve optimal data transmission in the network. Furthermore, energy consumption and overhead are considered essential factors for the effective data transmission rate and better data delivery rate. In this paper, a Gradient-Based Energy Optimization model (GBEOM) for the route in MANET is proposed to achieve an improved data delivery rate. Initially, the Weighted Multi-objective Cluster-based Spider Monkey Load Balancing (WMC-SMLB) technique is utilized for obtaining energy efficiency and load balancing routing. The WMC algorithm is applied to perform an efficient node clustering process from the considered mobile nodes in MANET. Load balancing efficiency is improved with a higher data delivery ratio and minimum routing overhead based on the residual energy and bandwidth estimation. Next, the Gradient Boosted Multinomial ID3 Classification algorithm is applied to improve the performance of multipath multicast routing in MANET with minimal energy consumption and higher load balancing efficiency. The proposed GBEOM exhibits ∼4% improved performance in MANET routing.

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“…Deep Learning and Reinforcement Learning [115] Cyber Attacks Detection Machine Learning [117] Security-Aware Deep Learning [118] Early Fire Detection Deep Learning…”

Section: Nb-iotmentioning

confidence: 99%

A Systematic Review of LPWAN and Short-Range Network using AI to Enhance Internet of Things

Widianto

Sinaga

Ginting

2022

Journal of Robotics and Control

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Artificial intelligence (AI) has recently been used frequently, especially concerning the Internet of Things (IoT). However, IoT devices cannot work alone, assisted by Low Power Wide Area Network (LPWAN) for long-distance communication and Short-Range Network for a short distance. However, few reviews about AI can help LPWAN and Short-Range Network. Therefore, the author took the opportunity to do this review. This study aims to review LPWAN and Short-Range Networks AI papers in systematically enhancing IoT performance. Reviews are also used to systematically maximize LPWAN systems and Short-Range networks to enhance IoT quality and discuss results that can be applied to a specific scope. The author utilizes selected reporting items for systematic review and meta-analysis (PRISMA). The authors conducted a systematic review of all study results in support of the authors' objectives. Also, the authors identify development and related study opportunities. The author found 79 suitable papers in this systematic review, so a discussion of the presented papers was carried out. Several technologies are widely used, such as LPWAN in general, with several papers originating from China. Many reports from conferences last year and papers related to this matter were from 2020-2021. The study is expected to inspire experimental studies in finding relevant scientific papers and become another review.

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Security-Aware Routing Protocol Based on Artificial Neural Network Algorithm and 6LoWPAN in the Internet of Things

Cited by 6 publications

References 40 publications

Detection of attacks in the Internet of Things with the feature selection approach based on the whale optimization algorithm and learning by majority voting

Detection of attacks in the Internet of Things with the feature selection approach based on the whale optimization algorithm and learning by majority voting

A Novel Gradient Boosted Energy Optimization Model (GBEOM) for MANET

A Systematic Review of LPWAN and Short-Range Network using AI to Enhance Internet of Things

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