DDoS attacks in Industrial IoT: A survey

Chaudhary, Shubhankar; Mishra, Pramod Kumar

doi:10.1016/j.comnet.2023.110015

Cited by 16 publications

(3 citation statements)

References 96 publications

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“…The potential applications of IoT encompass a wide and diverse range of fields, and its impact is manifested in various industries, such as tourism [6], home healthcare [7], agriculture [8,9], and finance [10], among others. As the number of devices connected to the global network continues to grow, it has raised new concerns regarding security, stemming from the vulnerabilities presented by IoT devices, such as authentication, access control, device security, and heterogeneity, among others [2,[11][12][13]. These vulnerabilities require defense strategies against potential attacks.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Deep Learning Models for Classifying Cybersecurity Attacks in IoT Networks

Becerra-Suarez,

Tuesta-Monteza,

Mejia-Cabrera

et al. 2024

Informatics

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The Internet of Things (IoT) presents great potential in various fields such as home automation, healthcare, and industry, among others, but its infrastructure, the use of open source code, and lack of software updates make it vulnerable to cyberattacks that can compromise access to data and services, thus making it an attractive target for hackers. The complexity of cyberattacks has increased, posing a greater threat to public and private organizations. This study evaluated the performance of deep learning models for classifying cybersecurity attacks in IoT networks, using the CICIoT2023 dataset. Three architectures based on DNN, LSTM, and CNN were compared, highlighting their differences in layers and activation functions. The results show that the CNN architecture outperformed the others in accuracy and computational efficiency, with an accuracy rate of 99.10% for multiclass classification and 99.40% for binary classification. The importance of data standardization and proper hyperparameter selection is emphasized. These results demonstrate that the CNN-based model emerges as a promising option for detecting cyber threats in IoT environments, supporting the relevance of deep learning in IoT network security.

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Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Deep Learning Models for Classifying Cybersecurity Attacks in IoT Networks

Becerra-Suarez,

Tuesta-Monteza,

Mejia-Cabrera

et al. 2024

Informatics

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“…Moreover, IoT security issues likewise hinger the adoption of the IIoT. Since IoT devices are usually poorly secured, they are easy targets for malware to utilize in destructive cyberattacks, such as distributed denial of service (DDoS) [13,14] or sabotage attacks. Indeed, traditional industrial environments have been attacked in the past, sometimes with devastating consequences.…”

Section: Introductionmentioning

confidence: 99%

Security Issues on Industrial Internet of Things: Overview and Challenges

Wang,

Sun,

Sun

et al. 2023

Computers

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The Industrial Internet of Things (IIoT), where numerous smart devices associated with sensors, actuators, computers, and people communicate with shared networks, has gained advantages in many fields, such as smart manufacturing, intelligent transportation, and smart grids. However, security is becoming increasingly challenging due to the vulnerability of the IIoT to various malicious attacks. In this paper, the security issues of the IIoT are reviewed from the following three aspects: (1) security threats and their attack mechanisms are presented to illustrate the vulnerability of the IIoT; (2) the intrusion detection methods are listed from the attack identification perspectives; and (3) some defense strategies are comprehensively summarized. Several concluding remarks and promising future directions are provided at the end of this paper.

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“…Also, fuzzy-based approaches can be widely used in WSN to address various issues such as security, data fusion, and routing, and now current research interest tends toward clustering. Sensor nodes are primary in all emerging fields, whether IoT [25][26][27][28][29] digital image processing, cloud computing, 30 or artificial intelligence. Everywhere we need a sensor node to sense the data or surrounding and send the data to the server.…”

mentioning

confidence: 99%

Fuzzy based multi‐criteria based cluster head selection for enhancing network lifetime and efficient energy consumption

Srivastava,

Mishra

2023

Concurrency and Computation

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SummarySensors plays an important role in day‐to‐day life as they sense and transfer data on the cloud and servers. These sensors have limited battery power due to which their optimized use is preferred with efficient energy consumption. As sensors were deployed almost everywhere under‐earth, underwater, electronic devices, and mobiles thus their enhanced performance is really important. In WSN, clustering is considered to be an essential approach that gives various advantages such as efficient energy, stability period, network lifetime, less delay, and scalability but has an issue of hot‐spot or energy‐hole problems. For this Un‐Equal Clustering is proposed where the size of clusters varies directly to the distance of the B.S. (Base Station). The main objective of Un‐Equal clustering is energy consumption, hot‐spot problem, and load balance among the cluster heads. In this paper, we have proposed an improved fuzzy‐based multi‐attributes un‐equal clustering to overcome the problem of hot‐spot problem. Earlier, researchers considered two attributes for selecting cluster heads (CHs), but later some of the MADM approaches were used that considered multiple attributes for optimal cluster head selection. In this paper, we have applied the fuzzy‐based TOPSIS method for optimized Un‐Equal clustering where the optimal node deployments with load balance among sensor nodes having optimal coverage and connectivity is considered. The results of the proposed method validate that it is one of the effective ways for selecting optimal cluster heads using fuzzy‐based multi‐attributes. Fuzzy‐based TOPSIS for cluster heads selection method shows 37% enhanced network performance with other compared existing algorithms. The performance of the proposed method is evaluated in terms of network stability, packet delivery, energy consumption, and network lifetime and F‐MAUC (Fuzzy based multi‐attributes un‐equal clustering) outperforms all other compared algorithms.

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DDoS attacks in Industrial IoT: A survey

Cited by 16 publications

References 96 publications

Performance Evaluation of Deep Learning Models for Classifying Cybersecurity Attacks in IoT Networks

Performance Evaluation of Deep Learning Models for Classifying Cybersecurity Attacks in IoT Networks

Security Issues on Industrial Internet of Things: Overview and Challenges

Fuzzy based multi‐criteria based cluster head selection for enhancing network lifetime and efficient energy consumption

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