An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks

Kumar, Prabhat; Gupta, Govind P.; Tripathi, Rakesh

doi:10.1016/j.comcom.2020.12.003

Cited by 198 publications

(113 citation statements)

References 37 publications

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“…In addition, it is also evident that the artificial intelligence research community can analyse the data better to understand the attack characteristics and develop more sophisticated countermeasures. In recent times, there have been some novel approaches in securing the distributed and smart systems [39][40][41][42][43][44][45]. We are optimistic that, the ECU-IoFT dataset will bring forth several new directions in UAV and cuber security research.…”

Section: Discussionmentioning

confidence: 99%

ECU-IoFT: A Dataset for Analysing Cyber-Attacks on Internet of Flying Things

et al. 2022

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There has been a significant increase in the adoption of unmanned aerial vehicles (UAV) within science, technology, engineering, and mathematics project-based learning. However, the risks that education providers place their student and staff under is often unknown or undocumented. Low-end consumer drones used within the education sector are vulnerable to state-of-the-art cyberattacks. Therefore, datasets are required to conduct further research to establish cyber defenses for UAVs used within the education sector. This paper showcases the development of the ECU-IoFT dataset, documenting three known cyber-attacks targeting Wi-Fi communications and the lack of security in an affordable off-the-shelf drone. At present, there are no publicly available labeled datasets that reflect cyberattacks on the Internet of Flying Things (IoFT). The majority of the publicly available network traffic datasets are emulated and do not reflect the scenarios/attacks from a real test setup. This dataset will be beneficial for both cybersecurity researchers to develop defense strategies and UAV manufacturers to design more secure products. In the future, endeavors will be taken to incorporate newer attacks and create datasets appropriate for big data analysis.

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Section: Discussionmentioning

confidence: 99%

ECU-IoFT: A Dataset for Analysing Cyber-Attacks on Internet of Flying Things

et al. 2022

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“…Other research proposes using an architecture that contains more than three layers [17]. Different technologies are proposed to manage medical data, such as fog/cloud computing [18], software-defined networking (SDN) [19] or Blockchain [20]. This review paper assumes that a three-layered architecture is suitable for logically divided IoMT architecture.…”

Section: Architecture Of Iomtmentioning

confidence: 99%

Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things

Si-Ahmed¹,

Al-Garadi²,

Boustia³

2022

Preprint

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Internet of Medical Things (IoMT) represents an application of the Internet of Things, where health professionals perform remote analysis of physiological data collected using sensors that are associated with patients, allowing real-time and permanent monitoring of the patient's health condition and the detection of possible diseases at an early stage. However, the use of wireless communication for data transfer exposes this data to cyberattacks, and the sensitive and private nature of this data may represent a prime interest for attackers. The use of traditional security methods on equipment that is limited in terms of storage and computing capacity is ineffective. In this context, we have performed a comprehensive survey to investigate the use of the intrusion detection system based on machine learning (ML) for IoMT security. We presented the generic three-layer architecture of IoMT, the security requirement of IoMT security. We review the various threats that can affect IoMT security and identify the advantage, disadvantages, methods, and datasets used in each solution based on ML. Then we provide some challenges and limitations of applying ML on each layer of IoMT, which can serve as direction for future study.

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“…However, the FPR is higher than existing works. Kumar et al [29] proposed a cyber-attack detection framework for internet of medical things (IoMT) based on fog-cloud architecture and ensemble learning. The ensemble learning includes NB, RF and DT classifiers.…”

Section: Hybrid Feature Selection and Voting Ensemble Classifiermentioning

confidence: 99%

A Novel Intrusion Detection System for RPL Based IoT Networks with Bio-Inspired Feature Selection and Ensemble Classifier

Prakash

2021

Preprint

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Internet of Things (IoT) is the powerful latest trend that allows communications and networking of many sources over the internet. Routing protocol for low power and lossy networks (RPL) based IoT networks may be exposed to many routing attacks due to resource-constrained and open nature of the IoT nodes. Hence, there is a need for network intrusion detection system (NIDS) to protect RPL based IoT networks from routing attacks. The existing techniques for anomaly-based NIDS (ANIDS) subjects to high false alarm rate (FAR). Therefore, a novel bio-inspired voting ensemble classifier with feature selection technique is proposed in this paper to improve the performance of ANIDS for RPL based IoT networks. The proposed voting ensemble classifier combines the results of various base classifiers such as logistic Regression, support vector machine, decision tree, bidirectional long short-term memory and K-nearest neighbor to detect the attacks accurately based on majority voting rule. The optimized weights of base classifiers are obtained by using the feature selection method called simulated annealing based improved salp swarm algorithm (SA-ISSA), which is the hybridization of particle swarm optimization, opposition based learning and salp swarm algorithm. The experiments are performed with RPL-NIDDS17 dataset that contains seven types of attack instances. The performance of the proposed model is evaluated and compared with existing feature selection and classification techniques in terms of accuracy, attack detection rate (ADR), FAR and so on. The proposed ensemble classifier shows better performance with higher accuracy (96.4%), ADR (97.7%) and reduced FAR (3.6%).

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An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks

Cited by 198 publications

References 37 publications

ECU-IoFT: A Dataset for Analysing Cyber-Attacks on Internet of Flying Things

ECU-IoFT: A Dataset for Analysing Cyber-Attacks on Internet of Flying Things

Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things

A Novel Intrusion Detection System for RPL Based IoT Networks with Bio-Inspired Feature Selection and Ensemble Classifier

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