The limitations in the state-of-the-art counter-measures against the security threats in H-IoT

Qadri, Yazdan Ahmad; Ali, Rashid; Musaddiq, Arslan; Al‐Turjman, Fadi; Kim, Dae Wan; Kim, Sung Won

doi:10.1007/s10586-019-03036-7

Cited by 25 publications

(9 citation statements)

References 62 publications

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“…Existing studies in the network layer have a set of same aspects on the BaaS model to support privacy, authentication, safety, and integrity for all information of the valid transactions in the network layer. In the data layer, all existing studies have discussed the BaaS models for storing and sharing data of blockchain-based applications with high privacy and managed transparency to guarantee ACID 1 with private blockchain technology. In the resource layer of the BaaS model, all existing studies have applied the benefits of cloud-edge resource management and deployment to support the high efficiency and performance of the blockchain ecosystems with relevant services.…”

Section: Statistical Analysis Of Existing Questionsmentioning

confidence: 99%

“…All of the Internet of Things (IoT) applications need to create secure with the easy performance. 1 Reliable systems could form a more simple information process and more desirable for users and organizations. As the safety matters increase the more requests appear for systems that could prepare intense safety and ability to extend and develop, therefore blockchain algorithms appeared for such desires.…”

Section: Introductionmentioning

confidence: 99%

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Blockchain as a service models in the Internet of Things management: Systematic review

Deng

Cai

et al. 2020

Trans Emerging Tel Tech

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Today, blockchain uses a list of various blocks for storing a distributed flat of invariable information that informs of a set of replicated logical things in the Internet of Things (IoT). In the blockchain, a set of blocks includes various transactions containing a cryptographic hash value and a timestamp. Blockchain‐as‐a‐service (BaaS) as a new service in cloud providers presents an infrastructure for accessing users to execute, manage, and monitor blockchain applications without high secured infrastructure requirements. Recently, BaaS concepts widely have used the management of IoT applications in different layers such as network, data, control, and resource. This paper provides a systematic review of recent research studies in BaaS models. The main goal behind this review is to categorize the applied scenarios, trends, evaluated Quality of Service (QoS) factors, new challenges, and open directions on BaaS models in IoT management. To evaluate the existing research studies in this field, five analytical research questions are proposed to analyze the technical aspects of each study. The analytical results based on existing research questions specify that the BaaS models are applied to network layer to manage IoT environment more than other layers. Also, security and privacy are two important factors to evaluate the existing BaaS models in cloud‐edge IoT environments. Finally, the integration of BaaS models on IoT environments with interconnections of cloud‐edge computing and software defined networks creates great secure opportunities for smart environment applications to monitor, manage, and improve all the atomic services and resources.

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Section: Statistical Analysis Of Existing Questionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blockchain as a service models in the Internet of Things management: Systematic review

Deng

Cai

et al. 2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…The main applications of unsupervised ML techniques include anomaly detection and data processing to reduce the number of features in the dataset [15]. Similarly, the effect of ML for securing the data and network information is very profound [27,28].…”

Section: Machine Learning For Iot-based Systemsmentioning

confidence: 99%

Reinforcement Learning-Enabled Cross-Layer Optimization for Low-Power and Lossy Networks under Heterogeneous Traffic Patterns

Musaddiq

Nain

Qadri

et al. 2020

Sensors

Self Cite

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The next generation of the Internet of Things (IoT) networks is expected to handle a massive scale of sensor deployment with radically heterogeneous traffic applications, which leads to a congested network, calling for new mechanisms to improve network efficiency. Existing protocols are based on simple heuristics mechanisms, whereas the probability of collision is still one of the significant challenges of future IoT networks. The medium access control layer of IEEE 802.15.4 uses a distributed coordination function to determine the efficiency of accessing wireless channels in IoT networks. Similarly, the network layer uses a ranking mechanism to route the packets. The objective of this study was to intelligently utilize the cooperation of multiple communication layers in an IoT network. Recently, Q-learning (QL), a machine learning algorithm, has emerged to solve learning problems in energy and computational-constrained sensor devices. Therefore, we present a QL-based intelligent collision probability inference algorithm to optimize the performance of sensor nodes by utilizing channel collision probability and network layer ranking states with the help of an accumulated reward function. The simulation results showed that the proposed scheme achieved a higher packet reception ratio, produces significantly lower control overheads, and consumed less energy compared to current state-of-the-art mechanisms.

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“…In [13], the authors discuss the limitations in the state-ofthe-art counter-measures against the security threats in health IoT. They identify internal attacks, where cryptographic keying material can be compromised, as serious threats to the IoT system especially due to the difficulty in detecting such compromised nodes.…”

Section: Related Workmentioning

confidence: 99%

Attack and Defense Strategies for Intrusion Detection in Autonomous Distributed IoT Systems

et al. 2020

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In this paper, we develop a methodology to capture and analyze the interplay of attack-defense strategies for intrusion detection in an autonomous distributed Internet of Things (IoT) system. In our formulation, every node must participate in lightweight intrusion detection of a neighbor target node. Consequently, every good node would play a set of defense strategies to faithfully defend the system while every bad node would play a set of attack strategies for achieving their own goals. We develop an analytical model based on Stochastic Petri Net (SPN) modeling techniques. Our methodology allows the optimal defense strategies to be played by good nodes to maximize the system lifetime when given a set of parameter values characterizing the distributed IoT system operational environment. We conduct a detailed performance evaluation based on an experiment dataset deriving from a reference autonomous distributed IoT system comprising 128 sensor-carrying mobile nodes and show how IDS defense mechanisms can counter malicious attack mechanisms under the ADIoTS system while considering multiple failure conditions.

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The limitations in the state-of-the-art counter-measures against the security threats in H-IoT

Cited by 25 publications

References 62 publications

Blockchain as a service models in the Internet of Things management: Systematic review

Blockchain as a service models in the Internet of Things management: Systematic review

Reinforcement Learning-Enabled Cross-Layer Optimization for Low-Power and Lossy Networks under Heterogeneous Traffic Patterns

Attack and Defense Strategies for Intrusion Detection in Autonomous Distributed IoT Systems

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