Machine learning and the Internet of Things security: Solutions and open challenges

Tariq, Noshina; Asim, Muhammad; Baker, Thar; Al-Shamma’a, Ahmed

doi:10.1016/j.jpdc.2022.01.015

Cited by 39 publications

(25 citation statements)

References 132 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This section details the importance of business architecture in the context of IoT-based systems and examines its essential elements and factors. In IoT and ML deployments, enterprise architecture frameworks give security and privacy a top priority (Farooq et al (2022), Moqurrab et al (2021)). It provides strong security measures, data governance regulations, and compliance standards to safeguard private data and reduce dangers related to connected devices and ML algorithms (Moqurrab et al (2022b)).…”

Section: Enterprise Architecture In the Context Of Iot And MLmentioning

confidence: 99%

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

Rehman,

Tariq,

Moqurrab

et al. 2023

Expert Systems

Self Cite

View full text Add to dashboard Cite

SummaryThe rapid growth of the Internet of Things (IoT) has led to its widespread adoption in various industries, enabling enhanced productivity and efficient services. Integrating IoT systems with existing enterprise application systems has become common practice. However, this integration necessitates reevaluating and reworking current Enterprise Architecture (EA) models and Expert Systems (ES) to accommodate IoT and cloud technologies. Enterprises must adopt a multifaceted view and automate various aspects, including operations, data management, and technology infrastructure. Machine Learning (ML) is a powerful IoT and smart automation tool within EA. Despite its potential, a need for dedicated work focuses on ML applications for IoT services and systems. With IoT being a significant field, analyzing IoT‐generated data and IoT‐based networks is crucial. Many studies have explored how ML can solve specific IoT‐related challenges. These mutually reinforcing technologies allow IoT applications to leverage sensor data for ML model improvement, leading to enhanced IoT operations and practices. Furthermore, ML techniques empower IoT systems with knowledge and enable suspicious activity detection in smart systems and objects. This survey paper conducts a comprehensive study on the role of ML in IoT applications, particularly in the domains of automation and security. It provides an in‐depth analysis of the state‐of‐the‐art ML approaches within the context of IoT, highlighting their contributions, challenges, and potential applications.

show abstract

Section: Enterprise Architecture In the Context Of Iot And MLmentioning

confidence: 99%

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

Rehman,

Tariq,

Moqurrab

et al. 2023

Expert Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…These devices gather data from clinical units and transmit it to the cloud for assessment. Numerous weak points may be exploited to compromise IoMTs, such as wireless connection assaults [4]. Furthermore, deploying new technologies on IoMT increases the danger of exposure to new hazards, which is essential, especially in the case of life-threatening diseases, such as COVID-19.…”

Section: Introductionmentioning

confidence: 99%

BFT-IoMT: A Blockchain-Based Trust Mechanism to Mitigate Sybil Attack Using Fuzzy Logic in the Internet of Medical Things

Ali

Tariq

Khan

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Numerous sensitive applications, such as healthcare and medical services, need reliable transmission as a prerequisite for the success of the new age of communications technology. Unfortunately, these systems are highly vulnerable to attacks like Sybil, where many false nodes are created and spread with deceitful intentions. Therefore, these false nodes must be instantly identified and isolated from the network due to security concerns and the sensitivity of data utilized in healthcare applications. Especially for life-threatening diseases like COVID-19, it is crucial to have devices connected to the Internet of Medical Things (IoMT) that can be believed to respond with high reliability and accuracy. Thus, trust-based security offers a safe environment for IoMT applications. This study proposes a blockchain-based fuzzy trust management framework (BFT-IoMT) to detect and isolate Sybil nodes in IoMT networks. The results demonstrate that the proposed BFT-IoMT framework is 25.43% and 12.64%, 12.54% and 6.65%, 37.85% and 19.08%, 17.40% and 8.72%, and 13.04% and 5.05% more efficient and effective in terms of energy consumption, attack detection, trust computation reliability, packet delivery ratio, and throughput, respectively, as compared to the other state-of-the-art frameworks available in the literature.

show abstract

“…Recently, the development of the blockchain (BC) technique has mentioned a path worth attempting to resolve the distributed trust issue in the IoT platform [11]. BC is a peer-to-peer distributed system with decentralization, non-tempera, system autonomy, and transparency [12], which could successfully improve network collaboration and device security.…”

Section: Introductionmentioning

confidence: 99%

Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for Clustered IoT Driven Ubiquitous Computing System

Salama¹,

Ragab²

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

In the Internet of Things (IoT) based system, the multi-level client's requirements can be fulfilled by incorporating communication technologies with distributed homogeneous networks called ubiquitous computing systems (UCS). The UCS necessitates heterogeneity, management level, and data transmission for distributed users. Simultaneously, security remains a major issue in the IoT-driven UCS. Besides, energy-limited IoT devices need an effective clustering strategy for optimal energy utilization. The recent developments of explainable artificial intelligence (XAI) concepts can be employed to effectively design intrusion detection systems (IDS) for accomplishing security in UCS. In this view, this study designs a novel Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for IoT Driven Ubiquitous Computing System (BXAI-IDCUCS) model. The major intention of the BXAI-IDCUCS model is to accomplish energy efficacy and security in the IoT environment. The BXAI-IDCUCS model initially clusters the IoT nodes using an energy-aware duck swarm optimization (EADSO) algorithm to accomplish this. Besides, deep neural network (DNN) is employed for detecting and classifying intrusions in the IoT network. Lastly, blockchain technology is exploited for secure inter-cluster data transmission processes. To ensure the productive performance of the BXAI-IDCUCS model, a comprehensive experimentation study is applied, and the outcomes are assessed under different aspects. The comparison study emphasized the superiority of the BXAI-IDCUCS model over the current state-of-the-art approaches with a packet delivery ratio of 99.29%, a packet loss rate of 0.71%, a throughput of 92.95 Mbps, energy consumption of 0.0891 mJ, a lifetime of 3529 rounds, and accuracy of 99.38%.

show abstract

Machine learning and the Internet of Things security: Solutions and open challenges

Cited by 39 publications

References 132 publications

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

BFT-IoMT: A Blockchain-Based Trust Mechanism to Mitigate Sybil Attack Using Fuzzy Logic in the Internet of Medical Things

Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for Clustered IoT Driven Ubiquitous Computing System

Contact Info

Product

Resources

About