Decision tree based user-centric security solution for critical IoT infrastructure

Puthal, Deepak; Wilson, Stanly; Nanda, Ashish; Liu, Ming; Swain, Srinibas; Sahoo, Biswa P. S.; Yelamarthi, Kumar; Pillai, Prashant; El-Sayed, Hesham; Prasad, Mukesh

doi:10.1016/j.compeleceng.2022.107754

Cited by 16 publications

(11 citation statements)

References 15 publications

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“…Using feature selection and principal component analysis, it is possible to create new brand components that explain the most important data. As part of machine-learning-based security modeling, these enhanced signature properties may make it easier to manage large amounts of IoT security data, such as identifying anomalies in IoT network traffic [ 103 ].…”

Section: Resultsmentioning

confidence: 99%

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Mazhar

Talpur

Shloul³

et al. 2023

Brain Sciences

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The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website’s content as a technical resource and reference.

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Section: Resultsmentioning

confidence: 99%

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Mazhar

Talpur

Shloul³

et al. 2023

Brain Sciences

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“…A novel sidechannel attack variant that aims to extract the global AES-CCM key used by Philips to encrypt and authenticate new firmware is one example of such a vulnerability (Ronen et al 2017). Another type of threat involves vulnerability attacks targeting the TCP/IP communication mode, which poses significant risks because TCP/IPfocused security frameworks frequently allow devices to connect and communicate before authentication, leading to ineffective security foundations (Puthal et al 2022), i.e.intruders get huge space to be part of the communication system before authentication happens (Puthal et al 2020). Furthermore, the security issues associated with outsourced chip production pose a significant threat to the semiconductor supply chain.…”

Section: Direct Communicationmentioning

confidence: 99%

Dissecting zero trust: research landscape and its implementation in IoT

Liu,

Tan,

et al. 2024

Cybersecurity

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As a progressive security strategy, the zero trust model has attracted notable attention and importance within the realm of network security, especially in the context of the Internet of Things (IoT). This paper aims to evaluate the current research regarding zero trust and to highlight its practical applications in the IoT sphere through extensive bibliometric analysis. We also delve into the vulnerabilities of IoT and explore the potential role of zero trust security in mitigating these risks via a thorough review of relevant security schemes. Nevertheless, the challenges associated with implementing zero trust security are acknowledged. We provide a summary of these issues and suggest possible pathways for future research aimed at overcoming these challenges. Ultimately, this study aims to serve as a strategic analysis of the zero trust model, intending to empower scholars in the field to pursue deeper and more focused research in the future.

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“…Promising and effective outcomes in solving complicated challenges, for instance, automatic system diagnostics and identification [ 22 ], fault detection in wireless system [ 23 , 24 ], cyber threat detection [ 25 ], and specific security problems in other fields have been generated using ML tools throughout the past decade [ 26 , 27 ]. To detect intrusions, ML approaches can be highly effective.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection in Vehicle Controller Area Network (CAN) Bus Using Machine Learning: A Comparative Performance Study

Bari

Yelamarthi

Ghafoor

2023

Sensors

Self Cite

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Electronic Control Units (ECUs) have been increasingly used in modern vehicles to control the operations of the vehicle, improve driving comfort, and safety. For the operation of the vehicle, these ECUs communicate using a Controller Area Network (CAN) protocol that has many security vulnerabilities. According to the report of Upstream 2022, more than 900 automotive cybersecurity incidents were reported in 2021 only. In addition to developing a more secure CAN protocol, intrusion detection can provide a path to mitigate cyberattacks on the vehicle. This paper proposes a machine learning-based intrusion detection system (IDS) using a Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN) and investigates the effectiveness of the IDS using multiple real-world datasets. The novelty of our developed IDS is that it has been trained and tested on multiple vehicular datasets (Kia Soul and a Chevrolet Spark) to detect and classify intrusion. Our IDS has achieved accuracy up to 99.9% with a high true positive and a low false negative rate. Finally, the comparison of our performance evaluation outcomes demonstrates that the proposed IDS outperforms the existing works in terms of its liability and efficiency to detect cyber-attacks with a minimal error rate.

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Decision tree based user-centric security solution for critical IoT infrastructure

Cited by 16 publications

References 15 publications

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Dissecting zero trust: research landscape and its implementation in IoT

Intrusion Detection in Vehicle Controller Area Network (CAN) Bus Using Machine Learning: A Comparative Performance Study

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