Analyzing anonymous activities using Interrupt-aware Anonymous User-System Detection Method (IAU-S-DM) in IoT.

Hani Alshahrani, Mohd Anjum, Sana Shahab, Mana Saleh Al Reshan, Adel Sulaiman, Asadullah Shaikh

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Hani AlshahraniDepartment Computer Science, College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia.ORCID http://orcid.org/0000-0002-8799-9448

Mohd AnjumDepartment of Computer Engineering, Aligarh Muslim University, Aligarh, 202002, India.

Sana ShahabDepartment of Business Administration, College of Business Administration, Princess Nourah Bint Abdulrahman University, PO Box 84428, 11671, Riyadh, Saudi Arabia.

Mana Saleh Al ReshanEmerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia.ORCID http://orcid.org/0000-0002-2266-9608

Adel SulaimanDepartment Computer Science, College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia. aaalsulaiman@nu.edu.sa.ORCID http://orcid.org/0000-0001-8539-7024

Asadullah ShaikhEmerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia.ORCID http://orcid.org/0000-0003-4806-6159

Funding

The authors are thankful to the Deanship of Graduate Studies and Scientific Research at Najran University for funding this work under the Elite Funding Program grant code NU/EP/SERC/13/416

6 · The paper itself

Abstract

The intrusion detection process is important in various applications to identify unauthorized Internet of Things (IoT) network access. IoT devices are accessed by intermediators while transmitting the information, which causes security issues. Several intrusion detection systems are developed to identify intruders and unauthorized access in different software applications. Existing systems consume high computation time, making it difficult to identify intruders accurately. This research issue is mitigated by applying the Interrupt-aware Anonymous User-System Detection Method (IAU-S-DM). The method uses concealed service sessions to identify the anonymous interrupts. During this process, the system is trained with the help of different parameters such as origin, session access demands, and legitimate and illegitimate users of various sessions. These parameters help to recognize the intruder's activities with minimum computation time. In addition, the collected data is processed using the deep recurrent learning approach that identifies service failures and breaches, improving the overall intruder detection rate. The created system uses the TON-IoT dataset information that helps to identify the intruder activities while accessing the different data resources. This method's consistency is verified using the metrics of service failures of 10.65%, detection precision of 14.63%, detection time of 15.54%, and classification ratio of 20.51%.

Indexed as

Adversary classificationDeep recurrent learningInternet of ThingsIntrusion detection systemService failure

Identifiers

PMID39103381

DOI10.1038/s41598-024-67956-0

PMCPMC11300712

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.