Citation

Khan, Yawar and Attaullah, Hafiz Muhammad and Alam, Muhammad Mansoor and Mohd Su'ud, Mazliham and Sajid, Ahthasham and Khan, Inam Ullah (2026) Anomaly based IDS for rescue operations using IoT network. In: 3rd International Conference on Applied Data Science and Smart Systems, ADSSS 2024, 13 December 2024 - 14 December 2024, Rajpura, India.

Text Anomaly based IDS for rescue operations using IoT network _ AIP Conference Proceedings _ AIP Publishing.pdf - Published Version Restricted to Repository staff only Download (253kB)

Abstract

The growing use of Internet of Things (IoT) devices in mission-critical activities, such as rescue missions, raises new cybersecurity concerns that may threaten system reliability and data integrity. This research presents an anomaly-based Intrusion Detection System (IDS) for securing IoT networks during rescue operations. By continuously monitoring network traffic, the IDS detects variations from usual patterns that may signal malicious activity. The system is trained and tested using three real-world datasets such as CICIDS17, CICIDS18, and IoTID24, which represent various classical attack scenarios such as DoS, DDoS, and infiltration. Machine learning techniques used include XGBOOST, SGD, and Navie Bayes. The simulation findings show that the Anomaly model outperforms standard models such as Random Forest and Logistic Regression, with higher detection accuracy and fewer false positives. This anomaly-based IDS study has proven to be a useful solution for safeguarding IoT networks in dynamic and resource-constrained contexts, such as emergency rescue operations, by assuring reliable communication and speedy threat detection.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Cybersecurity
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Jun 2026 03:02
Last Modified:	30 Jun 2026 03:02
URII:	http://shdl.mmu.edu.my/id/eprint/16120

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