A Framework for Malicious Traffic Detection in IoT Healthcare Environment
Open accessAbstract
The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices’ security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT use cases. The proposed framework consists of a newly created, open-source IoT data generator tool named IoT-Flock. The IoT-Flock tool allows researchers to develop an IoT use-case comprised of both normal and malicious IoT devices and generate traffic. Additionally, the proposed framework provides an open-source utility for converting the captured traffic generated by IoT-Flock into an IoT dataset. Using the proposed framework in this research, we first generated an IoT healthcare dataset which comprises both normal and IoT attack traffic. Afterwards, we applied different machine learning techniques to the generated dataset to detect the cyber-attacks and protect the healthcare system from cyber-attacks. The proposed framework will help in developing the context-aware IoT security solutions, especially for a sensitive use case like IoT healthcare environment.
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With the growth in data traffic, the network's bandwidth is challenged.
“With the growth in data traffic, the network’s bandwidth is once again challenged [207].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [207]
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Cites
Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A. (2018) · Proceedings of the 4th International Conference on Information Systems Security and Privacy · doi:10.5220/0006639801080116 · Cited by 5,318 · cited 1× · see citing works
“However, this dataset does not contain real-time traffic and has some irregularities like the absence of false positives [35].”
BackgroundMoustafa, N., & Slay, J. (2015) · 2015 Military Communications and Information Systems Conference (MilCIS) · doi:10.1109/milcis.2015.7348942 · Cited by 4,033 · see citing works
Koroniotis, N., Moustafa, N., Sitnikova, E., & Turnbull, B. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.05.041 · Cited by 1,983 · see citing works
A survey of intrusion detection in Internet of Things
Zarpelão, B. B., Miani, R. S., Kawakani, C. T., & de Alvarenga, S. C. (2017) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.02.009 · Cited by 1,022 · see citing works
Lallie, H. S., Shepherd, L. A., Nurse, J. R. C., Erola, A., Epiphaniou, G., Maple, C., & Bellekens, X. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102248 · Cited by 671 · see citing works
Healthcare Data Breaches: Insights and Implications
Seh, A. H., Zarour, M., Alenezi, M., Sarkar, A. K., Agrawal, A., Kumar, R., & Ahmad Khan, R. (2020) · Healthcare · doi:10.3390/healthcare8020133 · Cited by 600 · see citing works
Semi-supervised learning based distributed attack detection framework for IoT
Rathore, S., & Park, J. H. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.05.049 · Cited by 355 · see citing works
Habibzadeh, H., Nussbaum, B. H., Anjomshoa, F., Kantarcı, B., & Soyata, T. (2019) · Sustainable Cities and Society · doi:10.1016/j.scs.2019.101660 · Cited by 304 · see citing works
MQTTset, a New Dataset for Machine Learning Techniques on MQTT
Vaccari, I., Chiola, G., Aiello, M., Mongelli, M., & Cambiaso, E. (2020) · Sensors · doi:10.3390/s20226578 · Cited by 292 · see citing works
Pundir, S., Wazid, M., Singh, D. P., Das, A. K., Rodrigues, J. J. P. C., & Park, Y. (2020) · IEEE Access · doi:10.1109/access.2019.2962829 · Cited by 202 · see citing works
IoT DoS and DDoS Attack Detection using ResNet
Hussain, F. B., Abbas, S. G., Husnain, M., Fayyaz, U. U., Shahzad, F., & Shah, G. A. (2020) · IEEE International Multitopic Conference (INMIC) · doi:10.1109/inmic50486.2020.9318216 · Cited by 200 · see citing works
Self-citationPraseed, A., & Thilagam, P. S. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2870658 · Cited by 178 · see citing works
Intrusion detection systems in Internet of Things: A literature review
Santos, L., Rabadao, C. M. D. S., & Gonçalves, R. (2018) · Iberian Conference on Information Systems and Technologies (CISTI) · doi:10.23919/cisti.2018.8399291 · Cited by 117 · see citing works
FBAD: Fog-based Attack Detection for IoT Healthcare in Smart Cities
Alrashdi, I., Alqazzaz, A., Alharthi, R. S., Aloufi, E., Zohdy, M. A., & Ming, H. (2019) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon47517.2019.8992963 · Cited by 73 · see citing works
IoT-Flock: An Open-source Framework for IoT Traffic Generation
Ghazanfar, S., Hussain, F. B., Rehman, A., et al. (2020) · 2020 International Conference on Emerging Trends in Smart Technologies (ICETST) · doi:10.1109/icetst49965.2020.9080732 · Cited by 72 · see citing works
Self-citationDetecting zero-day attacks using context-aware anomaly detection at the application-layer
Düssel, P., Gehl, C., Flegel, U., Dietrich, S., & Meier, M. (2016) · International Journal of Information Security · doi:10.1007/s10207-016-0344-y · Cited by 71 · see citing works
Towards a Universal Features Set for IoT Botnet Attacks Detection
Hussain, F. B., Abbas, S. G., Fayyaz, U. U., Shah, G. A., Toqeer, A., & Ali, A. (2020) · IEEE International Multitopic Conference (INMIC) · doi:10.1109/inmic50486.2020.9318106 · Cited by 63 · see citing works
Self-citationHomogeneous Data Normalization and Deep Learning: A Case Study in Human Activity Classification
Pires, I. M., Hussain, F., García, N. M., Lameski, P., & Zdravevski, E. (2020) · Future Internet · doi:10.3390/fi12110194 · Cited by 62 · see citing works
Self-citationA Local Feature Engineering Strategy to Improve Network Anomaly Detection
Carta, S., Podda, A. S., Recupero, D. R., & Saia, R. (2020) · Future Internet · doi:10.3390/fi12100177 · Cited by 51 · see citing works
An Efficient Machine Learning-based Elderly Fall Detection AlgorithmPreprint
Hussain, F. B., Umair, M. B., Ehatisham-ul-Haq, M., Pires, I. M., Valente, T., García, N. M., & Pombo, N. G. C. C. (2020) · doi:10.21203/rs.3.rs-39065/v1 · Cited by 50 · see citing works
Self-citationRughoobur, P., & Nagowah, L. (2017) · doi:10.1109/ictus.2017.8286118 · Cited by 47 · see citing works
Kumar, A., & Lim, T. J. (2019) · Lecture notes in networks and systems · doi:10.1007/978-3-030-12385-7_58 · Cited by 44 · see citing works
Security Challenges in IoT Cyber World
Patel, C., & Doshi, N. (2018) · Lecture notes in intelligent transportation and infrastructure · doi:10.1007/978-3-030-01560-2_8 · Cited by 42 · see citing works
Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
Pires, I. M., Hussain, F., García, N. M., et al. (2020) · Future Internet · doi:10.3390/fi12090155 · Cited by 26 · see citing works
Self-citationElderly Assistance Using Wearable Sensors by Detecting Fall and Recognizing Fall Patterns
Hussain, F. B., Ehatisham-ul-Haq, M., Azam, M. A., et al. (2018) · doi:10.1145/3267305.3274129 · Cited by 21 · see citing works
Self-citationFeature extraction based on word embedding models for intrusion detection in network traffic
Corizzo, R., Zdravevski, E., Russell, M., Vagliano, A., & Japkowicz, N. (2020) · Journal of Surveillance Security and Safety · doi:10.20517/jsss.2020.15 · Cited by 20 · see citing works
Self-citationAddressing Privacy and Security in Connected Health with Fog Computing
Dimitrievski, A., Zdravevski, E., Lameski, P., & Trajkovik, V. (2019) · EAI International Conference on Smart Objects and Technologies for Social Good (GOODTECHS) · doi:10.1145/3342428.3342654 · Cited by 9 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 3
- [27]KDD Cup 1999 Data Available online: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html (accessed on 4 February 2021)
- [28]NSL-KDD Dataset Available online: https://www.unb.ca/cic/datasets/nsl.html (accessed on 6 February 2021)
- [46]A-Framework-for-Malicious-Traffic-Detection-in-IoT-Healthcare-Environment Available online: https://github.com/ThingzDefense/A-Framework-for-Malicious-Traffic-Detection-in-IoT-Healthcare-Environment (accessed on 16 April 2021) PMC8123414 33925813
Cited by
Sorted by citations
Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399 · see citing works
Blockchain and explainable AI for enhanced decision making in cyber threat detection
Kumar, P., Javeed, D., Kumar, R., & Islam, A. K. M. N. (2024) · Software: Practice and Experience · doi:10.1002/spe.3319 · Cited by 58 · see citing works
Advancing IoMT Defenses: Deep Collaborative Learning for Robust Healthcare Security
Otoum, Y., Singh, P., & Nayak, A. (2024) · GLOBECOM 2024 - 2024 IEEE Global Communications Conference · doi:10.1109/globecom52923.2024.10901705 · Cited by 9 · see citing works
- Authors
- Hussain, F., Abbas, S. G., Shah, G. A., Pires, I. M., Fayyaz, U. U., Shahzad, F., Garcia, N. M., & Zdravevski, E.
- Affiliations
- University of Engineering and Technology Lahore (PK)
- University of Beira Interior (PT)
- Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
- Ss. Cyril and Methodius University in Skopje (MK)
- Corresponding author
- Hussain, Faisal
- Pires, Ivan Miguel
- ORCID
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-04-26
- Volume
- 21
- Issue
- 9
- Pages
- 3025
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC8123414
- OpenAlex
- W3159885303
- Cited by
- 230
- References
- 27
- Citations by year
- 2026: 29 · 2025: 50 · 2024: 72 · 2023: 40 · 2022: 30 · 2021: 9
- FWCI
- 16.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministerio de Economía y Competitividad (PT2020)
- Fundação para a Ciência e a Tecnologia (UIDB/00742/2020, UIDB/50008, PT2020, UIDB/50008/2020, 50008)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- malicious traffic detection, Internet of Things security, Internet of Medical Things, cyberattack detection, context-aware security, resource-constrained devices, machine learning for intrusion detection
- MeSH terms
- Internet of Things, Cities, Confidentiality, Delivery of Health Care, Humans, Computer Security
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