A Machine Learning Security Framework for Iot Systems

Bagaa, Miloud; Taleb, Tarik; Bernabé, Jorge Bernal; Skarmeta, Antonio F.

doi:10.1109/access.2020.2996214

Cited by 155 publications

(70 citation statements)

References 46 publications

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“…When a network attack occurs in an SDN, ML can be introduced as a detection technology to dynamically control and route the communication flow. Recently, studies using ML to detect and automatically respond to DDoS attacks, abnormal patterns, and data leaks against IoT networks and devices have increased [60,[189][190][191][192][193][194][195][196][197][198][199].…”

Section: Identification Of Topics In Iot Securitymentioning

confidence: 99%

Current Research Trends in IoT Security: A Systematic Mapping Study

Lee

2021

Mobile Information Systems

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The smart mobile Internet-of-things (IoT) network lays the foundation of the fourth industrial revolution, the era of hyperconnectivity, hyperintelligence, and hyperconvergence. As this revolution gains momentum, the security of smart mobile IoT networks becomes an essential research topic. This study aimed to provide comprehensive insights on IoT security. To this end, we conducted a systematic mapping study of the literature to identify evolving trends in IoT security and determine research subjects. We reviewed the literature from January 2009 to August 2020 to identify influential researchers and trends of keywords. We additionally performed structural topic modeling to identify current research topics and the most promising ones via topic trend estimation. We synthesized and interpreted the results of the systematic mapping study to devise future research directions. The results obtained from this study are useful to understand current trends in IoT security and provide insights into research and development of IoT security.

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Section: Identification Of Topics In Iot Securitymentioning

confidence: 99%

Current Research Trends in IoT Security: A Systematic Mapping Study

Lee

2021

Mobile Information Systems

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“…Testing of various IoT devices such as smart home, wireless sensor networks (WSN), and smart wearables is done to identify the security holes. An integrated AI security framework which exploits machine learning approaches to detect new kind of cyber-attacks in IoT is implemented [38]. Tempo-spatial correlation between different sensor data is analyzed for threat identification.…”

Section: Vulnerability Mitigation Approaches and Security Testbedsmentioning

confidence: 99%

A Hadoop Based Framework Integrating Machine Learning Classifiers for Anomaly Detection in the Internet of Things

et al. 2021

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In recent years, different variants of the botnet are targeting government, private organizations and there is a crucial need to develop a robust framework for securing the IoT (Internet of Things) network. In this paper, a Hadoop based framework is proposed to identify the malicious IoT traffic using a modified Tomek-link under-sampling integrated with automated Hyper-parameter tuning of machine learning classifiers. The novelty of this paper is to utilize a big data platform for benchmark IoT datasets to minimize computational time. The IoT benchmark datasets are loaded in the Hadoop Distributed File System (HDFS) environment. Three machine learning approaches namely naive Bayes (NB), K-nearest neighbor (KNN), and support vector machine (SVM) are used for categorizing IoT traffic. Artificial immune network optimization is deployed during cross-validation to obtain the best classifier parameters. Experimental analysis is performed on the Hadoop platform. The average accuracy of 99% and 90% is obtained for BoT_IoT and ToN_IoT datasets. The accuracy difference in ToN-IoT dataset is due to the huge number of data samples captured at the edge layer and fog layer. However, in BoT-IoT dataset only 5% of the training and test samples from the complete dataset are considered for experimental analysis as released by the dataset developers. The overall accuracy is improved by 19% in comparison with state-of-the-art techniques. The computational times for the huge datasets are reduced by 3–4 hours through Map Reduce in HDFS.

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“…Security and network Orchestration [ 10 , 11 ] leveraging NFV/SDN in next-generation networks and IoT [ 12 , 13 ], is gaining more and more research attraction as key element to achieve truly efficient, reliable and resilient infrastructures. In this sense, novel zero-touch network security management architectures and frameworks [ 6 , 14 ] for IoT are emerging, which aims to optimize the deployment and (re-)configuration of virtualized and softwarized network security appliances, such as vFirewalls, vHoneynets, vAAA, vChannelProtection, vProxies or vIDS (Intrusion Detection Systems).…”

Section: Related Workmentioning

confidence: 99%

Semantic-Aware Security Orchestration in SDN/NFV-Enabled IoT Systems

Zarca

Bagaa

Bernabé

et al. 2020

Sensors

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IoT systems can be leveraged by Network Function Virtualization (NFV) and Software-Defined Networking (SDN) technologies, thereby strengthening their overall flexibility, security and resilience. In this sense, adaptive and policy-based security frameworks for SDN/NFV-aware IoT systems can provide a remarkable added value for self-protection and self-healing, by orchestrating and enforcing dynamically security policies and associated Virtual Network Functions (VNF) or Virtual network Security Functions (VSF) according to the actual context. However, this security orchestration is subject to multiple possible inconsistencies between the policies to enforce, the already enforced management policies and the evolving status of the managed IoT system. In this regard, this paper presents a semantic-aware, zero-touch and policy-driven security orchestration framework for autonomic and conflict-less security orchestration in SDN/NFV-aware IoT scenarios while ensuring optimal allocation and Service Function Chaining (SFC) of VSF. The framework relies on Semantic technologies and considers the security policies and the evolving IoT system model to dynamically and formally detect any semantic conflict during the orchestration. In addition, our optimized SFC algorithm maximizes the QoS, security aspects and resources usage during VSF allocation. The orchestration security framework has been implemented and validated showing its feasibility and performance to detect the conflicts and optimally enforce the VSFs.

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A Machine Learning Security Framework for Iot Systems

Cited by 155 publications

References 46 publications

Current Research Trends in IoT Security: A Systematic Mapping Study

Current Research Trends in IoT Security: A Systematic Mapping Study

A Hadoop Based Framework Integrating Machine Learning Classifiers for Anomaly Detection in the Internet of Things

Semantic-Aware Security Orchestration in SDN/NFV-Enabled IoT Systems

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