Machine Learning Benchmarking for Secured IoT Smart Systems

Abdalzaher, Mohamed S.; Salim, Mahmoud M.; El-Sayed, Hesham; Fouda, Mostafa M.

doi:10.1109/iotais56727.2022.9975952

Cited by 16 publications

(7 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SS can be involved in a smart garage, individuals' monitoring, the effective interconnection between outdoor lighting, building, lighting, heating, cooling power systems, security surveillance, physical and access systems [40], [57], [58], [59]. Interestingly, long-term evolution (LTE) can also play a significant role in the SS when D2D communication is utilized as extensively studied in [60], [61], [62], and [63].…”

Section: Common Security Methodologiesmentioning

confidence: 99%

Toward Secured IoT-Based Smart Systems Using Machine Learning

et al. 2023

Self Cite

View full text Add to dashboard Cite

Machine learning (ML) and the internet of things (IoT) are among the most booming research directions. Smart cities, smart campuses (SCs), smart homes, smart cars, early warning systems (EWSs), etc.; or it could be called ''Smart x'' systems are implemented using ML and IoT. Those systems will alter how various world entities communicate with one another. This paper spots light on the significant roles of the IoT in SS. Also, it focuses on the importance of ML in IoT-based SS. Besides, an overview of smartness and IoT is presented. Then, this paper offers ML benchmarking along with a taxonomy that categorizes the ML models into linear and non-linear ones depending on the problem type (classification or regression). Afterward, the commonly utilized evaluation metrics are provided. In addition, this paper considers the trust techniques used for mitigating different security aspects in IoT networks, which play a crucial part in regulating the new era of communication. Moreover, two case studies devoting ML for IoT-based SS, namely IoT-based SC and IoT-based EWS, are considered for data collection and manipulation with guided research directions. Finally, the paper presents effective recommendations of ML's significant roles in SC and earthquake EWS for interested scholars.INDEX TERMS Machine learning, Internet of Things, smart systems, security.

show abstract

Section: Common Security Methodologiesmentioning

confidence: 99%

Toward Secured IoT-Based Smart Systems Using Machine Learning

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…Traditional routing protocols within SDN might not fully exploit the dynamic nature of network traffic and changes in network topology. ML algorithms can adaptively learn from network data to optimize routing decisions, leading to improved network performance, reduced latency, and enhanced QoS [147,148].…”

Section: Routing Optimization and Resource Managementmentioning

confidence: 99%

Machine-Learning-Based Traffic Classification in Software-Defined Networks

Serag,

Abdalzaher,

Elsayed

et al. 2024

Electronics

Self Cite

View full text Add to dashboard Cite

Many research efforts have gone into upgrading antiquated communication network infrastructures with better ones to support contemporary services and applications. Smart networks can adapt to new technologies and traffic trends on their own. Software-defined networking (SDN) separates the control plane from the data plane and runs programs in one place, changing network management. New technologies like SDN and machine learning (ML) could improve network performance and QoS. This paper presents a comprehensive research study on integrating SDN with ML to improve network performance and quality-of-service (QoS). The study primarily investigates ML classification methods, highlighting their significance in the context of traffic classification (TC). Additionally, traditional methods are discussed to clarify the ML outperformance observed throughout our investigation, underscoring the superiority of ML algorithms in SDN TC. The study describes how labeled traffic data can be used to train ML models for appropriately classifying SDN TC flows. It examines the pros and downsides of dynamic and adaptive TC using ML algorithms. The research also examines how ML may improve SDN security. It explores using ML for anomaly detection, intrusion detection, and attack mitigation in SDN networks, stressing the proactive threat-detection and response benefits. Finally, we discuss the SDN-ML QoS integration problems and research gaps. Furthermore, scalability and performance issues in large-scale SDN implementations are identified as potential issues and areas for additional research.

show abstract

“…For example, Poslad et al [41] proposed a data-driven IoT system for EEWSs that addressed scalability and data exchange challenges. The system utilized small IoT nodes for efficient data transmission and real-time monitoring [42,43].…”

Section: Related Workmentioning

confidence: 99%

Employing Machine Learning for Seismic Intensity Estimation Using a Single Station for Earthquake Early Warning

Abdalzaher,

Soliman,

Krichen

et al. 2024

Remote Sensing

Self Cite

View full text Add to dashboard Cite

An earthquake early-warning system (EEWS) is an indispensable tool for mitigating loss of life caused by earthquakes. The ability to rapidly assess the severity of an earthquake is crucial for effectively managing earthquake disasters and implementing successful risk-reduction strategies. In this regard, the utilization of an Internet of Things (IoT) network enables the real-time transmission of on-site intensity measurements. This paper introduces a novel approach based on machine-learning (ML) techniques to accurately and promptly determine earthquake intensity by analyzing the seismic activity 2 s after the onset of the p-wave. The proposed model, referred to as 2S1C1S, leverages data from a single station and a single component to evaluate earthquake intensity. The dataset employed in this study, named “INSTANCE,” comprises data from the Italian National Seismic Network (INSN) via hundreds of stations. The model has been trained on a substantial dataset of 50,000 instances, which corresponds to 150,000 seismic windows of 2 s each, encompassing 3C. By effectively capturing key features from the waveform traces, the proposed model provides a reliable estimation of earthquake intensity, achieving an impressive accuracy rate of 99.05% in forecasting based on any single component from the 3C. The 2S1C1S model can be seamlessly integrated into a centralized IoT system, enabling the swift transmission of alerts to the relevant authorities for prompt response and action. Additionally, a comprehensive comparison is conducted between the results obtained from the 2S1C1S method and those derived from the conventional manual solution method, which is considered the benchmark. The experimental results demonstrate that the proposed 2S1C1S model, employing extreme gradient boosting (XGB), surpasses several ML benchmarks in accurately determining earthquake intensity, thus highlighting the effectiveness of this methodology for earthquake early-warning systems (EEWSs).

show abstract

Machine Learning Benchmarking for Secured IoT Smart Systems

Cited by 16 publications

References 99 publications

Toward Secured IoT-Based Smart Systems Using Machine Learning

Toward Secured IoT-Based Smart Systems Using Machine Learning

Machine-Learning-Based Traffic Classification in Software-Defined Networks

Employing Machine Learning for Seismic Intensity Estimation Using a Single Station for Earthquake Early Warning

Contact Info

Product

Resources

About