Ensemble Learning Techniques for Advanced Threat Detection in Complex Data Environments for Smart Education DOI

Virender Dhiman

Advances in educational technologies and instructional design book series, Год журнала: 2024, Номер unknown, С. 191 - 206

Опубликована: Дек. 20, 2024

As educational environments increasingly leverage digital technologies, they become more susceptible to a myriad of cyber threats. This chapter explores the application ensemble learning techniques for advanced threat detection in complex data environments, particularly within smart education frameworks. Ensemble learning, which combines multiple machine models enhance predictive performance, provides robust solution identifying and mitigating threats real-time. By analyzing diverse datasets from various illustrates how these can improve accuracy efficiency systems. Furthermore, it discusses integration methods with emerging technologies such as IoT, big analytics, AI create comprehensive security framework tailored education. Case studies demonstrating successful implementations highlight effectiveness adapting evolving landscape.

Язык: Английский

Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost Approach DOI Creative Commons
Thuan Minh Nguyen, Hanh Hong-Phuc Vo, Myungsik Yoo

и другие.

Sensors, Год журнала: 2024, Номер 24(11), С. 3339 - 3339

Опубликована: Май 23, 2024

Intrusion detection systems (IDSs) in wireless sensor networks (WSNs) rely heavily on effective feature selection (FS) for enhanced efficacy. This study proposes a novel approach called Genetic Sacrificial Whale Optimization (GSWO) to address the limitations of conventional methods. GSWO combines genetic algorithm (GA) and whale optimization algorithms (WOA) modified by applying new three-population division strategy with proposed conditional inherited choice (CIC) overcome premature convergence WOA. The achieves balance between exploration exploitation enhances global search abilities. Additionally, CatBoost model is employed classification, effectively handling categorical data complex patterns. A technique fine-tuning CatBoost’s hyperparameters introduced, using quantization strategy. Extensive experimentation various datasets demonstrates superiority GSWO-CatBoost, achieving higher accuracy rates WSN-DS, WSNBFSF, NSL-KDD, CICIDS2017 than existing approaches. comprehensive evaluations highlight real-time applicability method across diverse sources, including specialized WSN established benchmarks. Specifically, our GSWO-CatBoost has an inference time nearly 100 times faster deep learning methods while high 99.65%, 99.99%, 99.76%, 99.74% CICIDS2017, respectively.

Язык: Английский

Процитировано

6
Toward Virtualized Optical-Wireless Heterogeneous Networks DOI Creative Commons
Zoran Vujičić, María C. Santos,

Rodrigo Méndez

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 87776 - 87806

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

3
Enhancing Sound-Based Anomaly Detection Using Deep Denoising Autoencoder DOI Creative Commons
Seong‐Mok Kim,

Yong Soo Kim

IEEE Access, Год журнала: 2024, Номер 12, С. 84323 - 84332

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

2
Variance‐driven security optimisation in industrial IoT sensors DOI Creative Commons
Hardik Gupta, Sunil K. Singh, Sudhakar Kumar

и другие.

IET Networks, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 16, 2024

Abstract The Industrial Internet of Things (IIoT) has transformed industrial operations with real‐time monitoring and control, enhancing efficiency productivity. However, this connectivity brings significant security challenges. This study addresses these challenges by identifying abnormal sensor data patterns using machine learning‐based anomaly detection models. proposed framework employs advanced algorithms to strengthen defences against cyber threats disruptions. Focusing on temperature anomalies, a critical yet often overlooked aspect security, research fills gap in the literature evaluating learning models for purpose. A novel variance‐based model is introduced, demonstrating high efficacy accuracy scores 0.92 0.82 NAB AnoML‐IOT datasets, respectively. Additionally, achieved F1 0.96 0.89 underscoring its effectiveness IIoT optimising cybersecurity processes. not only identifies vulnerabilities but also presents concrete solutions improve posture systems.

Язык: Английский

Процитировано

2
Contextual Anomaly Detection in Smart Homes Using Temporal Graph Based Distances DOI
Amirhosein Bodaghi, Chris Nugent

Lecture notes in networks and systems, Год журнала: 2024, Номер unknown, С. 118 - 128

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0
Ensemble Learning Techniques for Advanced Threat Detection in Complex Data Environments for Smart Education DOI

Virender Dhiman

Advances in educational technologies and instructional design book series, Год журнала: 2024, Номер unknown, С. 191 - 206

Опубликована: Дек. 20, 2024

As educational environments increasingly leverage digital technologies, they become more susceptible to a myriad of cyber threats. This chapter explores the application ensemble learning techniques for advanced threat detection in complex data environments, particularly within smart education frameworks. Ensemble learning, which combines multiple machine models enhance predictive performance, provides robust solution identifying and mitigating threats real-time. By analyzing diverse datasets from various illustrates how these can improve accuracy efficiency systems. Furthermore, it discusses integration methods with emerging technologies such as IoT, big analytics, AI create comprehensive security framework tailored education. Case studies demonstrating successful implementations highlight effectiveness adapting evolving landscape.

Язык: Английский

Процитировано

0