A hybrid approach using support vector machine rule-based system: detecting cyber threats in internet of things

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Ноя. 7, 2024

While the proliferation of Internet Things (IoT) has revolutionized several industries, it also created severe data security concerns. The these network devices and dependability IoT networks depend on efficient threat detection. Device heterogeneity, computing resource constraints, ever-changing nature cyber threats are a few obstacles that make detecting in systems difficult. Complex often go undetected by conventional measures, requiring more sophisticated, adaptive detection methods. Therefore, this study presents Hybrid approach based Support Vector Machines Rule-Based Detection (HSVMR-D) method for an all-encompassing to identifying IoT. HSVMR-D employs SVM categorize known unknown using attributes acquired from data. Identifying attack signatures patterns rule-based approaches improves efficiency without retraining adapting pre-trained models new contexts. Moreover, protecting vital infrastructure sensitive data, provides thorough adaptable solution improve posture deployments. Comprehensive experiment analysis simulation results compared baseline have confirmed proposed HSVMR-D. Furthermore, increased resilience completely novel changing threats, fewer false positives, improved accuracy all outcomes show work outperforms others. is helpful where primary objective secure environment when resources limited.

Язык: Английский

Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Дек. 30, 2024

Язык: Английский

AI-enhanced smart grid framework for intrusion detection and mitigation in EV charging stations

Alexandria Engineering Journal, Год журнала: 2024, Номер unknown

Опубликована: Дек. 1, 2024

Язык: Английский