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Faeiz M. Alserhani on Muck Rack

Faeiz M. Alserhani

As seen in: Nature, MDPI
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Articles

A robust hybrid deep learning framework for IoT intrusion detection with adaptive feature embedding and imbalance-aware optimization

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By Shuangcen Li, Faeiz M. Alserhani, Abdulrahman Alotaibi, Hemant Petwal
| Nature Verified
Abstract The rapid expansion of Internet of Things (IoT) ecosystems has increased exposure to cyber threats, highlighting the need for robust and generalizable intrusion detection systems (IDS). Existing approaches often suffer from weak feature representation, poor handling of class imbalance, and inconsistent evaluation practices, limiting their real-world applicability.
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Evaluating Ensemble Learning Mechanisms for Predicting Advanced Cyber Attacks

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By Faeiz M. Alserhani, Alaa Aljared
| MDPI
This is an early access version, the complete PDF, HTML, and XML versions will be available soon. Open AccessArticle by 1,* and 2 1 Department of Computer Engineering and Networks, Jouf University, Sakaka 72388, Saudi Arabia 2 Department of Computer Sciences, Jouf University, Sakaka 72388, Saudi Arabia * Author to whom correspondence should be addressed. Appl. Sci.
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Collaborative Federated Learning-Based Model for Alert Correlation and Attack Scenario Recognition

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By Hadeel K. Alkhpor, Faeiz M. Alserhani
| MDPI
1. Introduction An intrusion detection system (IDS) refers to a hardware or software entity that monitors a network to detect and identify unauthorized activities or violations of established policies. In the event that an intrusion is detected, IDS captures pertinent data regarding the event, disseminates alerts, and executes appropriate remedial or preventive measures as deemed required [1].
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