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Malware represents the most critical threat in cybersecurity, meant to compromise the security for any individual or any organization. These are covert software, designed to perform malicious act like data theft, data al...

Federated learning (FL) is a decentralized machine learning (ML) approach that can be used for intrusion detection in Internet of Things (IoT) devices. It involves the local training of AI models and their aggregation at...

Federated learning (FL) is a pivotal paradigm for financial institutions to collaboratively train models while preserving data privacy, yet standard frameworks are hampered by a triad of challenges: vulnerability to pois...

With the constant advancement of the smart grid, zero-day attack processes in Advanced Metering Infrastructure (AMI) and Supervisory Control and Data Acquisition (SCADA) networks are also continually evolving. This study...

Abstract Unmanned Aerial Systems (UAS) have become increasingly integral to industries such as agriculture, surveillance, transportation, and entertainment, raising significant concerns about their vulnerability to cyber...

Abstract Training deep learning models requires substantial financial and human resources, so once deployed in untrusted environments, these models immediately attract the attention of attackers who seek to steal and mis...

Abstract The rapid adoption of encrypted communication protocols has raised privacy levels but has also dealt a significant blow to traditional intrusion detection systems that rely on payload inspection for anomaly dete...