GETA: Generalized Encrypted Traffic Analysis

Traditional traffic analysis is being fundamentally challenged by the rapid adoption of encryption, tunnelling, and privacy-preserving protocols, which increasingly obscure packet payloads and limit the usefulness of Deep Packet Inspection DPI. Although machine learning has advanced encrypted...

Unknown Attack Detection in IoT Networks Using Large Language Models: A Robust, Data-Efficient Approach

The rapid evolution of cyberattacks continues to drive the emergence of unknown zero-day threats, posing significant challenges for network intrusion detection systems in Internet of Things IoT networks. Existing machine learning and deep learning approaches typically rely on large labeled...

MeLeMaD: Adaptive Malware Detection Via Chunk-Wise Feature Selection and Meta-Learning

Confronting the substantial challenges of malware detection in cybersecurity necessitates solutions that are both robust and adaptable to the ever-evolving threat environment. The paper introduces Meta Learning Malware Detection MeLeMaD, a novel framework leveraging the adaptability and...

Meta-Learning Based Radio Frequency Fingerprinting for GNSS Spoofing Detection

The rapid development of technology has led to an increase in the number of devices that rely on position, velocity, and time PVT information to perform their functions. As such, the Global Navigation Satellite Systems GNSS have been adopted as one of the most promising solutions to provide PVT...

Finetuning-Activated Backdoors in LLMs

Finetuning openly accessible Large Language Models LLMs has become standard practice for achieving task-specific performance improvements. Until now, finetuning has been regarded as a controlled and secure process in which training on benign datasets led to predictable behaviors. In this paper, w...

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings

The rapid evolution of malware variants requires robust classification methods to enhance cybersecurity. While Large Language Models LLMs offer potential for generating malware descriptions to aid family classification, their utility is limited by semantic embedding overlaps and misalignment with...