FedTDP: a Privacy-Preserving and Unified Framework for Trajectory Data Preparation Via Federated Learning

Trajectory data, which capture the movement patterns of people and vehicles over time and space, are crucial for applications like traffic optimization and urban planning. However, issues such as noise and incompleteness often compromise data quality, leading to inaccurate trajectory analyses and...

QUIC-Exfil: Exploiting QUIC'S Server Preferred Address Feature to Perform Data Exfiltration Attacks

The QUIC protocol is now widely adopted by major tech companies and accounts for a significant fraction of today's Internet traffic. QUIC's multiplexing capabilities, encrypted headers, dynamic IP address changes, and encrypted parameter negotiations make the protocol not only more efficient,...

Rogue Cell: Adversarial Attack and Defense in Untrusted O-RAN Setup Exploiting the Traffic Steering XApp

The Open Radio Access Network O-RAN architecture is revolutionizing cellular networks with its open, multi-vendor design and AI-driven management, aiming to enhance flexibility and reduce costs. Although it has many advantages, O-RAN is not threat-free. While previous studies have mainly examined...

Development of an Adapter for Analyzing and Protecting Machine Learning Models from Competitive Activity in the Networks Services

Due to the increasing number of tasks that are solved on remote servers, identifying and classifying traffic is an important task to reduce the load on the server. There are various methods for classifying traffic. This paper discusses machine learning models for solving this problem. However, su...

AI-Driven IRM: Transforming Insider Risk Management with Adaptive Scoring and LLM-Based Threat Detection

Insider threats pose a significant challenge to organizational security, often evading traditional rule-based detection systems due to their subtlety and contextual nature. This paper presents an AI-powered Insider Risk Management IRM system that integrates behavioral analytics, dynamic risk...

FFCBA: Feature-Based Full-Target Clean-Label Backdoor Attacks

Backdoor attacks pose a significant threat to deep neural networks, as backdoored models would misclassify poisoned samples with specific triggers into target classes while maintaining normal performance on clean samples. Among these, multi-target backdoor attacks can simultaneously target multip...

Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security Applications

This work empirically evaluates machine learning models on two imbalanced public datasets KDDCUP99 and Credit Card Fraud 2013. The method includes data preparation, model training, and evaluation, using an 80/20 train/test split. Models tested include eXtreme Gradient Boosting XGB, Multi Layer...

Quantum Autoencoder for Multivariate Time Series Anomaly Detection

Anomaly Detection AD defines the task of identifying observations or events that deviate from typical - or normal - patterns, a critical capability in IT security for recognizing incidents such as system misconfigurations, malware infections, or cyberattacks. In enterprise environments like SAP...

Contrastive Learning for Continuous Touch-Based Authentication

Smart mobile devices have become indispensable in modern daily life, where sensitive information is frequently processed, stored, and transmitted-posing critical demands for robust security controls. Given that touchscreens are the primary medium for human-device interaction, continuous user...

CSI2Dig: Recovering Digit Content from Smartphone Loudspeakers Using Channel State Information

Eavesdropping on sounds emitted by mobile device loudspeakers can capture sensitive digital information, such as SMS verification codes, credit card numbers, and withdrawal passwords, which poses significant security risks. Existing schemes either require expensive specialized equipment, rely on...

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders

The rapid growth in web-based services has significantly increased security risks related to user information, as web-based attacks become increasingly sophisticated and prevalent. Traditional security methods frequently struggle to detect previously unknown zero-day web attacks, putting sensitiv...

How to Enhance Downstream Adversarial Robustness (Almost) without Touching the Pre-Trained Foundation Model?

With the rise of powerful foundation models, a pre-training-fine-tuning paradigm becomes increasingly popular these days: A foundation model is pre-trained using a huge amount of data from various sources, and then the downstream users only need to fine-tune and adapt it to specific downstream...

Are Deepfakes coming to a scam near you?

Your boss contacts you over Skype. You see her face and hear her voice, asking you to transfer a considerable amount of money to a firm you've never ever heard of. Would you ask for written confirmation of her orders? Or would you simply follow through on her instructions? I would certainly be...