Description
Book SynopsisOnline Clustering of Known and Emerging Malware Families.- Applying Word Embeddings and Graph Neural Networks for Effective Malware Classification.- A Comparative Analysis of SHAP and LIME in Detecting Malicious URLs.- Comparing Balancing Techniques for Malware Classification.- Multimodal Deception and Lie Detection Using Linguistic and Acoustic Features, Deep Models, and Large Language Models.- Enhancing Dynamic Keystroke Authentication with GAN-Optimized Deep Learning Classifiers.- Selecting Representative Samples from Malware Datasets.- FLChain: Integration of Federated Learning and Blockchain for Building Unified Models for Privacy Preservation.- On the Steganographic Capacity of Selected Learning Models.- An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack.- An Empirical Analysis of Hidden Markov Models with Momentum.- Image-Based Malware Classification Using QR and Aztec Codes.- Keystroke Dynamics for User Identification.- Distinguishing Chatbot from Human.- Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network.- Temporal Analysis of Adversarial Attacks in Federated Learning.- Steganographic Capacity of Transformer Models.- Robustness of Selected Learning Models under Label Flipping Attacks.- Effectiveness of Adversarial Benign and Malware Examples in Evasion and Poisoning Attacks.- Quantum Computing Methods for Malware Detection.- Reducing the Surface for Adversarial Attacks in Malware Detectors.- XAI and Android Malware Models.
