4 papers
Preventing Jailbreak Prompts as Malicious Tools for Cybercriminals: A Cyber Defense Perspective
Jean Marie Tshimula, Xavier Ndona, D'Jeff K. Nkashama +4
Jailbreak prompts pose a significant threat in AI and cybersecurity, as they are crafted to bypass ethical safeguards in large language models, potentially enabling misuse by cyber…
Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation
D'Jeff K. Nkashama, Jordan Masakuna Félicien, Arian Soltani +4
Deep learning (DL) has emerged as a crucial tool in network anomaly detection (NAD) for cybersecurity. While DL models for anomaly detection excel at extracting features and learni…
Impact of Inaccurate Contamination Ratio on Robust Unsupervised Anomaly Detection
Jordan F. Masakuna, DJeff Kanda Nkashama, Arian Soltani +3
Training data sets intended for unsupervised anomaly detection, typically presumed to be anomaly-free, often contain anomalies (or contamination), a challenge that significantly un…
Psychological Profiling in Cybersecurity: A Look at LLMs and Psycholinguistic Features
Jean Marie Tshimula, D'Jeff K. Nkashama, Jean Tshibangu Muabila +21
The increasing sophistication of cyber threats necessitates innovative approaches to cybersecurity. In this paper, we explore the potential of psychological profiling techniques, p…