70 citations · 105 across the 12 of their papers we have counts for
12 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…
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…
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…
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…
Characterizing Financial Market Coverage using Artificial Intelligence
Jean Marie Tshimula, D'Jeff K. Nkashama, Patrick Owusu +7
This paper scrutinizes a database of over 4900 YouTube videos to characterize financial market coverage. Financial market coverage generates a large number of videos. Therefore, wa…
Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection Systems
D'Jeff Kanda Nkashama, Arian Soltani, Jean-Charles Verdier +3
Recently, advances in deep learning have been observed in various fields, including computer vision, natural language processing, and cybersecurity. Machine learning (ML) has demon…
A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms
Maxime Alvarez, Jean-Charles Verdier, D'Jeff K. Nkashama +3
Anomaly detection has many applications ranging from bank-fraud detection and cyber-threat detection to equipment maintenance and health monitoring. However, choosing a suitable al…
Imagination-Augmented Deep Learning for Goal Recognition
Thibault Duhamel, Mariane Maynard, Froduald Kabanza
Being able to infer the goal of people we observe, interact with, or read stories about is one of the hallmarks of human intelligence. A prominent idea in current goal-recognition…
A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features
Thibault Duhamel, Mariane Maynard, Froduald Kabanza
The ability to infer the intentions of others, predict their goals, and deduce their plans are critical features for intelligent agents. For a long time, several approaches investi…
Cost-Based Goal Recognition Meets Deep Learning
Mariane Maynard, Thibault Duhamel, Froduald Kabanza
The ability to observe the effects of actions performed by others and to infer their intent, most likely goals, or course of action, is known as a plan or intention recognition cog…
Anytime State-Based Solution Methods for Decision Processes with non-Markovian Rewards
Sylvie Thiebaux, Froduald Kabanza, John Slanley
A popular approach to solving a decision process with non-Markovian rewards (NMRDP) is to exploit a compact representation of the reward function to automatically translate the NMR…
Decision-Theoretic Planning with non-Markovian Rewards
C. Gretton, F. Kabanza, D. Price +2
A decision process in which rewards depend on history rather than merely on the current state is called a decision process with non-Markovian rewards (NMRDP). In decision-theoretic…