151 citations · 195 across the 20 of their papers we have counts for
21 papers
Signal2Symbol: Neuro-Symbolic Temporal Reasoning for Explainable Physiological Time-Series Anomaly Detection
Naser Mansour, Sidahmed Benabderrahmane, Ameer Rahwan
Physiological time series such as electrocardiograms (ECG) and electroencephalograms (EEG) exhibit complex temporal structure, substantial acquisition variability, and a strong nee…
RareSense: Rarity-Aware Similarity Search for Anomaly Retrieval in Transactional Data
Sidahmed Benabderrahmane, Talal Rahwan
Similarity search over sparse set-valued data is often dominated by frequent background attributes because classical measures such as Jaccard, cosine, and Hamming compare objects t…
A Source Domain is All You Need: Source-Only Cross-OS Transfer Learning for APT Anomaly Detection via Semantic Alignment and Optimal Transport
Sidahmed Benabderrahmanea, Petko Valtchev, James Cheney +1
Advanced Persistent Threats (APTs) are stealthy, multi-stage cyberattacks whose detection is difficult due to scarce labeled traces, severe class imbalance, and the challenge of ge…
Interpretable Crisis Behavior Analysis Using Mobility and Social Media Data
Muhammad Hamza Arshad Majeed, Sidahmed Benabderrahmane, Talal Rahwan
Crises alter both how people move and how they communicate. During emergencies such as wildfires and pandemics, changes in mobility patterns and online emotional discourse evolve j…
The Algorithmic Caricature: Auditing LLM-Generated Political Discourse Across Crisis Events
Gunjan, Sidahmed Benabderrahmane, Talal Rahwan
Large Language Models (LLMs) can generate fluent political text at scale, raising concerns about synthetic discourse during crises and social conflict. Existing AI-text detection o…
Real Talk, Virtual Faces: Symbolic-Semantic Discourse Geometry of Virtual and Human Influencer Audiences
Shahram Chaudhry, Sidahmed Benabderrahmane, Talal Rahwan
Virtual influencers~(VIs) -- digitally constructed social-media personas -- are becoming increasingly visible in online culture, marketing, and identity formation. Yet it remains u…
RPG-AE: Neuro-Symbolic Graph Autoencoders with Rare Pattern Mining for Provenance-Based Anomaly Detection
Asif Tauhid, Sidahmed Benabderrahmane, Mohamad Altrabulsi +2
Advanced Persistent Threats (APTs) are sophisticated, long-term cyberattacks that are difficult to detect because they operate stealthily and often blend into normal system behavio…
Refining Decision Boundaries In Anomaly Detection Using Similarity Search Within the Feature Space
Sidahmed Benabderrahmane, Petko Valtchev, James Cheney +1
Detecting rare and diverse anomalies in highly imbalanced datasets-such as Advanced Persistent Threats (APTs) in cybersecurity-remains a fundamental challenge for machine learning…
From One Attack Domain to Another: Contrastive Transfer Learning with Siamese Networks for APT Detection
Sidahmed Benabderrahmane, Talal Rahwan
Advanced Persistent Threats (APT) pose a major cybersecurity challenge due to their stealth, persistence, and adaptability. Traditional machine learning detectors struggle with cla…
Ranking-Enhanced Anomaly Detection Using Active Learning-Assisted Attention Adversarial Dual AutoEncoders
Sidahmed Benabderrahmane, James Cheney, Talal Rahwan
Advanced Persistent Threats (APTs) pose a significant challenge in cybersecurity due to their stealthy and long-term nature. Modern supervised learning methods require extensive la…
Adversarial Augmentation and Active Sampling for Robust Cyber Anomaly Detection
Sidahmed Benabderrahmane, Talal Rahwan
Advanced Persistent Threats (APTs) present a considerable challenge to cybersecurity due to their stealthy, long-duration nature. Traditional supervised learning methods typically…
Attackers Strike Back? Not Anymore -- An Ensemble of RL Defenders Awakens for APT Detection
Sidahmed Benabderrahmane, Talal Rahwan
Advanced Persistent Threats (APTs) represent a growing menace to modern digital infrastructure. Unlike traditional cyberattacks, APTs are stealthy, adaptive, and long-lasting, ofte…
Metric Matters: A Formal Evaluation of Similarity Measures in Active Learning for Cyber Threat Intelligence
Sidahmed Benabderrahmane, Talal Rahwan
Advanced Persistent Threats (APTs) pose a severe challenge to cyber defense due to their stealthy behavior and the extreme class imbalance inherent in detection datasets. To addres…
APT-LLM: Embedding-Based Anomaly Detection of Cyber Advanced Persistent Threats Using Large Language Models
Sidahmed Benabderrahmane, Petko Valtchev, James Cheney +1
Advanced Persistent Threats (APTs) pose a major cybersecurity challenge due to their stealth and ability to mimic normal system behavior, making detection particularly difficult in…
Hack Me If You Can: Aggregating AutoEncoders for Countering Persistent Access Threats Within Highly Imbalanced Data
Sidahmed Benabderrahmane, Ngoc Hoang, Petko Valtchev +2
Advanced Persistent Threats (APTs) are sophisticated, targeted cyberattacks designed to gain unauthorized access to systems and remain undetected for extended periods. To evade det…
Interpretable Machine Learning Models for Predicting the Next Targets of Activist Funds
Minwu Kim, Sidahmed Benabderrahmane, Talal Rahwan
This research presents a predictive model to identify potential targets of activist investment funds--entities that acquire significant corporate stakes to influence strategic and…
Perception, performance, and detectability of conversational artificial intelligence across 32 university courses
Hazem Ibrahim, Fengyuan Liu, Rohail Asim +36
The emergence of large language models has led to the development of powerful tools such as ChatGPT that can produce text indistinguishable from human-generated work. With the incr…
A Rule Mining-Based Advanced Persistent Threats Detection System
Sidahmed Benabderrahmane, Ghita Berrada, James Cheney +1
Advanced persistent threats (APT) are stealthy cyber-attacks that are aimed at stealing valuable information from target organizations and tend to extend in time. Blocking all APTs…
Categorical anomaly detection in heterogeneous data using minimum description length clustering
James Cheney, Xavier Gombau, Ghita Berrada +1
Fast and effective unsupervised anomaly detection algorithms have been proposed for categorical data based on the minimum description length (MDL) principle. However, they can be i…
A baseline for unsupervised advanced persistent threat detection in system-level provenance
Ghita Berrada, Sidahmed Benabderrahmane, James Cheney +4
Advanced persistent threats (APT) are stealthy, sophisticated, and unpredictable cyberattacks that can steal intellectual property, damage critical infrastructure, or cause million…
Satellite Images Analysis with Symbolic Time Series: A Case Study of the Algerian Zone
Dalila Attaf, Djamila Hamdadou, Sidahmed Benabderrahmane +1
Satellite Image Time Series (SITS) are an important source of information for studying land occupation and its evolution. Indeed, the very large volumes of digital data stored, usu…