15 citations · 88 across the 61 of their papers we have counts for
22 papers · 1 filter
Towards Hierarchical Cyber Defense with Large Language Models: From Planning to Execution
Harshith Doppalapudi, Nathaniel D. Bastian, Ankit Shah
An autonomous cyber defender trained with reinforcement learning (RL) is typically tied to the network on which it was trained, limiting its ability to generalize as network scale…
No One Architecture Fits All: A Cross-Environment Evaluation of Hierarchical Red Team Agents
Ayan Javeed Shaikh, Arunesh Sinha, Nathaniel D. Bastian +1
Autonomous red team agents increasingly stress-test AI-enabled cyber defenses by planning strategy and executing multistage attacks. Reinforcement learning (RL) and large language…
Privacy Foundations for Multi-Institutional Scientific Artificial Intelligence
Olivera Kotevska, Sumit Jha, Aur'elien Bellet +6
Scientific artificial intelligence (AI), spanning foundation models (FMs) to federated data-analysis pipelines, is becoming shared infrastructure across national laboratories, univ…
Trident : How to Break Deep Reinforcement Learning Cyber Defenses (Agentic)
Ryozo Masukawa, Ian Bryant, Armita Kazeminajafabadi +6
Autonomous cyber defense systems based on Deep Reinforcement Learning (DRL) have attracted significant research attention, yet remain evaluated almost exclusively against static, h…
Synthetic Network Packet Generation through Statistical Learning and Genetic Algorithms
Mayank Raj, Nathaniel D. Bastian, Lance Fiondella +1
Developing robust intrusion detection systems (IDS) for IoT environments requires large, labeled datasets capturing realistic traffic distributions across both benign and malicious…
Categorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systems
Mayank Raj, Nathaniel D. Bastian, Lance Fiondella +1
Network Intrusion Detection Systems (NIDS) heavily utlize Machine Learning (ML) but ML models can be manipulated via adversarial attacks. These attacks add carefully crafted pertur…
A Red Teaming Framework for Evaluating Robustness of AI-enabled Security Orchestration, Automation, and Response Systems
Ayan Javeed Shaikh, Nathaniel D. Bastian, Ankit Shah
AI-enabled Security Orchestration, Automation, and Response (SOAR) systems increasingly employ autonomous agents for cyber defense, yet their resilience to adaptive adversaries is…
An Automated, Scalable Machine Learning Model Inversion Assessment Pipeline
Tyler Shumaker, Jessica Carpenter, David Saranchak +1
Machine learning (ML) models have the potential to transform military battlefields, presenting a large external pressure to rapidly incorporate them into operational settings. Howe…
Adapting Under Fire: Multi-Agent Reinforcement Learning for Adversarial Drift in Network Security
Emilia Rivas, Sabrina Saika, Ahtesham Bakht +3
Evolving attacks are a critical challenge for the long-term success of Network Intrusion Detection Systems (NIDS). The rise of these changing patterns has exposed the limitations o…
PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning
Ryozo Masukawa, Sanggeon Yun, Sungheon Jeong +5
Traffic classification is vital for cybersecurity, yet encrypted traffic poses significant challenges. We present PacketCLIP, a multi-modal framework combining packet data with nat…
XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language Model
Yasir Ali Farrukh, Syed Wali, Irfan Khan +1
In the rapidly evolving field of cybersecurity, the integration of flow-level and packet-level information for real-time intrusion detection remains a largely untapped area of rese…
A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI
Alice Bizzarri, Chung-En Yu, Brian Jalaian +2
The prevailing approaches in Network Intrusion Detection Systems (NIDS) are often hampered by issues such as high resource consumption, significant computational demands, and poor…
TrojFM: Resource-efficient Backdoor Attacks against Very Large Foundation Models
Yuzhou. Nie, Yanting. Wang, Jinyuan. Jia +4
One key challenge in backdoor attacks against large foundation models is the resource limits. Backdoor attacks usually require retraining the target model, which is impractical for…
Real-time Network Intrusion Detection via Decision Transformers
Jingdi Chen, Hanhan Zhou, Yongsheng Mei +3
Many cybersecurity problems that require real-time decision-making based on temporal observations can be abstracted as a sequence modeling problem, e.g., network intrusion detectio…
RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture
Jingdi Chen, Lei Zhang, Joseph Riem +3
Deep Learning (DL) based methods have shown great promise in network intrusion detection by identifying malicious network traffic behavior patterns with high accuracy, but their ap…
Detecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection
Yasir Ali Farrukh, Syed Wali, Irfan Khan +1
The widespread integration of Internet of Things (IoT) devices across all facets of life has ushered in an era of interconnectedness, creating new avenues for cybersecurity challen…
Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation
Soumyadeep Hore, Jalal Ghadermazi, Diwas Paudel +3
Recent advancements in artificial intelligence (AI) and machine learning (ML) algorithms, coupled with the availability of faster computing infrastructure, have enhanced the securi…
Novelty Detection in Network Traffic: Using Survival Analysis for Feature Identification
Taylor Bradley, Elie Alhajjar, Nathaniel Bastian
Intrusion Detection Systems are an important component of many organizations' cyber defense and resiliency strategies. However, one downside of these systems is their reliance on k…
A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System
Zong-Zhi Lin, Thomas D. Pike, Mark M. Bailey +1
Network intrusion detection systems (NIDS) to detect malicious attacks continue to meet challenges. NIDS are often developed offline while they face auto-generated port scan infilt…
Cybersecurity Anomaly Detection in Adversarial Environments
David A. Bierbrauer, Alexander Chang, Will Kritzer +1
The proliferation of interconnected battlefield information-sharing devices, known as the Internet of Battlefield Things (IoBT), introduced several security challenges. Inherent to…
Algorithm Selection Framework for Cyber Attack Detection
Marc Chalé, Nathaniel D. Bastian, Jeffery Weir
The number of cyber threats against both wired and wireless computer systems and other components of the Internet of Things continues to increase annually. In this work, an algorit…
Adversarial Machine Learning in Network Intrusion Detection Systems
Elie Alhajjar, Paul Maxwell, Nathaniel D. Bastian
Adversarial examples are inputs to a machine learning system intentionally crafted by an attacker to fool the model into producing an incorrect output. These examples have achieved…