- Sponsor:
- sigsac
It is our great pleasure to welcome you to the inaugural edition of the 1st International Workshop on Autonomous Cybersecurity (AutonomousCyber 2024). As part of the 31st ACM Conference on Computer and Communications Security (ACM CCS 2024), this workshop provides an important platform for researchers, practitioners, and experts in the field of cybersecurity to discuss and explore the transformative potential of autonomous systems in safeguarding digital infrastructure.
Autonomous cybersecurity represents a new frontier in cybersecurity technology, where systems independently detect, respond to, and neutralize threats without human intervention. These advancements mark a significant step forward, with implications for reducing human error, enhancing adaptability, and improving response times. AutonomousCyber 2024 brings together cutting-edge research in areas such as Machine Learning (ML), Reinforcement Learning (RL), and Quantum Machine Learning (QML), integrated with advanced automation techniques for tasks like automated patch management and incident response.
Proceeding Downloads
Autonomous Cybersecurity: Evolving Challenges, Emerging Opportunities, and Future Research Trajectories
Autonomous cybersecurity represents a significant advancement in information security, enabling systems to autonomously detect, respond to, and mitigate cyber threats without human intervention. This position paper comprehensively analyzes the current ...
PenHeal: A Two-Stage LLM Framework for Automated Pentesting and Optimal Remediation
Recent advances in Large Language Models (LLMs) have shown significant potential in enhancing cybersecurity defenses against sophisticated threats. LLM-based penetration testing is an essential step in automating system security evaluations by ...
Automated APT Defense Using Reinforcement Learning and Attack Graph Risk-based Situation Awareness
- Anh Tuan Le,
- Gregory Epiphaniou,
- Carsten Maple,
- Konstantinos G. Kyriakopoulos,
- Lincoln Kiarie,
- Marios Aristodemou,
- Iain Phillips
Advanced Persistent Threats pose significant risks to communication and infrastructure systems. While both heuristic and reinforcement learning have been applied to address this challenge, current approaches rely on fixed assumptions or use simplistic ...
P3GNN: A Privacy-Preserving Provenance Graph-Based Model for Autonomous APT Detection in Software Defined Networking
Software Defined Networking (SDN) has brought significant advancements in network management and programmability. However, this evolution has also heightened vulnerability to Advanced Persistent Threats (APTs), sophisticated and stealthy cyberattacks ...
Auto-CIDS: An Autonomous Intrusion Detection System for Vehicular Networks
Control Area Network (CAN), despite facilitating electronic control unit (ECU) communications, lacks built-in mechanisms for secure transmission, exposing its messages to cyber-attacks due to unsecured broadcasting. Current Intrusion Detection Systems (...
Entity-based Reinforcement Learning for Autonomous Cyber Defence
A significant challenge for autonomous cyber defence is ensuring a defensive agent's ability to generalise across diverse network topologies and configurations. This capability is necessary for agents to remain effective when deployed in dynamically ...
Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection
The rapid evolution of mobile networks from 5G to 6G has necessitated the development of autonomous network management systems, such as Zero-Touch Networks (ZTNs). However, the increased complexity and automation of these networks have also escalated ...
Index Terms
- Proceedings of the Workshop on Autonomous Cybersecurity