Proactive Cyber Defense

Proactive cyber defense aims to anticipate and prevent cyberattacks before they occur, rather than simply reacting to them. Current research heavily focuses on leveraging machine learning, particularly deep reinforcement learning and large language models, to analyze threat intelligence, predict attack vectors (like mapping vulnerabilities to attack techniques), and automatically generate robust defensive strategies, including obfuscation techniques to evade malware detection. These advancements are crucial for enhancing the resilience of critical infrastructure (like smart grids) and improving overall cybersecurity posture, offering a significant shift from reactive to predictive security measures.

Papers

November 19, 2024

ProSec: Fortifying Code LLMs with Proactive Security Alignment
Xiangzhe Xu, Zian Su, Jinyao Guo, Kaiyuan Zhang, Zhenting Wang, Xiangyu Zhang
Large Language Model Vulnerable Code Alignment Dataset Proactive Cyber Defense

March 1, 2024

Crimson: Empowering Strategic Reasoning in Cybersecurity through Large Language Models
Jiandong Jin, Bowen Tang, Mingxuan Ma, Xiao Liu, Yunfei Wang, Qingnan Lai, Jia Yang, Changling Zhou
Strategy Logic Cybersecurity Perspective Vulnerability Data Strategic Ability Proactive Cyber Defense

February 4, 2024

Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach
Brian Etter, James Lee Hu, Mohammedreza Ebrahimi, Weifeng Li, Xin Li, Hsinchun Chen
Malware Detection Obfuscation Technique Deep Reinforcement Learning Approach Adversarial Malware Based Malware Proactive Cyber Defense

January 11, 2024

The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey
Nima Abdi, Abdullatif Albaseer, Mohamed Abdallah
Deep Learning Timely Survey Integral Role Smart Grid Active Defense Proactive Cyber Defense

February 3, 2023

Deep Reinforcement Learning for Cyber System Defense under Dynamic Adversarial Uncertainties
Ashutosh Dutta, Samrat Chatterjee, Arnab Bhattacharya, Mahantesh Halappanavar
Deep Reinforcement Learning Data Driven Defense Algorithm Cyber Defense Autonomous Cyber Adversarial Uncertainty Proactive Cyber Defense

August 25, 2022

Automatic Mapping of Unstructured Cyber Threat Intelligence: An Experimental Study
Vittorio Orbinato, Mariarosaria Barbaraci, Roberto Natella, Domenico Cotroneo
Experimental Study Attack Method Threat Intelligence Proactive Cyber Defense

Proactive Cyber Defense

Papers

ProSec: Fortifying Code LLMs with Proactive Security Alignment

Crimson: Empowering Strategic Reasoning in Cybersecurity through Large Language Models

Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach

The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey

Deep Reinforcement Learning for Cyber System Defense under Dynamic Adversarial Uncertainties

Automatic Mapping of Unstructured Cyber Threat Intelligence: An Experimental Study