Deep Reinforcement Learning for Advanced Persistent Threat Detection in Wireless Networks

Recent cyberattacks have shifted their focus from causing financial loss or service disruption to covertly exfil-trating confidential data. Advanced Persistent Threats (APTs) pose a significant challenge due to their dynamic and sophis-ticated attack mechanisms. Unlike other cyberattacks, APTs are coordinated and targeted, executed by high-profile hackers who exploit identified vulnerabilities and deliver novel malware through phishing attacks to infiltrate networks. Traditional deep learning approaches for APT detection are static and lack adaptability, making them unsuitable for handling the dynamic and evolving attack scenarios commonly found in uncertain network traffic flows, such as multi-stage APT attacks. To address these challenges, this study proposes a Deep Reinforcement Learning approach for APT detection, referred to as APT-DRL. This approach dynamically learns from interactions with the environment, continuously adapting to emerging attack patterns. Performance evaluations demonstrate that APT-DRL effectively learns from dynamic network interactions, enabling it to formulate new policies for APT detection. Consequently, APT-DRL learns faster and achieves better accuracy compared to Feed Forward Neural Network (FNN) models, which lack the adaptability and learning capabilities of the proposed APT-DRL approach.