Deep learning solutions are instrumental in cybersecurity, harnessing their ability to analyze vast datasets, identify complex patterns, and detect anomalies. However, malevolent actors can exploit these capabilities to orchestrate sophisticated attacks, posing significant challenges to defenders and traditional security measures. Adversarial attacks, particularly those targeting vulnerabilities in deep learning models, present a nuanced and substantial threat to cybersecurity. Our study delves into adversarial learning threats such as Data Poisoning, Test Time Evasion, and Reverse Engineering, specifically impacting Network Intrusion Detection Systems. Our research explores the intricacies and countermeasures of attacks to deepen understanding of network security challenges amidst adversarial threats. In our study, we present insights into the dynamic realm of adversarial learning and its implications for network intrusion. The intersection of adversarial attacks and defenses within network traffic data, coupled with advances in machine learning and deep learning techniques, represents a relatively underexplored domain. Our research lays the groundwork for strengthening defense mechanisms to address the potential breaches in network security and privacy posed by adversarial attacks. Through our in-depth analysis, we identify domain-specific research gaps, such as the scarcity of real-life attack data and the evaluation of AI-based solutions for network traffic. Our focus on these challenges aims to stimulate future research efforts toward the development of resilient network defense strategies.

翻译：深度学习解决方案在网络安全领域发挥着关键作用，其能够分析海量数据集、识别复杂模式并检测异常。然而，恶意行为者可能利用这些能力策划复杂攻击，给防御者和传统安全措施带来重大挑战。对抗性攻击，特别是针对深度学习模型漏洞的攻击，对网络安全构成了微妙而严重的威胁。本研究深入探讨了数据投毒、测试时规避和逆向工程等对抗性学习威胁，这些威胁尤其影响网络入侵检测系统。我们通过剖析攻击的复杂机理与防御对策，深化对对抗威胁环境下网络安全挑战的理解。本研究揭示了对抗学习这一动态领域的核心见解及其对网络入侵的影响。网络流量数据中对抗攻击与防御的交叉结合，加之机器学习与深度学习技术的进步，构成了一个尚未被充分探索的研究领域。我们的研究为强化防御机制奠定了基础，以应对对抗攻击可能导致的网络安全与隐私漏洞。通过深入分析，我们指出了领域特定的研究空白，例如真实攻击数据的匮乏以及基于人工智能的网络流量解决方案的评估。对这些挑战的关注旨在推动未来研究，促进弹性网络防御策略的发展。