Next Generation Intrusion Detection: Autonomous Reinforcement Learning of Network Attacks

机译：下一代入侵检测：网络攻击的自主强化学习

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The timely and accurate detection of computer and network system intrusions has always been an elusivegoal for system administrators and information security researchers. Existing intrusion detection approachesrequire either manual coding of new attacks in expert systems or the complete retraining of a neural networkto improve analysis or learn new attacks. This paper presents a new approach to applying adaptive neuralnetworks to intrusion detection that is capable of autonomously learning new attacks rapidly through the useof a modified reinforcement learning method that uses feedback from the protected system. The approach hasbeen demonstrated to be extremely effective in learning new attacks, detecting previously learned attacks in anetwork data stream, and in autonomously improving its analysis over time using feedback from the protectedsystem.

机译：及时准确地检测计算机和网络系统的入侵一直以来都是遥不可及的系统管理员和信息安全研究人员的目标。现有的入侵检测方法要求对专家系统中的新攻击进行手动编码或对神经网络进行完全重新训练改善分析或学习新的攻击。本文提出了一种新的方法来应用自适应神经网络到入侵检测，该入侵检测能够通过使用来快速自主学习新攻击一种改进的强化学习方法，该方法使用了受保护系统的反馈。该方法有已被证明在学习新攻击，检测先前学习到的攻击中非常有效网络数据流，并使用受保护者的反馈自动改进其分析能力系统。

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《National information systems security conference》|2000年|p.1-12|共12页
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作者
James Cannady;
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中图分类安全保密;
关键词
Intrusion detection; neural networks; reinforcement learning;

机译：入侵检测;;神经网络;;强化学习;

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专利

1. Multi-agent technologies for computer network security: Attack simulation, intrusion detection and intrusion detection learning [J] . Vladimir Gorodetski, Igor Kotenko, Oleg Karsaev International Journal of Computer Systems Science & Engineering . 2003,第4期

机译：用于计算机网络安全的多代理技术：攻击模拟，入侵检测和入侵检测学习
2. Trust-Evaluation-Based Intrusion Detection and Reinforcement Learning in Autonomous Driving [J] . Xing Rui, Su Zhou, Zhang Ning, IEEE Network . 2019,第5期

机译：自动驾驶中基于信任评估的入侵检测与强化学习
3. DReLAB - Deep REinforcement Learning Adversarial Botnet: A benchmark dataset for adversarial attacks against botnet Intrusion Detection Systems [J] . Andrea Venturi, Giovanni Apruzzese, Mauro Andreolini, Data in Brief . 2021,第3期

机译：DRELAB - 深度加强学习对抗僵尸网络：用于对僵尸网络入侵检测系统进行对抗性攻击的基准数据集
4. Network Intrusion Detection Systems Using Adversarial Reinforcement Learning with Deep Q-network [C] . Ekachai Suwannalai, Chantri Polprasert International Conference on ICT and Knowledge Engineering . 2020

机译：利用深度Q网络使用对抗性增强学习的网络入侵检测系统
5. Identification of Compromised Nodes in Collaborative Intrusion Detection Systems for Large Scale Networks Due to Insider Attacks [D] . Yenugunti, Chandralekha. 2020

机译：由于内幕攻击，大规模网络的协同入侵检测系统中受损的节点识别
6. DReLAB - Deep REinforcement Learning Adversarial Botnet: A benchmark dataset for adversarial attacks against botnet Intrusion Detection Systems [O] . Andrea Venturi, Giovanni Apruzzese, Mauro Andreolini, 2021

机译：DRELAB - 深度加强学习对抗僵尸网络：用于对僵尸网络入侵检测系统进行对抗性攻击的基准数据集
7. Analyzing Adversarial Attacks against Deep Learning for Intrusion Detection in IoT Networks [O] . Olakunle Ibitoye, Omair Shafiq, Ashraf Matrawy 2019

机译：IOT网络中对深度学习的对抗攻击分析

Next Generation Intrusion Detection: Autonomous Reinforcement Learning of Network Attacks

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