Deep Learning Approach for Intrusion Detection System

Niharika A P
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Abstract

The rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue for the security of our systems and represents one of the biggest challenges for intrusion detection. An intrusion detection system (IDS) is tool that helps to detect intrusions by inspecting the network traffic. A system called an intrusion detection system (IDS) observes network traffic for malicious transactions and sends immediate alerts when it is observed. It is software that checks a network or system for malicious activities or policy violations. Each illegal activity or violation is often recorded and notified to an administrator. IDS monitors a network or system for malicious activity and protects a computer network from unauthorized access from users, including perhaps insiders. The intrusion detector learning task is to build a predictive model capable of distinguishing between ‘malicious connections’ and ‘genuine connections’. Keywords: Cyber security, intrusion detection, malware, machine learning, deep learning, deep neural networks, CNN,
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入侵检测系统的深度学习方法
互联网和通信的快速发展导致传输的数据大量增加。攻击者觊觎这些数据,并不断制造新的攻击手段来窃取或破坏这些数据。这些攻击的增长是我们系统安全的一个问题,也是入侵检测面临的最大挑战之一。入侵检测系统(IDS)是一种通过检测网络流量来帮助检测入侵的工具。被称为入侵检测系统(IDS)的系统会观察网络流量中的恶意交易,并在观察到恶意交易时立即发出警报。它是一种检查网络或系统是否存在恶意活动或违反策略行为的软件。每项非法活动或违规行为通常都会被记录下来并通知管理员。IDS 监控网络或系统的恶意活动,保护计算机网络免受用户(可能包括内部人员)未经授权的访问。入侵探测器的学习任务是建立一个能够区分 "恶意连接 "和 "真实连接 "的预测模型。关键词网络安全 入侵检测 恶意软件 机器学习 深度学习 深度神经网络 CNN
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