论文标题
DDOSDET：使用神经网络检测DDOS攻击的方法
DDoSDet: An approach to Detect DDoS attacks using Neural Networks
论文作者
论文摘要
网络攻击一直是当今世界上最致命的攻击之一。其中之一是DDO（分布式拒绝服务）。这是一个网络攻击，攻击者攻击并使其预期用户暂时或无限期地使网络或机器无法使用，从而中断了连接到网络的主机的服务。为了简单地定义它，这是通过用不必要的请求淹没目标机器来实现的攻击，以试图使系统崩溃并使用户无法使用该网络或机器。在这篇研究论文中，我们介绍了使用神经网络对DDOS攻击的检测，这些神经网络会标记恶意和合法的数据流，从而阻止网络性能降低。我们将建议的系统与现场中的当前模型进行了比较并评估。我们很高兴地注意到，我们的工作是99.7 \％准确的。
Cyber-attacks have been one of the deadliest attacks in today's world. One of them is DDoS (Distributed Denial of Services). It is a cyber-attack in which the attacker attacks and makes a network or a machine unavailable to its intended users temporarily or indefinitely, interrupting services of the host that are connected to a network. To define it in simple terms, It's an attack accomplished by flooding the target machine with unnecessary requests in an attempt to overload and make the systems crash and make the users unable to use that network or a machine. In this research paper, we present the detection of DDoS attacks using neural networks, that would flag malicious and legitimate data flow, preventing network performance degradation. We compared and assessed our suggested system against current models in the field. We are glad to note that our work was 99.7\% accurate.
