论文标题
随着发达深度学习
BotNet Intrusion Detection System in Internet of Things with Developed Deep Learning
论文作者
论文摘要
技术的快速增长导致了计算网络的创建。随着传感器的扩展和开发以及使用一系列设备连接到Internet，物联网的应用变得越来越明显。当然，任何网络的增长也将带来一些挑战。像其他任何网络一样，物联网的主要挑战是其安全性。在安全领域，存在诸如攻击检测，身份验证，加密等问题。最重要的攻击之一是破坏网络使用情况的网络攻击。对物联网的最重要攻击之一是僵尸网络攻击。该主题的最重要挑战包括非常高的计算复杂性，与以前的方法缺乏比较，缺乏可扩展性，高执行时间，缺乏对拟议方法的评论，以检测和分类攻击和入侵。使用IOT的入侵检测系统是识别和检测各种攻击的重要步骤。因此，可以解决这些挑战的算法提供了一种近乎最佳的方法。使用基于培训的模型和算法，例如深深的珍惜 - 强化学习和组合中的XGBoost学习（DRL-XGBOOST）模型可能是克服以前的弱点的有趣方法。这项研究的数据是BOT-IOT-2018。
The rapid growth of technology has led to the creation of computing networks. The applications of the Internet of Things are becoming more and more visible with the expansion and development of sensors and the use of a series of equipment to connect to the Internet. Of course, the growth of any network will also provide some challenges. The main challenge of IoT like any other network is its security. In the field of security, there are issues such as attack detection, authentication, encryption and the so on. One of the most important attack is cyber-attacks that disrupt the network usage. One of the most important attacks on the IoT is BotNet attack. The most important challenges of this topic include very high computational complexity, lack of comparison with previous methods, lack of scalability, high execution time, lack of review of the proposed approach in terms of accuracy to detect and classify attacks and intrusions. Using intrusion detection systems for the IoT is an important step in identifying and detecting various attacks. Therefore, an algorithm that can solve these challenges has provided a near-optimal method. Using training-based models and algorithms such as Deep Dearning-Reinforcement Learning and XGBoost learning in combination (DRL-XGBoost) models can be an interesting approach to overcoming previous weaknesses. The data of this research is Bot-IoT-2018.
